A long-term and short-term coordinated water-wind-solar complementary integrated scheduling method and system

Abstract

The present invention relates to the intersection of renewable energy utilization and reservoir scheduling, and specifically to a long-term and short-term coordinated water-wind-solar integrated scheduling method and system, including obtaining solar radiation, air temperature, and wind speed data to obtain wind power and photovoltaic output; constructing a medium- and long-term optimal scheduling model for water-wind-solar complementarity, using an implicit random optimization method to perform data analysis on medium- and long-term deterministic optimal scheduling samples, and extracting medium- and long-term scheduling rules; determining a daily power generation plan compilation model by constructing a short-term optimal scheduling model for water-wind-solar complementarity; solving the medium- and long-term optimization model to obtain daily average hydropower output and daily discharge as short-term inputs, obtaining a daily power generation plan through daily power generation plan compilation rules, inferring hydropower output through the daily power generation plan and wind power and photovoltaic output, and then considering reservoir characteristic constraints and calculating the flow rate based on the hydropower output to obtain the final water level, which is input into the medium- and long-term model for the next scheduling period, thereby simulating the scheduling process.

Description

Technical Field

[0001] The present invention relates to the intersection of renewable energy utilization and reservoir scheduling, and more particularly to a method and system for long-term and short-term coordinated water-wind-solar complementary integrated scheduling. Background Art

[0002] Energy has become a crucial component of sustainable development goals, impacting global ecological and environmental improvements, climate change, and people's lifestyles. Under the dual pressures of climate change and energy transition, clean energy, represented by hydropower, wind power, and photovoltaics, has experienced rapid growth globally. Vigorously developing clean energy sources such as hydropower, wind power, and photovoltaics, and striving to create a clean, low-carbon, safe, and efficient energy system, is an inevitable choice for achieving the "dual carbon" goals and building a new power system. However, the randomness, volatility, and undispatching nature of wind and photovoltaic power restrict their large-scale grid connection and absorption. Leveraging the natural complementarity of resources and the flexibility of hydropower, and implementing multi-energy complementary operation and management, is an effective way to promote the absorption of new energy.

[0003] However, due to the imperfect multi-energy complementary scheduling theory adapted to the new power system, the synergy of the hydro-wind-solar complementary system is insufficient, and the problem of "wind and solar curtailment" of new energy is very serious. At present, research on multi-energy complementary scheduling is mainly focused on a single time scale, which is essentially the coordination between different energy sources. Although the traditional multi-scale nesting method has achieved a certain degree of coordination between medium- and long-term scheduling and short-term scheduling, it does not consider the mutual feedback between long-term and short-term scheduling, making it difficult to form an integrated, closed-loop control model, and it cannot guarantee the global optimality of the multi-timescale coordinated scheduling strategy. Summary of the Invention

[0004] The present invention provides a long-term and short-term coordinated integrated scheduling method and system for water, wind and solar power complementarity, which is used to solve the above-mentioned problems existing in the prior art, namely, the problem of insufficient synergy between flexible hydropower and non-schedulable new energy in the prior art.

[0005] The present invention provides a long-term and short-term coordinated water-wind-solar complementary integrated scheduling method, the method comprising:

[0006] Obtaining solar radiation, air temperature, and wind speed data, and inputting the solar radiation, air temperature, and wind speed data into wind power and photovoltaic output models, respectively, to obtain wind power output and photovoltaic output data;

[0007] Taking maximum power generation and highest power generation guarantee rate as optimization goals, a medium- and long-term optimization scheduling model for hydropower, wind power and solar power complementarity is constructed. Based on wind power output and photovoltaic output data, an implicit stochastic optimization method is used to obtain the medium- and long-term scheduling function.

[0008] With the optimization objectives of maximizing power generation, minimizing the standard deviation of the grid's residual load, and minimizing the comprehensive risk rate of the hydro-wind-solar hybrid system, a short-term optimization scheduling model for hydro-wind-solar hybrid systems was constructed. Using an intelligent optimization algorithm, the optimal power generation plan unit line for each month was obtained, thereby deriving the system's daily power generation plan compilation rules.

[0009] With the optimization objectives of maximizing the power generation of the hydro-wind-solar complementary system, minimizing the standard deviation of the grid's residual load, and minimizing the comprehensive risk rate of the hydro-wind-solar complementary system, a hydro-wind-solar integrated optimization scheduling model is constructed.

[0010] Based on the integrated optimization scheduling model of water, wind and solar power, simulation scheduling is carried out through medium- and long-term scheduling functions to obtain the daily average hydropower output and daily discharge volume, which are used as inputs of the short-term model. The daily power generation plan is obtained according to the daily power generation plan compilation rules. The hydropower output is inferred through the daily power generation plan and the wind power and photovoltaic power output. The flow process is calculated based on the hydropower output considering the reservoir characteristic constraints to obtain the final water level, which is input into the medium- and long-term model of the next scheduling period, thereby simulating the scheduling process.

[0011] Optionally, the optimization goal is to maximize power generation and maximize power generation guarantee rate, and to construct a medium- and long-term optimization scheduling model for water, wind, and solar power complementarity, specifically including:

[0012] The medium- and long-term optimization scheduling model for water-wind-solar complementary power generation includes a deterministic optimization scheduling model and a simulation-optimization model. The objective functions for determining the maximum power generation and the highest power generation guarantee rate of the two models are:

[0013] The maximum power generation of the water-wind-solar complementary system is calculated using the following formula:

[0014]

[0015] The following formula is used to calculate that the water-wind-solar complementary system has the highest power generation guarantee rate:

[0016]

[0017] Among them, EP is used to indicate the total power generation of the complementary system; LT is used to indicate the number of medium- and long-term scheduling periods; ΔT is used to indicate the number of medium- and long-term scheduling periods; and They represent the hydropower, photovoltaic and wind power outputs in period i respectively; It is used to indicate the number of times that the sum of the outputs of the hydro-wind-solar complementary system exceeds the guaranteed output during the entire scheduling period.

[0018] Optionally, the medium- and long-term scheduling function is obtained by adopting an implicit stochastic optimization method based on the wind and solar power output data, specifically including:

[0019] According to the wind and solar power output data, it is input into the deterministic optimization scheduling model for scheduling to obtain sample data;

[0020] The sample data is analyzed to obtain the medium- and long-term scheduling function.

[0021] Optionally, analyzing the sample data to obtain a medium- to long-term scheduling function specifically includes:

[0022] The medium- and long-term scheduling function is obtained using the following formula:

[0023]

[0024] Among them, k and K are the number and total number of scheduling functions. When the medium- and long-term scheduling period is ten days, K = 36; when the medium- and long-term scheduling period is monthly, K = 12; and are the independent variables and decision variables of the scheduling function; and Parameters of the linear scheduling function.

[0025] Optionally, the optimization objectives are to maximize power generation, minimize the standard deviation of the grid's residual load, and minimize the comprehensive risk rate of the hydro-wind-solar complementary system, and to construct a short-term optimal scheduling model for hydro-wind-solar complementary systems, specifically including:

[0026] The objective function to determine the maximum power generation of the hydro-wind-solar complementary system, the minimum standard deviation of the grid residual load, and the minimum comprehensive risk rate of the hydro-wind-solar complementary system is:

[0027] The maximum power generation of the water-wind-solar complementary system is calculated using the following formula:

[0028]

[0029] The following formula is used to calculate the minimum standard deviation of the grid residual load:

[0030]

[0031] The following formula is used to calculate the minimum comprehensive risk rate of the water-wind-solar complementary system:

[0032]

[0033] in, and They represent the hydropower, photovoltaic and wind power outputs during period t respectively; and They are used to indicate the power abandonment rate and load loss rate in the t-th scheduling period respectively; L t Used to indicate the large power grid load in the tth dispatch period; Used to indicate the remaining load in the tth scheduling period; represents the average residual load of the complementary system; represents the power generation plan of the complementary system in the tth period; T represents the total number of scheduling periods.

[0034] Optionally, obtaining the daily power generation plan according to the daily power generation plan compilation rule specifically includes:

[0035] According to the daily power generation plan compilation rules, the daily available electricity is allocated according to the power generation plan unit line of the corresponding month to obtain the daily power generation plan.

[0036] Optionally, obtaining solar radiation, air temperature, and wind speed data, and inputting the solar radiation, air temperature, and wind speed data into wind power and photovoltaic output models, respectively, to obtain wind power output and photovoltaic output data, specifically includes:

[0037] The wind power output is calculated using the following formula:

[0038]

[0039] in, is the wind power output in the i-th period; θ is the installed capacity of the wind farm; v hub,i is the wind speed at the hub of the fan in the i-th period; v in 、v out and v r are the cut-in, cut-out and full-load wind speeds of the wind turbine, respectively;

[0040] The photoelectric output is calculated using the following formula:

[0041]

[0042] in, is the actual average output of photovoltaic power in the i-th period; χ is the installed capacity of the photovoltaic power station; is the solar radiation intensity in the i-th period; T i is the solar panel temperature; and T stc is the solar radiation intensity and air temperature under standard test conditions.

[0043] Optionally, the optimization objectives are to maximize the power generation of the water-wind-solar complementary system, minimize the standard deviation of the grid residual load, and minimize the comprehensive risk rate of the water-wind-solar complementary system, and to construct an integrated optimization scheduling model for water-wind-solar complementary, specifically including:

[0044] The objective function of maximizing the system power generation, achieving the highest power generation guarantee rate, minimizing the system operation risk, and minimizing the residual load standard deviation is:

[0045]

[0046] Wherein, EP and GR represent the total power generation and power generation guarantee rate of the system respectively; t represents the number of the medium- and long-term scheduling period; and They represent the hydropower, photovoltaic and wind power outputs during period t respectively; P firm Indicates the guaranteed output of the system; is the number of times the system output exceeds the guaranteed output during the entire dispatch period; RI and RL represent the system's short-term risk rate and residual load standard deviation, respectively; i represents the short-term dispatch period number; and They represent the power abandonment rate and load loss rate in the i-th period respectively; L i represents the grid load in the i-th period; represents the power generation plan of the complementary system in period i; Represents the average residual load of the complementary system.

[0047] The present invention also provides a long-term and short-term coordinated water-wind-solar complementary integrated scheduling system, comprising:

[0048] An acquisition module is used to obtain solar radiation, temperature and wind speed data, and input the solar radiation, temperature and wind speed data into the wind power and photovoltaic output models respectively to obtain wind power output and photovoltaic output data;

[0049] The medium- and long-term optimization scheduling module for hydropower, wind-solar hybrid power generation is used to build a medium- and long-term optimization scheduling model for hydropower, wind-solar hybrid power generation with the maximum power generation and the highest power generation guarantee rate as the optimization goals. Based on wind power output and photovoltaic output data, the medium- and long-term scheduling function is extracted using an implicit stochastic optimization method.

[0050] The short-term optimization scheduling module for hydropower, wind-solar hybrid systems is used to build a short-term optimization scheduling model for hydropower, wind-solar hybrid systems with the optimization objectives of maximizing power generation, minimizing the standard deviation of the grid's residual load, and minimizing the overall risk rate of the hydropower, wind-solar hybrid system. Using an intelligent optimization algorithm, it obtains the optimal power generation plan unit line for each month, thereby deriving the system's daily power generation plan compilation rules.

[0051] The hydro-wind-solar integrated optimization scheduling model construction module is used to build a hydro-wind-solar integrated optimization scheduling model with the optimization objectives of maximizing the power generation of the hydro-wind-solar complementary system, minimizing the standard deviation of the grid's residual load, and minimizing the comprehensive risk rate of the hydro-wind-solar complementary system;

[0052] The model solving module is used to simulate scheduling based on the integrated optimization scheduling model of water, wind and solar power through medium- and long-term scheduling functions to obtain the daily average hydropower output and daily discharge volume, which are used as inputs of the short-term model. The daily power generation plan is obtained according to the daily power generation plan compilation rules. The hydropower output is reversed through the daily power generation plan and wind power and photovoltaic output. The flow process is reversed according to the hydropower output considering the reservoir characteristic constraints to obtain the final water level, which is input into the medium- and long-term model of the next scheduling period to simulate the scheduling process.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a long-term and short-term coordinated water-wind-solar integrated scheduling method and system, which obtains wind power and photovoltaic output by inputting solar radiation, air temperature, and wind speed data; constructs a medium- and long-term optimal scheduling model for water-wind-solar complementarity, uses implicit random optimization methods to perform data analysis on medium- and long-term deterministic optimal scheduling samples, and extracts medium- and long-term scheduling rules; constructs a short-term optimal scheduling model for water-wind-solar complementarity, and determines a daily power generation plan compilation model; finally, by solving the medium- and long-term optimization model, the daily average hydropower output and daily discharge volume are obtained as short-term inputs, and the daily power generation plan is obtained through the daily power generation plan compilation rules, and the hydropower output is inferred through the daily power generation plan and wind power and photovoltaic output, and then the flow rate is inverted according to the hydropower output considering the reservoir characteristic constraints to obtain the final water level, which is input into the medium- and long-term model of the next scheduling period, thereby simulating the scheduling process. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0055] Figure 1 A schematic diagram of a long-term and short-term coordinated integrated water-wind-solar complementary scheduling method provided by an embodiment of the present invention;

[0056] Figure 2 A schematic diagram of a short-term daily power generation plan compilation model for a long-term and short-term coordinated water-wind-solar complementary integrated scheduling method provided in an embodiment of the present invention.

[0057] The above drawings illustrate specific embodiments of the present invention, which will be described in more detail below. These drawings and the accompanying description are not intended to limit the scope of the present invention in any way, but rather to illustrate the concept of the present invention to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0058] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0059] At present, the existing hydropower station operation and dispatching methods often do not take into account the participation of wind and solar power plants when they are formulated. Therefore, when guiding the operation and management of multi-energy complementarity, it often leads to insufficient synergy between flexible hydropower and non-dispatchable new energy, thus restricting the improvement of multi-energy complementarity efficiency.

[0060] In response to the above problems, the present invention provides a long-term and short-term coordinated integrated water-wind-solar scheduling method, which obtains wind power and photovoltaic output by inputting solar radiation, air temperature, and wind speed data; constructs a medium- and long-term optimal scheduling model for water-wind-solar complementarity, uses an implicit random optimization model to perform data analysis on medium- and long-term deterministic optimal scheduling samples, and extracts medium- and long-term scheduling rules; determines daily power generation plan compilation rules by constructing a short-term optimal scheduling model for water-wind-solar complementarity; finally, constructs a water-wind-solar complementary integrated optimal scheduling model, obtains daily average hydropower output and daily discharge as short-term inputs by solving the medium- and long-term optimization model, obtains daily power generation plan through daily power generation plan compilation rules, and reversely infers hydropower output through daily power generation plan and wind power and photovoltaic output, and then considers reservoir characteristic constraints to reversely calculate the flow process according to hydropower output to obtain the final water level, which is input into the medium- and long-term model of the next scheduling period, thereby simulating the scheduling process and ensuring the global optimality of the scheduling strategy.

[0061] The following describes in detail the technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems using specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The following embodiments of the present invention are described in conjunction with the accompanying drawings.

[0062] Figure 1 This is a schematic diagram of a long-term and short-term coordinated water-wind-solar complementary integrated scheduling method provided by an embodiment of the present invention. Figure 1 As shown, this embodiment shows a long-term and short-term coordinated water-wind-solar complementary integrated scheduling method, including:

[0063] S101: Input solar radiation, temperature and wind speed data to obtain wind power output and photovoltaic output data.

[0064] The wind power output and photovoltaic output data are calculated by inputting solar radiation, temperature and wind speed data into the wind power and photovoltaic output models respectively.

[0065] The following formula is used to calculate wind power output and photovoltaic output:

[0066] The calculation formula for wind power output is:

[0067]

[0068] in, is the wind power output in the i-th period; θ is the installed capacity of the wind farm; v hub,i is the wind speed at the hub of the fan in the i-th period; v in 、v out and v r are the cut-in, cut-out and full-load wind speeds of the wind turbine, respectively.

[0069] The calculation formula for photoelectric output is:

[0070]

[0071] Where: is the actual average output of photovoltaic power in the i-th period; χ is the installed capacity of the photovoltaic power station; is the solar radiation intensity in the i-th period; T i is the solar panel temperature; and T stc is the solar radiation intensity and air temperature under standard test conditions.

[0072] S102: Construct a medium- and long-term optimization scheduling model for water, wind, and solar power complementarity, and extract the medium- and long-term scheduling function using implicit stochastic optimization methods based on wind and solar power output data.

[0073] Among them, the medium- and long-term optimization scheduling model includes the deterministic optimization scheduling model and the simulation-optimization model. The objective functions of the two models are:

[0074] The specific formula for the maximum power generation of the water-wind-solar complementary system is as follows:

[0075]

[0076] The specific formula for the highest guaranteed power generation rate of the hydro-wind-solar complementary system is as follows:

[0077]

[0078] Among them, EP is used to indicate the total power generation of the complementary system; LT is used to indicate the number of medium- and long-term scheduling periods; ΔT is used to indicate the number of medium- and long-term scheduling periods; and They represent the hydropower, photovoltaic and wind power outputs in period i respectively; It is used to indicate the number of times that the sum of the outputs of the hydro-wind-solar complementary system exceeds the guaranteed output during the entire scheduling period.

[0079] Based on wind and solar power output data, an implicit stochastic optimization method is used to extract medium- and long-term scheduling functions.

[0080] Optionally, based on the wind and solar power output data, the data is input into a deterministic optimization scheduling model for scheduling to obtain sample data; the sample data is analyzed to extract the medium- and long-term scheduling function.

[0081] The specific form of the medium- and long-term scheduling function is as follows:

[0082]

[0083] Among them, k and K are the number and total number of scheduling functions. When the medium- and long-term scheduling period is ten days, K = 36; when the medium- and long-term scheduling period is monthly, K = 12; and are the independent variables and decision variables of the scheduling function; and Parameters of the linear scheduling function.

[0084] S103: Construct a short-term optimization scheduling model for the water-wind-solar complementary system, and use an intelligent optimization algorithm to obtain daily power generation plan compilation rules for the water-wind-solar complementary system.

[0085] Specifically, the objective functions of the short-term scheduling model include maximizing the power generation of the hydro-wind-solar complementary system, minimizing the standard deviation of the grid's residual load, and minimizing the comprehensive risk rate of the hydro-wind-solar complementary system.

[0086] Among them, the specific formula for the maximum power generation of the water-wind-solar complementary system is as follows:

[0087]

[0088] The specific formula for the minimum standard deviation of the grid residual load is as follows:

[0089]

[0090] The specific formula for minimizing the comprehensive risk rate of the hydro-wind-solar complementary system is as follows:

[0091]

[0092] in, and They represent the hydropower, photovoltaic and wind power outputs during period t respectively; and They are used to indicate the power abandonment rate and load loss rate in the t-th scheduling period respectively; L t Used to indicate the large power grid load in the tth dispatch period; Used to indicate the remaining load in the tth scheduling period; represents the average residual load of the complementary system; represents the power generation plan of the complementary system in the tth period; T represents the total number of scheduling periods.

[0093] The intelligent optimization algorithm is used to optimize the power generation plan unit line, and the daily available electricity is allocated according to the power generation plan unit line curve of the corresponding month to obtain the daily power generation plan of the water-wind-solar complementary system.

[0094] Specifically, the intelligent optimization algorithm is used to optimize the daily power generation plan compilation rules, using the "five-segment line" transmission curve type, such as Figure 2As shown in the figure, the decision variables are the time node and the output ratio corresponding to the time node. For example, the time node and the output ratio corresponding to the time node can be optimized by the cuckoo search algorithm to obtain the optimal power generation plan unit line for each month, a total of 12 power generation plan unit lines, that is, 12 "five-segment line" transmission curves are obtained.

[0095] According to the daily available power and the power generation plan unit line of the corresponding month, the daily power generation plan of the water-wind-solar hybrid system is obtained, as shown in the following formula:

[0096] N plan,t =unitline y,t ×energy t

[0097] Among them, N plan,t The daily power generation plan for the hydro-wind-solar hybrid system in period t; unitline y,t The unit line of power generation plan for month y corresponding to period t; energy t is the daily power generation of the water-wind-solar complementary system in period t.

[0098] S104: With the optimization objectives of maximizing the power generation of the hydro-wind-solar complementary system, minimizing the standard deviation of the residual load of the power grid, and minimizing the comprehensive risk rate of the hydro-wind-solar complementary system, an integrated optimization scheduling model for hydro-wind-solar complementary is constructed.

[0099] Specifically, the objective function of the integrated optimization scheduling model is determined to be the maximum system power generation, the highest power generation guarantee rate, the lowest system operation risk, and the smallest residual load standard deviation. The specific expression is as follows:

[0100]

[0101] Wherein, EP and GR represent the total power generation and power generation guarantee rate of the system respectively; t represents the number of the medium- and long-term scheduling period; and They represent the hydropower, photovoltaic and wind power outputs during period t respectively; P firm Indicates the guaranteed output of the system; is the number of times the system output exceeds the guaranteed output during the entire dispatch period; RI and RL represent the system's short-term risk rate and residual load standard deviation, respectively; i represents the short-term dispatch period number; and They represent the power abandonment rate and load loss rate in the i-th period respectively; L i represents the grid load in the i-th period; represents the power generation plan of the complementary system in period i; Represents the average residual load of the complementary system.

[0102] S105: Based on the integrated optimization scheduling model of water, wind and solar power, simulate scheduling through medium- and long-term scheduling functions to obtain the daily average hydropower output and daily discharge volume, which are used as inputs of the short-term model. The daily power generation plan is obtained through the daily power generation plan compilation rules. The hydropower output is reversed through the daily power generation plan and the wind power and photovoltaic power output. Then, considering the reservoir characteristic constraints, the flow process is reversed according to the hydropower output to obtain the final water level, which is input into the medium- and long-term model of the next scheduling period, thereby simulating the scheduling process.

[0103] Optionally, by solving the medium- and long-term optimization model, the optimal daily water / power allocation of the system can be determined to provide boundary conditions for subsequent short-term scheduling periods.

[0104] The specific formula for allocating water is as follows:

[0105]

[0106] Among them, W d is the amount of water allocated on day d; v ini and v end They are the initial and final storage capacity boundary conditions of the reservoir during the medium and long-term dispatching period; is the average inflow flow during the medium- and long-term dispatch period; ΔT is the length of the medium- and long-term dispatch period; It represents the ratio of available water on day d to the total available water during the scheduling period; It is the sum of daily wind power and photovoltaic power output; It is a nonlinear parameter between the daily water allocation ratio and the wind power and photovoltaic power output, and serves as one of the decision variables of the daytime optimization scheduling model.

[0107] The total power generation of the system for the next day is calculated based on the short-term forecast information of water, wind and solar power for the next day. The daily power generation process of the next day is scaled using the daily power generation plan compilation rules to obtain the day-ahead power generation plan process of the water-wind-solar complementary system to compile the daily power generation plan process.

[0108] The specific formula of the daily power generation plan curve is as follows:

[0109]

[0110] in, is the daily power generation plan curve of the complementary power station; and are the daily planned electricity of hydropower, photovoltaic power and wind power respectively; P t hsw is the planned output value; L is the output value of the typical daily load curve; is the average output of the typical daily load curve.

[0111] When prioritizing renewable energy integration, load demand can be met by renewable energy, with hydropower compensating for any load shortfalls. This step calculates hydropower output based on the daily power generation plan, grid load, and the real-time output of wind and photovoltaic power, allowing for subsequent short-term scheduling of output water levels.

[0112] The specific formula for calculating hydropower output is as follows:

[0113] P t h =P t hsw -P t s -P t w

[0114] Among them, P t s Contribute to photovoltaic theory; t w Contribute to wind power theory.

[0115] After obtaining the hydropower output, the flow process is back-calculated according to the hydropower output considering the reservoir characteristic constraints to obtain the final water level so as to carry out a refined simulation of subsequent scheduling.

[0116] At the end of this month's calculation, the end-of-month water level output by the short-term model is input into the medium- and long-term model as the beginning-of-month water level value of the next month to facilitate the scheduling process simulation of the next month, thereby realizing the coupled operation of the medium- and long-term and short-term.

[0117] The present invention provides a long-term and short-term coordinated water-wind-solar complementary integrated scheduling system, comprising:

[0118] An acquisition module is used to obtain solar radiation, temperature and wind speed data, and input the solar radiation, temperature and wind speed data into the wind power and photovoltaic output models respectively to obtain wind power output and photovoltaic output data;

[0119] The medium- and long-term optimization scheduling module for hydropower, wind-solar hybrid power generation is used to build a medium- and long-term optimization scheduling model for hydropower, wind-solar hybrid power generation with the maximum power generation and the highest power generation guarantee rate as the optimization goals. Based on wind power output and photovoltaic output data, the medium- and long-term scheduling function is extracted using an implicit stochastic optimization method.

[0120] The short-term optimization scheduling module for hydropower, wind-solar hybrid systems is used to build a short-term optimization scheduling model for hydropower, wind-solar hybrid systems with the optimization objectives of maximizing power generation, minimizing the standard deviation of the grid's residual load, and minimizing the overall risk rate of the hydropower, wind-solar hybrid system. Using an intelligent optimization algorithm, it obtains the optimal power generation plan unit line for each month, thereby deriving the system's daily power generation plan compilation rules.

[0121] The hydro-wind-solar integrated optimization scheduling model construction module is used to build a hydro-wind-solar integrated optimization scheduling model with the optimization objectives of maximizing the power generation of the hydro-wind-solar complementary system, minimizing the standard deviation of the grid's residual load, and minimizing the comprehensive risk rate of the hydro-wind-solar complementary system;

[0122] The model solving module is used to simulate scheduling based on the integrated optimization scheduling model of water, wind and solar power through medium- and long-term scheduling functions to obtain the daily average hydropower output and daily discharge volume, which are used as inputs of the short-term model. The daily power generation plan is obtained according to the daily power generation plan compilation rules. The hydropower output is reversed through the daily power generation plan and wind power and photovoltaic output. The flow process is reversed according to the hydropower output considering the reservoir characteristic constraints to obtain the final water level, which is input into the medium- and long-term model of the next scheduling period to simulate the scheduling process.

[0123] The present invention provides a long-term and short-term coordinated integrated scheduling method for water-wind-solar complementarity. By inputting solar radiation, temperature, and wind speed data, the wind power and photovoltaic output are obtained. A medium- and long-term optimal scheduling model for water-wind-solar complementarity is constructed, a deterministic optimal scheduling model and a simulation-optimization model are established, and a hidden random optimization model is used to perform data analysis on medium- and long-term deterministic optimal scheduling samples to extract medium- and long-term scheduling functions. A short-term optimal scheduling model for water-wind-solar complementarity is constructed. By determining the objective function, an intelligent optimization algorithm is used for optimization, and a cuckoo search algorithm is used to optimize the decision variables to obtain the optimal power generation plan unit line for each month, thereby obtaining the system. The daily power generation plan compilation rules of the system are established; finally, an integrated optimization scheduling model for water, wind and solar complementarity is constructed. By solving the medium- and long-term optimization model, the daily average hydropower output and daily discharge volume are obtained, which are used as the input of the short-term model, and the daily power generation plan is obtained through the daily power generation plan compilation rules. The hydropower output is inferred from the daily power generation plan and the wind and solar output. Then, considering the reservoir characteristic constraints, the flow rate is calculated according to the hydropower output to obtain the final water level, which is input into the medium- and long-term model of the next scheduling period. At the same time, the mutual feedback between the short-term and medium- and long-term is taken into account to achieve integrated collaborative optimization, which can ensure the global optimality of the scheduling strategy.

[0124] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0125] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A long-term and short-term coordinated integrated scheduling method for water, wind and solar power complementarity, characterized in that: The following steps are involved: Obtain solar radiation, temperature, and wind speed data, and input the solar radiation, temperature, and wind speed data into wind power and photovoltaic output models, respectively, to obtain wind power output and photovoltaic output data; Taking maximum power generation and highest power generation guarantee rate as optimization goals, a medium- and long-term optimization scheduling model for hydropower, wind power, and solar power complementarity is constructed. Based on wind power output and photovoltaic output data, an implicit stochastic optimization method is used to extract the medium- and long-term scheduling function. With the optimization objectives of maximizing power generation, minimizing the standard deviation of the grid's residual load, and minimizing the comprehensive risk rate of the hydro-wind-solar hybrid system, a short-term optimization scheduling model for hydro-wind-solar hybrid systems was constructed. Using an intelligent optimization algorithm, the optimal power generation plan unit line for each month was obtained, thereby deriving the system's daily power generation plan compilation rules. With the optimization objectives of maximizing the power generation of the hydro-wind-solar complementary system, minimizing the standard deviation of the grid's residual load, and minimizing the comprehensive risk rate of the hydro-wind-solar complementary system, a hydro-wind-solar integrated optimization scheduling model is constructed. The optimization objectives are to maximize the power generation of the water-wind-solar complementary system, minimize the standard deviation of the grid residual load, and minimize the comprehensive risk rate of the water-wind-solar complementary system, and to construct an integrated optimization scheduling model for water-wind-solar complementary systems, specifically including: The objective function of maximizing the system power generation, achieving the highest power generation guarantee rate, minimizing the system operation risk, and minimizing the residual load standard deviation is: in, EP , GR Respectively represent the total power generation and power generation guarantee rate of the system; Indicates the mid- to long-term scheduling period number; 、 and Respectively Hydropower, photovoltaic and wind power output during the time period; Indicates the guaranteed output of the system; The number of times the system output exceeds the guaranteed output during the entire dispatch period; RI , RL They represent the short-term risk rate of the system and the standard deviation of the residual load respectively; Indicates the short-term scheduling period number; and Respectively represent The power curtailment rate and load loss rate of the time period; Indicates the Grid load during the time period; Indicates the Generation planning for time-of-day complementary systems; represents the average residual load of the complementary system; Based on the integrated optimization scheduling model of water, wind and solar power, simulation scheduling is carried out through medium- and long-term scheduling functions to obtain the daily average hydropower output and daily discharge volume, which are used as inputs of the short-term model. The daily power generation plan is obtained according to the daily power generation plan compilation rules. The hydropower output is inferred through the daily power generation plan and the wind power and photovoltaic power output. The flow process is calculated based on the hydropower output considering the reservoir characteristic constraints to obtain the final water level, which is input into the medium- and long-term model of the next scheduling period, thereby simulating the scheduling process.

2. The long-term and short-term coordinated integrated water-wind-solar complementary scheduling method according to claim 1 is characterized in that: The optimization goal is to maximize power generation and guarantee the highest power generation rate, and to build a medium- and long-term optimization scheduling model for water, wind, and solar power complementarity, specifically including: The medium- and long-term optimization scheduling model for water-wind-solar complementary power generation includes a deterministic optimization scheduling model and a simulation-optimization model. The objective functions for determining the maximum power generation and the highest power generation guarantee rate of the two models are: The maximum power generation of the water-wind-solar complementary system is calculated using the following formula: The following formula is used to calculate that the water-wind-solar complementary system has the highest power generation guarantee rate: Among them, EP is used to indicate the total power generation of the complementary system; Used to indicate the number of medium and long-term scheduling periods; Used to indicate the mid- to long-term scheduling period number; 、 and Respectively Hydropower, photovoltaic and wind power output during the time period; It is used to indicate the number of times that the sum of the outputs of the hydro-wind-solar complementary system exceeds the guaranteed output during the entire scheduling period.

3. The long-term and short-term coordinated water-wind-solar complementary integrated scheduling method according to claim 1 is characterized in that: The method of extracting the medium- and long-term scheduling function based on the wind power output and photovoltaic output data by using the implicit stochastic optimization method specifically includes: According to the wind and solar power output data, it is input into the deterministic optimization scheduling model for scheduling to obtain sample data; Analyze the sample data and extract the medium- and long-term scheduling functions.

4. The long-term and short-term coordinated water-wind-solar complementary integrated scheduling method according to claim 3 is characterized in that: The sample data is analyzed to obtain a medium- and long-term scheduling function, which specifically includes: The medium- and long-term scheduling function is obtained using the following formula: in, and is the number and total number of scheduling functions, and the medium and long-term scheduling period is ten days. K =36; when the medium- and long-term scheduling period is monthly, K =12; and are the independent variables and decision variables of the scheduling function; and Parameters of the linear scheduling function.

5. The long-term and short-term coordinated water-wind-solar complementary integrated scheduling method according to claim 1 is characterized in that: The optimization objectives are to maximize power generation, minimize the standard deviation of the grid's residual load, and minimize the comprehensive risk rate of the hydro-wind-solar complementary system, and to construct a short-term optimal scheduling model for hydro-wind-solar complementary systems, specifically including: The objective function to determine the maximum power generation of the hydro-wind-solar complementary system, the minimum standard deviation of the grid residual load, and the minimum comprehensive risk rate of the hydro-wind-solar complementary system is: The maximum power generation of the water-wind-solar complementary system is calculated using the following formula: The following formula is used to calculate the minimum standard deviation of the grid residual load: The following formula is used to calculate the minimum comprehensive risk rate of the water-wind-solar complementary system: in, 、 and Respectively Hydropower, photovoltaic and wind power output during the time period; and Used to indicate the t The power curtailment rate and load loss rate of each dispatch period; To indicate the t The maximum grid load during each dispatching period; To indicate the t Residual load in each dispatch period; represents the average residual load of the complementary system; Indicates the t Generation planning for time-of-day complementary systems; T Indicates the total number of scheduling periods.

6. The long-term and short-term coordinated water-wind-solar complementary integrated scheduling method according to claim 1 is characterized in that: The daily power generation plan is obtained according to the daily power generation plan compilation rules, specifically including: According to the daily power generation plan compilation rules, the daily available electricity is allocated according to the power generation plan unit line of the corresponding month to obtain the daily power generation plan.

7. The long-term and short-term coordinated water-wind-solar complementary integrated scheduling method according to claim 1 is characterized in that: The solar radiation, temperature and wind speed data are obtained, and the solar radiation, temperature and wind speed data are input into the wind power and photovoltaic output models respectively to obtain the wind power output and photovoltaic output data, specifically including: The wind power output is calculated using the following formula: in, For the Wind power output during the time period; is the installed capacity of the wind farm; For the Wind speed at the turbine hub during the time period; 、 and are the cut-in, cut-out and full-load wind speeds of the wind turbine, respectively; The photoelectric output is calculated using the following formula: in, It is Actual average photovoltaic output during the period; Installed capacity for photovoltaic power stations; For the Solar radiation intensity during the period; is the solar panel temperature; and is the solar radiation intensity and air temperature under standard test conditions.

8. A long-term and short-term coordinated water-wind-solar complementary integrated scheduling system, characterized by: Specifically include: An acquisition module is used to obtain solar radiation, temperature and wind speed data, and input the solar radiation, temperature and wind speed data into the wind power and photovoltaic output models respectively to obtain wind power output and photovoltaic output data; The medium- and long-term optimization scheduling module for hydropower, wind-solar hybrid power generation is used to build a medium- and long-term optimization scheduling model for hydropower, wind-solar hybrid power generation with the maximum power generation and the highest power generation guarantee rate as the optimization goals. Based on wind power output and photovoltaic output data, the medium- and long-term scheduling function is extracted using an implicit stochastic optimization method. The short-term optimization scheduling module for hydropower, wind-solar hybrid systems is used to build a short-term optimization scheduling model for hydropower, wind-solar hybrid systems with the optimization objectives of maximizing power generation, minimizing the standard deviation of the grid's residual load, and minimizing the overall risk rate of the hydropower, wind-solar hybrid system. Using an intelligent optimization algorithm, it obtains the optimal power generation plan unit line for each month, thereby deriving the system's daily power generation plan compilation rules. The water-wind-solar integrated optimization scheduling model construction module is used to construct a water-wind-solar integrated optimization scheduling model with the optimization objectives of maximizing the power generation of the water-wind-solar complementary system, minimizing the standard deviation of the grid residual load, and minimizing the comprehensive risk rate of the water-wind-solar complementary system. The water-wind-solar integrated optimization scheduling model is constructed with the optimization objectives of maximizing the power generation of the water-wind-solar complementary system, minimizing the standard deviation of the grid residual load, and minimizing the comprehensive risk rate of the water-wind-solar complementary system, specifically including: The objective function of maximizing the system power generation, achieving the highest power generation guarantee rate, minimizing the system operation risk, and minimizing the residual load standard deviation is: in, EP , GR Respectively represent the total power generation and power generation guarantee rate of the system; Indicates the mid- to long-term scheduling period number; 、 and Respectively Hydropower, photovoltaic and wind power output during the time period; Indicates the guaranteed output of the system; The number of times the system output exceeds the guaranteed output during the entire dispatch period; RI , RL They represent the short-term risk rate of the system and the standard deviation of the residual load respectively; Indicates the short-term scheduling period number; and Respectively represent The power curtailment rate and load loss rate of the time period; Indicates the Grid load during the time period; Indicates the Generation planning for time-of-day complementary systems; represents the average residual load of the complementary system; The model solving module is used to simulate scheduling based on the integrated optimization scheduling model of water, wind and solar power through medium- and long-term scheduling functions to obtain the daily average hydropower output and daily discharge volume, which are used as inputs of the short-term model. The daily power generation plan is obtained according to the daily power generation plan compilation rules. The hydropower output is reversed through the daily power generation plan and wind power and photovoltaic output. The flow process is reversed according to the hydropower output considering the reservoir characteristic constraints to obtain the final water level, which is input into the medium- and long-term model of the next scheduling period to simulate the scheduling process.

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