A method and device for optimizing the scheduling of water-wind-solar-storage complementary power supply under a time-of-use electricity price model

Abstract

The present invention relates to the technical field of energy optimization and scheduling, and discloses a method and device for optimizing the scheduling of water, wind, solar and storage complementarity under a time-of-use electricity price model. The present invention constructs a medium- to long-term coupled and short-term coupled water, wind, solar and storage complementarity scheduling model under a time-of-use electricity price model, comprehensively considering the annual objective function including minimizing the annual power abandonment rate and minimizing the annual source-load difference, as well as the daily objective function including minimizing the total daily output and maximizing the daily profit, and can more comprehensively realize the complementary optimization scheduling of the water, wind, solar and storage complementary system. Furthermore, by constructing a medium- to long-term coupled and short-term coupled water, wind, solar and storage complementary scheduling model, it is possible to better coordinate the scheduling needs on different time scales, and improve the flexibility and efficiency of the overall scheduling. Finally, by solving the water, wind, solar and storage complementary scheduling model, the target complementary optimization scheduling result of water, wind, solar and storage is obtained, thereby improving the accuracy of the optimized scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization scheduling, and in particular to a method and device for optimizing scheduling of water, wind, solar and storage complementarity under a time-of-use electricity price model. Background Art

[0002] With the rapid development of renewable energy and the ongoing transformation of the electricity market, time-of-use (TOU) electricity pricing has gradually become an important electricity pricing mechanism. However, within this model, methods that can effectively optimize the medium- to long-term, as well as short- to long-term, complementary scheduling of hydropower, wind power, solar power, and storage are relatively scarce. Hydropower, wind power, and photovoltaic power, as key components of renewable energy, each possess distinct characteristics and limitations. Hydropower output is relatively stable, but is constrained by water resources and geographical conditions; wind power and photovoltaic power are intermittent and fluctuating, depending on natural conditions. The emergence of energy storage systems offers a new approach to addressing these issues. However, how to fully leverage the complementary advantages of hydropower, wind power, solar power, and storage within TOU electricity pricing to maximize economic benefits and ensure safe and stable grid operation remains a pressing issue. Summary of the Invention

[0003] In view of this, the present invention provides a method and device for optimizing the scheduling of water-wind-solar-storage complementarity under a time-of-use electricity price model, so as to solve the problem that the method that can effectively realize the medium- and long-term to short-term optimization scheduling of water-wind-solar-storage complementarity under the time-of-use electricity price model cannot give full play to the advantages of water-wind-solar-storage complementarity and achieve maximum economic benefits and safe and stable operation of the power grid.

[0004] In a first aspect, the present invention provides a method for optimizing the scheduling of a hydro-wind-solar-storage complementary system under a time-of-use electricity price model, for use in a hydro-wind-solar-storage complementary system; the method comprises:

[0005] Obtain the historical output data set and real-time output data set of the hydro-wind-solar-storage complementary system; based on the historical output data set, determine the annual objective function and the daily objective function, the annual objective function includes the annual power curtailment rate minimization objective function and the annual source-load difference minimization objective function of the hydro-wind-solar-storage complementary system, the daily objective function includes the daily total output fluctuation minimization objective function and the daily profit maximization objective function of the hydro-wind-solar-storage complementary system; based on the preset constraint condition set, the annual objective function and the daily objective function, use the historical output data set to construct a medium- and long-term coupled and short-term hydro-wind-solar-storage complementary scheduling model under the time-of-use electricity price mode; solve the hydro-wind-solar-storage complementary scheduling model based on the real-time output data set, and obtain the hydro-wind-solar-storage target complementary optimization scheduling result of the hydro-wind-solar-storage complementary system.

[0006] The present invention provides a method for optimizing the complementary scheduling of hydropower, wind, solar power, and storage under a time-of-use electricity pricing model. This method, subject to a set of preset constraints, uses both an annual and daily objective function as optimization objectives. It also utilizes a historical output dataset to construct a medium- to long-term coupled with short-term complementary scheduling model for hydropower, wind, solar power, and storage under a time-of-use electricity pricing model. This method comprehensively considers the annual objective functions of minimizing the annual curtailment rate and the annual source-load differential, as well as the daily objective functions of minimizing the daily total output and maximizing the daily revenue. This method enables more comprehensive optimization of the complementary scheduling of hydropower, wind, solar power, and storage systems. Furthermore, by constructing a medium- to long-term coupled with short-term complementary scheduling model, scheduling demands across different timescales can be better coordinated, improving the flexibility and efficiency of overall scheduling. Finally, by solving the complementary scheduling model, the accuracy of the optimized scheduling results for the target complementary scheduling of hydropower, wind, solar power, and storage is improved. Therefore, by implementing the present invention, the advantages of complementary scheduling of hydropower, wind, solar power, and storage are fully utilized under a time-of-use electricity pricing model. By comprehensively considering both annual and daily objectives, the efficiency and accuracy of the optimized scheduling of the target complementary scheduling of hydropower, wind, solar power, and storage are further improved, while maximizing economic benefits and ensuring safe and stable power grid operation.

[0007] In an optional embodiment, determining the annual objective function and the daily objective function based on the historical output data set includes:

[0008] Based on the historical output data set, the transaction loss data set of the hydro-wind-solar-storage complementary system is calculated; based on the historical output data set, the annual objective function is determined; based on the historical output data set and the transaction loss data set, the intraday objective function is determined.

[0009] The proposed method for optimizing the scheduling of hydro-wind-solar-storage complementarity under a time-of-use electricity pricing model calculates a transaction loss dataset for the hydro-wind-solar-storage complementarity system using a historical output dataset. This method then combines the historical output and transaction loss datasets to determine an intraday objective function. This approach considers both the safe and stable operation of the power grid and economic benefits, supporting the subsequent flexible scheduling of the hydro-wind-solar-storage complementarity system in the short term. Furthermore, the method combines the output dataset to determine an intra-year objective function, taking into account the operational characteristics of the hydro-wind-solar-storage complementarity system over longer timescales, supporting the subsequent flexible scheduling of the hydro-wind-solar-storage complementarity system in the medium and long term.

[0010] In an optional embodiment, a hydropower-wind-solar-storage complementary system is connected to an energy storage system. Based on a preset set of constraints, an intra-year objective function, and an intra-day objective function, and utilizing a historical output data set, a medium- to long-term coupled and short-term coupled hydropower-wind-solar-storage complementary scheduling model under a time-of-use electricity price model is constructed, including:

[0011] Based on the preset constraint set, the annual objective function, and the daily objective function, a medium- and long-term coupled hydro-wind-solar complementary scheduling model is constructed using the historical output data set. Based on the historical output data set, the energy storage output of the energy storage system in different scenarios is obtained through the preset calculation logic processing under the time-of-use electricity price model. Based on the preset constraint set and the hydro-wind-solar complementary scheduling model, the energy storage output and the historical output data set are used to construct a hydro-wind-solar-storage complementary scheduling model that meets the annual objective function and the daily objective function.

[0012] The method for optimizing the scheduling of water-wind-solar-storage complementarity under a time-of-use electricity price model provided by the present invention utilizes a historical output data set to construct a medium- to long-term coupled water-wind-solar-storage complementarity scheduling model that meets both the annual objective function and the daily objective function, under the constraints of a preset set of constraints. This model comprehensively considers the operating requirements and limitations of the water-wind-solar-storage complementarity system at different time scales, and can better coordinate scheduling needs at different time scales. Furthermore, based on the water-wind-solar complementarity scheduling model, a water-wind-solar-storage complementarity scheduling model that meets both the annual objective function and the daily objective function is constructed in combination with the energy storage system's energy storage output in different scenarios. By integrating the advantages of water, wind, solar, and energy storage systems, efficient energy utilization and complementarity can be achieved, and the reliability and sustainability of the system can be improved. At the same time, by rationally arranging the energy storage output, economic benefits can be maximized and system operating costs can be reduced.

[0013] In an optional embodiment, based on a preset set of constraints, an intra-year objective function, and an intra-day objective function, a medium- to long-term coupled and short-term coupled hydro-wind-solar complementary scheduling model is constructed using a historical output data set, including:

[0014] Based on the preset constraint set and the intra-year objective function, a medium- and long-term water-wind-solar complementary scheduling model is constructed using the historical output data set; based on the preset constraint set and the intra-day objective function, a medium- and long-term coupled water-wind-solar complementary scheduling model is constructed using the historical output data set and the medium- and long-term water-wind-solar complementary scheduling model.

[0015] In an optional embodiment, based on a historical output data set and processed by a preset calculation logic in a time-of-use electricity price mode, the energy storage system's energy storage output in different scenarios is obtained, including:

[0016] Obtain the theoretical total output of hydropower, wind power and solar power and the transmission channel capacity in the historical output data set; based on the time-of-use electricity price model, use the theoretical total output of hydropower, wind power and solar power and the transmission channel capacity to determine the energy storage data set of the energy storage system in different scenarios and the energy storage demand data set corresponding to the hydropower, wind power and solar power complementary system; use the theoretical total output of hydropower, wind power and solar power, the transmission channel capacity, the energy storage data set and the energy storage demand data set to calculate the energy storage output of the energy storage system in different scenarios.

[0017] The proposed method for optimizing the scheduling of a hydro-wind-solar-storage system under a time-of-use electricity pricing model combines the theoretical total output of hydro-wind-solar systems and the transmission channel capacity to determine the energy storage system's energy storage data sets and corresponding energy storage demand data sets for the hydro-wind-solar-storage system under different scenarios. This method fully considers the impact of electricity price differences in different time periods on the operation of the energy storage system. Furthermore, by calculating the energy storage system's energy storage output under different scenarios, it provides support for the subsequent optimization of the scheduling of the hydro-wind-solar-storage system.

[0018] In an optional embodiment, the hydro-wind-solar-storage complementary scheduling model is solved based on the real-time output data set to obtain the hydro-wind-solar-storage target complementary optimization scheduling result of the hydro-wind-solar-storage complementary system, including:

[0019] Based on the real-time output data set, the POA algorithm is used to solve the water-wind-solar-storage complementary scheduling model, and the initial water-wind-solar-storage complementary optimization scheduling result that meets the annual objective function is obtained; based on the initial water-wind-solar-storage complementary optimization scheduling result, the genetic algorithm is used to solve the water-wind-solar-storage complementary scheduling model, and the water-wind-solar-storage complementary optimization scheduling result that meets the daily objective function is obtained.

[0020] The water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price model provided by the present invention combines the POA algorithm and the genetic algorithm to solve the water-wind-solar-storage complementary optimization scheduling results, which can give full play to the advantages of the two algorithms and realize the comprehensive optimization scheduling of the water-wind-solar-storage complementary system.

[0021] In a second aspect, the present invention provides a hydro-wind-solar-storage complementary optimization scheduling device under a time-of-use electricity price model, for use in a hydro-wind-solar-storage complementary system; the device comprises:

[0022] An acquisition module is used to obtain the historical output data set and real-time output data set of the hydro-wind-solar-storage complementary system; a determination module is used to determine the intra-year objective function and the intra-day objective function based on the historical output data set. The intra-year objective function is used to characterize the intra-year power curtailment rate and the intra-year source-load difference of the hydro-wind-solar-storage complementary system, and the intra-day objective function is used to characterize the intra-day total output fluctuation and intra-day profit of the hydro-wind-solar-storage complementary system; a construction module is used to construct a medium- and long-term coupled and short-term hydro-wind-solar-storage complementary scheduling model based on a preset constraint set, the intra-year objective function and the intra-day objective function using the historical output data set; a solution module is used to solve the hydro-wind-solar-storage complementary scheduling model based on the real-time output data set to obtain the hydro-wind-solar-storage target complementary optimization scheduling result of the hydro-wind-solar-storage complementary system.

[0023] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, computer instructions being stored in the memory, and the processor executing the computer instructions to thereby execute the hydro-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price model of the above-mentioned first aspect or any corresponding embodiment thereof.

[0024] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the hydro-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price model of the above-mentioned first aspect or any corresponding embodiment thereof.

[0025] In a fifth aspect, the present invention provides a computer program product comprising computer instructions, which are used to enable a computer to execute the hydro-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price model of the above-mentioned first aspect or any corresponding embodiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 2. It is a flow chart of a method for optimizing the scheduling of hydro-wind-solar-storage complementary operations under a time-of-use electricity price model according to an embodiment of the present invention;

[0028] Figure 2 2. It is a flow chart of a method for optimizing the scheduling of water-wind-solar-storage complementary operations under another time-of-use electricity price mode according to an embodiment of the present invention;

[0029] Figure 3 1 is a flow chart of a method for optimizing the scheduling of hydropower-wind-solar-storage complementarity under another time-of-use electricity price mode according to an embodiment of the present invention;

[0030] Figure 4 2. This is a structural block diagram of a hydro-wind-solar-storage complementary optimization scheduling device in a time-of-use electricity price mode according to an embodiment of the present invention;

[0031] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0033] An embodiment of the present invention provides a method for optimizing the scheduling of the complementarity of water, wind, solar and storage under a time-of-use electricity price model. By giving full play to the advantages of the complementarity of water, wind, solar and storage under the time-of-use electricity price model, and comprehensively considering the annual goals and daily goals to achieve the maximization of economic benefits and the safe and stable operation of the power grid, the efficiency and accuracy of the optimized scheduling of the target complementarity of water, wind, solar and storage are further improved.

[0034] According to an embodiment of the present invention, an embodiment of a method for optimizing the complementary scheduling of hydropower, wind power, solar power and storage under a time-of-use electricity price model is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that here.

[0035] This embodiment provides a water-wind-solar-storage complementary optimization scheduling method under a time-of-use electricity price model, which can be used in a water-wind-solar-storage complementary system. Figure 1 Flowchart of the water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price mode according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0036] Step S101: Acquire a historical output data set and a real-time output data set of a hydro-wind-solar-storage complementary system.

[0037] Specifically, the historical output data set represents the operation data of the hydro-wind-solar-storage complementary system in the past period of time, which may include the actual grid-connected total output P of the hydro-wind-solar-storage complementary system. act (MW), the capacity of the transmission channel C line (MW), total theoretical output of water, wind and solar power P hwp (MW), hydropower output P hydro (MW), wind power output P wind (MW), photovoltaic output P pho (MW) and other data can be obtained through relevant monitoring equipment and sensors, or by establishing a data acquisition and transmission system, or through external systems such as power grid companies or meteorological departments. hwp =P hydro +P wind+P pho .

[0038] Furthermore, the real-time output data set represents the operating data of the hydro-wind-solar-storage complementary system at the current moment.

[0039] Step S102: determining an intra-year objective function and an intra-day objective function based on a historical output data set.

[0040] Among them, the annual objective function may include the annual power curtailment rate minimization objective function F1 and the annual source-load difference minimization objective function F2 of the hydro-wind-solar-storage complementary system; the daily objective function may include the daily total output fluctuation minimization objective function F3 and the daily profit maximization objective function F4 of the hydro-wind-solar-storage complementary system.

[0041] Specifically, based on the historical output data set, the corresponding annual objective function can be determined through the following steps:

[0042] 1. Analyze historical output data sets:

[0043] (1) Detailed analysis of the output data of hydropower, wind power, photovoltaic and energy storage systems in the historical output data set is conducted, and information such as total output, power curtailment, and source-load differences in different time periods is collected.

[0044] (2) Consider the impact of seasonal changes, resource fluctuations and other factors on system output.

[0045] 2. Set goals:

[0046] (1) Minimizing the annual curtailment rate: Calculate the ratio of curtailment to total power generation in historical data, and set minimizing the annual curtailment rate as one of the goals.

[0047] (2) Analyze the differences between system output and load demand in historical data, with the goal of minimizing the source-load differences within the year, so that the system can better meet the load demand and improve power supply stability.

[0048] 3. Build function:

[0049] (1) Construct an annual objective function based on the set goals. For example, the annual curtailment rate and the source-load difference can be weighted and summed to obtain a comprehensive annual objective function.

[0050] (2) When determining the weight coefficient, the importance of different objectives and the actual needs of the system can be considered.

[0051] Furthermore, the intraday objective function can be determined as follows:

[0052] 1. Analyze the characteristics of intraday data:

[0053] (1) Extract daily output data from the historical output data set and analyze the fluctuation of daily output, the difference in electricity prices in different time periods, and the operating characteristics of the energy storage system.

[0054] (2) Consider the changing patterns of daily load demand and the changes in water, wind and solar resources during the day.

[0055] 2. Set goals:

[0056] (1) Minimizing intraday total output fluctuations: To ensure the stable operation of the power grid and reduce the impact on the grid, minimizing the intraday total output fluctuations is one of the goals. For example, the fluctuations can be measured by calculating the standard deviation or variance of the output at different times of the day.

[0057] (2) Maximizing intraday profits: Combined with the time-of-use electricity price model, the profits from electricity sales at different times are considered. By rationally arranging the output of hydropower, wind power, solar power, and energy storage, the sales volume is increased during high electricity price periods, and energy storage is carried out during low electricity price periods to maximize intraday profits.

[0058] 3. Build function:

[0059] (1) Constructing an intraday objective function based on the set target. The intraday total output fluctuation and the revenue situation can be comprehensively considered. For example, the intraday objective function can be constructed by weighted summation.

[0060] (2) The weight coefficient can be adjusted according to the economic objectives and stability requirements of the system.

[0061] Furthermore, by combining the historical output data set to determine the annual and daily objective functions, clear goals and directions can be provided for the optimal scheduling of the hydro-wind-solar-storage complementary system, thereby improving the system's operating efficiency, stability, and economic benefits.

[0062] Step S103 , based on a preset constraint set, an intra-year objective function and an intra-day objective function, and using a historical output data set, a medium- to long-term and short-term coupled hydro-wind-solar-storage complementary scheduling model under a time-of-use electricity price mode is constructed.

[0063] The preset constraint condition set may include multiple constraint conditions shown in the following equations (1) to (12):

[0064] 0 <P act ≤C line (1)

[0065]

[0066] Z min ≤Z t ≤Z max (3)

[0067] Q min ≤Q t +S t ≤Q max (4)

[0068]

[0069] V t+1 =V t +(I t -Q t -S t )Δt×60(8)

[0070]

[0071] SOC min ≤SOC t ≤SOC max (11)

[0072]

[0073] Where max and min represent the upper and lower limits of the variable, respectively; ΔP represents the maximum output change per unit time allowed by the complementary system in the operating specifications, in MW; represents the actual total grid-connected output of the hydro-wind-solar-storage complementary system during period t, MW; Z t represents the water level of the reservoir at the end of period t, m; Q t represents the power generation flow in period t, m3 / h; S t Indicates the abandoned water flow rate during period t, m3 / h; represents the hydropower output during period t, MW; represents the wind power output during period t, MW; Represents the photovoltaic output during period t; I t represents the reservoir inflow during period t, m3 / h; represents the energy storage charging power during period t; represents the energy storage discharge power during period t, MW; S Wh It represents the rated capacity of energy storage, MW·h; SOC represents the state of charge of energy storage; Δt represents the time interval, h; η c Represents the energy storage charging efficiency; η d Indicates the energy storage discharge efficiency.

[0074] Specifically, under the constraints of a preset set of constraints, the historical output data set can be used to construct a medium- and long-term coupled and short-term coupled hydro-wind-solar-storage complementary scheduling model under the time-of-use electricity price model that meets the annual objective function and the daily objective function.

[0075] Step S104 , solving the water-wind-solar-storage complementary scheduling model based on the real-time output data set to obtain the water-wind-solar-storage target complementary optimization scheduling result of the water-wind-solar-storage complementary system.

[0076] Specifically, a real-time output data set can be input into a constructed hydro-wind-solar-storage complementary scheduling model, and then solved using an intelligent algorithm to output the target complementary optimized scheduling results for the hydro-wind-solar-storage complementary system. The intelligent algorithms can include particle swarm optimization, POA, and genetic algorithms.

[0077] This embodiment provides a method for optimizing the complementary scheduling of hydropower, wind, solar, and storage under a time-of-use electricity pricing model. This method, subject to a set of preset constraints, uses both an annual and daily objective function as optimization objectives. It also utilizes a historical output dataset to construct a medium- to long-term coupled and short-term coupled hydropower, wind, solar, and storage complementary scheduling model under a time-of-use electricity pricing model. This method comprehensively considers the annual objective functions of minimizing the annual curtailment rate and the annual source-load differential, as well as the daily objective functions of minimizing the daily total output and maximizing the daily revenue. This method enables more comprehensive optimization of the complementary scheduling of the hydropower, wind, solar, and storage complementary system. Furthermore, by constructing a medium- to long-term coupled and short-term coupled hydropower, wind, solar, and storage complementary scheduling model, it is possible to better coordinate scheduling demands across different timescales, improving the flexibility and efficiency of overall scheduling. Finally, by solving the hydropower, wind, solar, and storage complementary scheduling model, the accuracy of the resulting optimized scheduling results for the target complementary scheduling of hydropower, wind, solar, and storage is improved. Therefore, by implementing this method, the advantages of hydropower, wind, solar, and storage complementary scheduling are fully utilized under a time-of-use electricity pricing model. By comprehensively considering both annual and daily objectives, the efficiency and accuracy of the optimized scheduling of the target complementary scheduling of hydropower, wind, solar, and storage are further improved, while maximizing economic benefits and ensuring safe and stable grid operation.

[0078] In this embodiment, a hydro-wind-solar-storage complementary optimization scheduling method under a time-of-use electricity price mode is provided, which can be used in a hydro-wind-solar-storage complementary system, and the hydro-wind-solar-storage complementary system is connected to an energy storage system.

[0079] Figure 2 Flowchart of the water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price mode according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0080] Step S201: Obtain the historical output data set and real-time output data set of the hydro-wind-solar-storage complementary system. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0081] Step S202: determining an intra-year objective function and an intra-day objective function based on the historical output data set.

[0082] Specifically, the above step S202 includes:

[0083] Step S2021: Calculate the transaction loss data set of the hydro-wind-solar-storage complementary system based on the historical output data set.

[0084] Among them, the transaction loss data set can include electricity sales revenue F y With the power abandonment penalty F ab .

[0085] Specifically, the electricity sales revenue F y It can be calculated by the following relations (13) to (14):

[0086]

[0087] Where: q t represents the electricity price at time t under the time-of-use electricity price mechanism, in yuan / (kW·h); q val represents the valley electricity price; q peak represents the peak electricity price; q av Indicates the average electricity price.

[0088] Furthermore, the power abandonment penalty F ab It can be calculated by the following relations (15) to (16):

[0089]

[0090] P net =P hwp +P sto (16)

[0091] Where: φ represents the penalty cost coefficient for power curtailment; P net represents the theoretical total output of the hydro-wind-solar-storage complementary system, MW; P sto Indicates energy storage output, MW.

[0092] Step S2022: Determine the annual objective function based on the historical output data set.

[0093] Specifically, according to the description of step S102, the annual objective function may include the annual power curtailment rate minimization objective function F1 of the hydro-wind-solar-storage complementary system and the annual source-load difference minimization objective function F2.

[0094] Furthermore, the objective function F1 for minimizing the curtailment rate within the year can be determined by combining the historical output data set, as shown in the following equation (17):

[0095]

[0096] Furthermore, the objective function F2 for minimizing the annual source load difference is expressed as follows:

[0097]

[0098] Step S2023: Determine the intraday objective function based on the historical output data set and the transaction loss data set.

[0099] Specifically, according to the description of step S102 , the intraday objective function may include an objective function F3 for minimizing the intraday total output fluctuation of the hydro-wind-solar-storage complementary system and an objective function F4 for maximizing the intraday profit.

[0100] Furthermore, the objective function F3 for minimizing the intraday total output fluctuation can be determined by combining the historical output data set, as shown in the following equation (19):

[0101]

[0102] Where: represents the variance of the total output of hydropower, wind power, solar power and storage power on the dth day; P act,t It represents the actual total grid-connected power output of hydropower, wind power, solar power and energy storage at hour t, 10,000 kW; It represents the average actual grid-connected total output of hydropower, wind power, solar power and energy storage at hour t, in 10,000 kW.

[0103] Furthermore, the intraday profit maximization objective function F4 can be determined by combining the transaction loss data set, as shown in the following equation (20):

[0104] F4=max(F y -F ab ) (20)

[0105] Step S203 , based on the preset constraint condition set, the intra-year objective function and the intra-day objective function, and using the historical output data set, a medium- to long-term and short-term coupled hydro-wind-solar-storage complementary scheduling model under the time-of-use electricity price mode is constructed.

[0106] Specifically, the above step S203 includes:

[0107] Step S2031 : Based on a preset constraint set, an intra-year objective function, and an intra-day objective function, a medium- to long-term coupled and short-term coupled hydro-wind-solar complementary scheduling model is constructed using a historical output data set.

[0108] Specifically, under the constraints of multiple constraints shown in the above-mentioned equations (1) to (12), the historical output data set can be used to train and construct a water-wind-solar complementary scheduling model until a medium- and long-term coupled water-wind-solar complementary scheduling model that satisfies the objective functions shown in the above-mentioned equations (17) to (20) is obtained.

[0109] In some optional implementations, the above step S2031 includes:

[0110] Step a1: Based on the preset constraint set and the annual objective function, a medium- and long-term hydro-wind-solar complementary scheduling model is constructed using the historical output data set.

[0111] Step a2: Based on the preset constraint set and the intraday objective function, a medium- and long-term coupled water-wind-solar complementary scheduling model is constructed using the historical output data set and the medium- and long-term water-wind-solar complementary scheduling model.

[0112] Specifically, under the constraints of multiple constraints shown in the above-mentioned equations (1) to (12), with the minimization of the annual power curtailment rate and the minimization of the annual source-load difference as the optimization goals, a medium- and long-term hydro-wind-solar complementary scheduling model that satisfies the objective functions shown in the above-mentioned equations (17) and (18) is constructed by training the historical output data set.

[0113] Furthermore, based on the constructed medium- and long-term hydro-wind-solar complementary scheduling model, with the minimization of intraday total output fluctuations and the maximization of intraday benefits as the optimization objectives, the historical output data set is continued to be used for model training until a medium- and long-term coupled hydro-wind-solar complementary scheduling model that satisfies the objective functions shown in the above-mentioned equations (19) and (20) is obtained.

[0114] Step S2032 : Based on the historical output data set, the energy storage output of the energy storage system in different scenarios is obtained through a preset calculation logic process under the time-of-use electricity price mode.

[0115] Specifically, by considering the intraday calculation logic of the energy storage system in different scenarios under the time-of-use electricity price model, the energy storage output of the energy storage system in different scenarios can be calculated.

[0116] In some optional implementations, the above step S2032 includes:

[0117] Step b1: Obtain the theoretical total output of water, wind and solar power and the transmission channel capacity in the historical output data set.

[0118] Step b2: Based on the time-of-use electricity price model and using the theoretical total output of hydropower, wind power, and solar power and the transmission channel capacity, determine the energy storage data set of the energy storage system under different scenarios and the energy storage demand data set corresponding to the hydropower, wind power, and solar power storage complementary system.

[0119] In step b3, the energy storage output of the energy storage system under different scenarios is calculated using the theoretical total output of water, wind and solar power, the transmission channel capacity, the energy storage data set and the energy storage demand data set.

[0120] The energy storage data set is used to characterize the remaining energy storage space ΔSOC of the energy storage system under different scenarios, as shown in the following equation (21):

[0121] ΔSOC=SOC max -SOC curt(twenty one)

[0122] Where: ΔSOC represents the change in the remaining charge and discharge capacity of the energy storage system; SOC max Indicates the maximum capacity state of the energy storage system; SOC curt Indicates the current capacity status of the energy storage system.

[0123] Furthermore, the energy storage demand dataset is used to characterize the energy storage demand ΔSOC′ required by the energy storage system for the hydro-wind-solar-storage complementary system in different scenarios, as shown in the following equation (22):

[0124] ΔSOC′=(P hwp -C line )η c

[0125] Specifically, the calculation process may include the following steps:

[0126] 1. When the total output of water, wind and solar power in the complementary system is P hwp At its peak:

[0127] (1) If P hwp Greater than C line , then the energy storage system should play a charging role. At this time, it is judged whether the remaining energy storage space ΔSOC of the energy storage system can completely cover the extra output ΔSOC′ of the complementary system:

[0128] a. If ΔSOC>ΔSOC′, then: P act =C line , at this time the energy storage output P sto =P hwp -C line ;

[0129] b. If ΔSOC < ΔSOC′, then: P act =C line , at this time the energy storage output P sto =SOC max S Wh / η c Δt.

[0130] (2) If P hwp Less than C line , then the energy storage system should play a discharging role. At this time, P act =P hwp +P sto , at this time the energy storage output P sto =(SOC curt -SOC min S Wh ) / η d Δt.

[0131] 2. When the total output of water, wind and solar power in the complementary system is P hwp When you are in a low period:

[0132] (1) If P hwp Greater than C line , then the energy storage system should play a charging role. At this time, it is judged whether the remaining energy storage space ΔSOC of the energy storage system can completely cover the extra output ΔSOC′ of the complementary system:

[0133] a. If ΔSOC>ΔSOC′, then: P act =C line , at this time the energy storage output P sto =P hwp -C line ;

[0134] b. If ΔSOC < ΔSOC′, then: P act =C line , at this time the energy storage output P sto =SOC max S Wh / η c Δt.

[0135] (2) If P hwp Less than C line , then the energy storage system should play a discharging role. At this time, if the energy storage system releases all the stored energy (i.e. P hwp +P sto , where P sto =(SOC curt -SOC min S Wh ) / η d Δt), whether the total output of the hydro-wind-solar-storage complementary system reaches the transmission channel capacity C line :

[0136] a. If P hwp +P sto >C line , then :P act =C line , at this time the energy storage output P sto =(SOC curt -SOC min S Wh ) / (η d Δt)-C line ;

[0137] b. If P hwp +P sto <C line , you can consider time-of-use electricity prices:

[0138] (b1) If q t η d ≥q peak Then discharge, that is, P act =P hwp +P sto , at this time the energy storage output P sto =(SOC curt -SOC min S Wh ) / (η d Δt);

[0139] (b2) If q t η d peak Then P act =P hwp , at this time the energy storage output P sto =0.

[0140] Step S2033: Based on the preset constraint condition set and the hydro-wind-solar complementary scheduling model, the hydro-wind-solar complementary scheduling model that meets the intra-year objective function and the intra-day objective function is constructed using the energy storage output and historical output data set.

[0141] Specifically, based on the constructed hydro-wind-solar complementary scheduling model, the annual optimization objectives are to minimize the curtailment rate and the source-load difference within the year, and the daily optimization objectives are to minimize the total daily output fluctuation and maximize the daily profit. The model training is continued in combination with the obtained energy storage output and historical output data sets until a hydro-wind-solar-storage complementary scheduling model that satisfies the objective functions shown in the above-mentioned relations (17) to (20) is obtained. In the process of model construction, multiple constraints shown in the above-mentioned relations (1) to (12) need to be satisfied simultaneously.

[0142] Step S204: Solve the hydro-wind-solar-storage complementary scheduling model based on the real-time output data set to obtain the hydro-wind-solar-storage complementary target optimized scheduling result of the hydro-wind-solar-storage complementary system. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0143] ​The optimized scheduling method for hydro-wind-solar-storage complementarity under a time-of-use electricity pricing model, provided in this embodiment, uses historical output datasets to calculate a transaction loss dataset for the hydro-wind-solar-storage complementarity system. This method then combines these historical output and transaction loss datasets to determine a daily objective function, taking into account both safe and stable grid operation and economic benefits. Furthermore, the annual objective function is determined in conjunction with the output dataset, taking into account the operational characteristics of the hydro-wind-solar-storage complementarity system over longer timescales. Furthermore, under a set of preset constraints, the historical output dataset is used to construct a coupled medium- to long-term hydro-wind-solar complementarity scheduling model that meets both the annual and daily objective functions. This model comprehensively considers the operational requirements and limitations of the hydro-wind-solar-storage complementarity system over different timescales, enabling better coordination of scheduling needs across these different timescales. Furthermore, based on the hydro-wind-solar complementarity scheduling model, a hydro-wind-solar-storage complementarity scheduling model that meets both the annual and daily objective functions is constructed by combining the energy storage system's energy storage output under different scenarios. By integrating the advantages of hydro, wind, solar, and energy storage systems, this model can achieve efficient energy utilization and complementarity, improving system reliability and sustainability. At the same time, by rationally arranging energy storage output, economic benefits can be maximized and system operating costs can be reduced.

[0144] This embodiment provides a water-wind-solar-storage complementary optimization scheduling method under a time-of-use electricity price model, which can be used in a water-wind-solar-storage complementary system. Figure 3 Flowchart of the water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price mode according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0145] Step S301: Obtain the historical output data set and real-time output data set of the hydro-wind-solar-storage complementary system. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0146] Step S302: Determine the annual target function and the daily target function based on the historical output data set. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0147] Step S303: Based on the preset constraint set, the annual objective function and the daily objective function, and using the historical output data set, a medium- to long-term and short-term coupled hydropower, wind power, solar power and storage complementary scheduling model is constructed under the time-of-use electricity price model. Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0148] Step S304: Solve the water-wind-solar-storage complementary scheduling model based on the real-time output data set to obtain the water-wind-solar-storage target complementary optimization scheduling result of the water-wind-solar-storage complementary system.

[0149] Specifically, the above step S304 includes:

[0150] Step S3041: Based on the real-time output data set, the POA algorithm is used to solve the water-wind-solar-storage complementary scheduling model to obtain the initial water-wind-solar-storage complementary optimization scheduling result that meets the annual objective function.

[0151] The Peafowl Optimization Algorithm (POA) is a swarm intelligence optimization algorithm that includes the following steps:

[0152] (1) Population initialization: First, a certain number of individuals are randomly generated, each of which represents a potential solution to the problem.

[0153] (2) Fitness evaluation: Calculate the fitness value of each individual. The fitness value is usually determined based on the objective function of the problem and is used to measure the quality of the individual.

[0154] (3) Peacock display stage: In this stage, individuals with higher fitness values ​​(similar to male peacocks) will display and attract other individuals to approach them. This process can be achieved through some mathematical operations, such as moving other individuals a certain distance towards the superior individual.

[0155] (4) Individual update stage: Each individual is updated according to certain rules. The updating methods may include learning from excellent individuals, random perturbations, etc. This can increase the diversity of the population and prevent the algorithm from falling into local optimality.

[0156] (5) Repeated iteration: The fitness evaluation, peacock display, and individual update stages are repeated until the termination condition of the algorithm is met, such as reaching the maximum number of iterations or finding the optimal solution that meets certain accuracy requirements.

[0157] Specifically, the POA algorithm can be used to perform sliding optimization within two time periods based on the original operation mode of the hydropower station in combination with the annual distribution characteristics of wind and solar resources. The daily water level and water, wind and solar output process can be obtained. Furthermore, all time periods throughout the year are traversed until the accuracy requirements are met, and the optimized daily water level and water, wind and solar output process throughout the year can be obtained, that is, the initial complementary optimization scheduling result of water, wind, solar and storage that meets the annual objective function can be obtained.

[0158] Step S3042: Based on the initial optimized scheduling results of the water-wind-solar-storage complementary functions, a genetic algorithm is used to solve the water-wind-solar-storage complementary scheduling model to obtain optimized scheduling results of the water-wind-solar-storage complementary functions that meet the intraday objective function.

[0159] Specifically, a genetic algorithm can be used to solve the daily output process of the objective function for the integrated complementary operation of hydropower, wind, and solar power. An initial population of hydropower stations' daily output processes is randomly generated. The fitness of the individuals is calculated based on the daily objective function. Through operations such as reproduction, crossover, and mutation, the daily output distribution process for hydropower, wind, and solar power is ultimately determined, achieving daily hydropower, wind, and solar power complementarity.

[0160] Furthermore, under a time-of-use electricity pricing model, the charging and discharging capabilities of the energy storage system can be used to smooth out the peaks and fill the valleys of the total hydropower, wind, and solar power output curve. Furthermore, a genetic algorithm can be used to further optimize the intraday output process of the hydropower, wind, solar, and storage integrated complementary operation objective function, obtaining the most economically efficient optimized dispatch result for hydropower, wind, solar, and storage complementary operations, that is, obtaining the hydropower, wind, solar, and storage complementary optimized dispatch result that meets the intraday objective function.

[0161] The method for optimizing the scheduling of the complementary water-wind-solar-storage system under the time-of-use electricity price model provided in this embodiment combines the POA algorithm and the genetic algorithm to solve the optimization scheduling results of the complementary water-wind-solar-storage system, which can give full play to the advantages of the two algorithms and realize the comprehensive optimization scheduling of the complementary water-wind-solar-storage system.

[0162] This embodiment also provides a hydro-wind-solar-storage complementary optimization scheduling device under a time-of-use electricity price model. The device is used to implement the above-mentioned embodiments and preferred implementations, and the details that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0163] This embodiment provides a water-wind-solar-storage complementary optimization scheduling device under a time-of-use electricity price mode, which is used in a water-wind-solar-storage complementary system; Figure 4 As shown, the device includes:

[0164] The acquisition module 401 is used to obtain the historical output data set and the real-time output data set of the hydro-wind-solar-storage complementary system.

[0165] Determination module 402 is used to determine the annual objective function and the daily objective function based on the historical output data set. The annual objective function includes the objective function of minimizing the annual power curtailment rate of the hydro-wind-solar-storage complementary system and the objective function of minimizing the annual source-load difference. The daily objective function includes the objective function of minimizing the intraday total output fluctuation and the objective function of maximizing the intraday profit of the hydro-wind-solar-storage complementary system.

[0166] The construction module 403 is used to construct a medium- to long-term and short-term coupled hydro-wind-solar-storage complementary scheduling model based on a preset constraint set, an intra-year objective function and a daily objective function, using a historical output data set.

[0167] The solution module 404 is used to solve the water-wind-solar-storage complementary scheduling model based on the real-time output data set to obtain the water-wind-solar-storage target complementary optimization scheduling result of the water-wind-solar-storage complementary system.

[0168] In some optional implementations, the determining module 402 includes:

[0169] The calculation submodule is used to calculate the transaction loss dataset of the hydro-wind-solar-storage complementary system based on the historical output dataset.

[0170] The first determination submodule is configured to determine an annual target function based on a historical output data set.

[0171] The second determination submodule is used to determine the intraday objective function based on the historical output data set and the transaction loss data set.

[0172] In some optional embodiments, the hydro-wind-solar-storage complementary system is connected to the energy storage system; the building block 403 includes:

[0173] The first construction submodule is used to construct a medium- and long-term coupled water-wind-solar complementary scheduling model based on a preset constraint set, an intra-year objective function and a daily objective function using a historical output data set.

[0174] The processing submodule is used to obtain the energy storage output of the energy storage system in different scenarios based on the historical output data set and the preset calculation logic processing under the time-of-use electricity price mode.

[0175] The second construction submodule is used to construct a water-wind-solar-storage complementary scheduling model that meets the annual objective function and the daily objective function based on a preset constraint set and the water-wind-solar complementary scheduling model using the energy storage output and historical output data set.

[0176] In some optional embodiments, the first building block includes:

[0177] The first construction unit is used to construct a medium- and long-term water-wind-solar complementary scheduling model based on a preset constraint set and an annual objective function using a historical output data set.

[0178] The second construction unit is used to construct a medium- and long-term coupled water-wind-solar complementary scheduling model based on a preset constraint set and an intraday objective function, using a historical output data set and a medium- and long-term water-wind-solar complementary scheduling model.

[0179] In some optional implementations, the processing submodule includes:

[0180] The acquisition unit is used to obtain the theoretical total output of water, wind and solar power and the transmission channel capacity in the historical output data set.

[0181] The determination unit is used to determine the energy storage data set of the energy storage system in different scenarios and the energy storage demand data set corresponding to the water-wind-solar-storage complementary system based on the time-of-use electricity price model and the theoretical total output of water, wind and solar power and the transmission channel capacity.

[0182] The calculation unit is used to calculate the energy storage output of the energy storage system under different scenarios using the theoretical total output of water, wind and solar power, the capacity of the transmission channel, the energy storage data set and the energy storage demand data set.

[0183] In some optional implementations, the solution module 404 includes:

[0184] The first solving submodule is used to solve the water-wind-solar-storage complementary scheduling model based on the real-time output data set using the POA algorithm to obtain the initial water-wind-solar-storage complementary optimization scheduling result that meets the annual objective function.

[0185] The second solving submodule is used to solve the water-wind-solar-storage complementary scheduling model based on the initial complementary optimization scheduling results of water, wind, solar and storage using a genetic algorithm to obtain the water-wind-solar-storage complementary optimization scheduling results that meet the intraday objective function.

[0186] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0187] The hydro-wind-solar-storage complementary optimization scheduling device under the time-of-use electricity price model in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0188] The embodiment of the present invention also provides a computer device having the above Figure 4 The hydro-wind-solar-storage complementary optimization scheduling device under the time-of-use electricity price model is shown.

[0189] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of a GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0190] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0191] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0192] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0193] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0194] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0195] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0196] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0197] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for optimizing the scheduling of hydropower, wind power, solar power and storage complementarity under a time-of-use electricity price model, characterized in that: Used in a water-wind-solar-storage complementary system, the water-wind-solar-storage complementary system is connected to an energy storage system; the method includes: Obtaining a historical output data set and a real-time output data set of the hydro-wind-solar-storage complementary system; Based on the historical output data set, determining an intra-year objective function and an intra-day objective function, wherein the intra-year objective function includes an objective function for minimizing the intra-year power curtailment rate and an objective function for minimizing the intra-year source-load difference of the hydro-wind-solar-storage complementary system, and the intra-day objective function includes an objective function for minimizing the intra-day total output fluctuation and an objective function for maximizing the intra-day profit of the hydro-wind-solar-storage complementary system; Based on the preset constraint set, the intra-year objective function and the intra-day objective function, and using the historical output data set, a medium- to long-term and short-term coupled hydro-wind-solar-storage complementary scheduling model under the time-of-use electricity price model is constructed; Solving the water-wind-solar-storage complementary scheduling model based on the real-time output data set to obtain a water-wind-solar-storage target complementary optimization scheduling result of the water-wind-solar-storage complementary system; Wherein, based on the preset constraint condition set, the intra-year objective function and the intra-day objective function, and using the historical output data set, a medium- and long-term coupled hydro-wind-solar-storage complementary scheduling model under the time-of-use electricity price mode is constructed, including: Based on the preset constraint condition set, the intra-year objective function and the intra-day objective function, a medium- to long-term coupled and short-term coupled hydro-wind-solar complementary scheduling model is constructed using the historical output data set; Based on the historical output data set, the energy storage output of the energy storage system in different scenarios is obtained through preset calculation logic processing under the time-of-use electricity price mode; Based on the preset constraint condition set and the water-wind-solar complementary scheduling model, the water-wind-solar-storage complementary scheduling model that meets the intra-year objective function and the intra-day objective function is constructed using the energy storage output and the historical output data set.

2. The method according to claim 1, characterized in that Based on the historical output data set, an annual objective function and a daily objective function are determined, including: Calculating a transaction loss dataset of the hydro-wind-solar-storage complementary system based on the historical output dataset; determining the intra-year objective function based on the historical output data set; The intraday objective function is determined based on the historical output data set and the transaction loss data set.

3. The method according to claim 1, characterized in that Based on the preset constraint condition set, the intra-year objective function, and the intra-day objective function, a medium- to long-term coupled and short-term coupled hydro-wind-solar complementary scheduling model is constructed using the historical output data set, including: Based on the preset constraint set and the intra-year objective function, a medium- and long-term hydro-wind-solar complementary scheduling model is constructed using the historical output data set; Based on the preset constraint condition set and the intraday objective function, the medium- and long-term coupled water-wind-solar complementary scheduling model is constructed by utilizing the historical output data set and the medium- and long-term water-wind-solar complementary scheduling model.

4. The method according to claim 1, wherein Based on the historical output data set, the energy storage output of the energy storage system in different scenarios is obtained through preset calculation logic processing under the time-of-use electricity price mode, including: Obtaining the theoretical total output of water, wind, and solar power and the transmission channel capacity in the historical output data set; Based on the time-of-use electricity price model, using the theoretical total output of the hydropower, wind-solar power plant and the transmission channel capacity, determine the energy storage data set of the energy storage system in different scenarios and the energy storage demand data set corresponding to the hydropower, wind-solar power plant and storage complementary system; The energy storage output of the energy storage system in different scenarios is calculated using the theoretical total output of water, wind and solar power, the capacity of the transmission channel, the energy storage data set and the energy storage demand data set.

5. The method according to claim 1, wherein The water-wind-solar-storage complementary scheduling model is solved based on the real-time output data set to obtain the water-wind-solar-storage target complementary optimization scheduling result of the water-wind-solar-storage complementary system, including: Based on the real-time output data set, the POA algorithm is used to solve the water-wind-solar-storage complementary scheduling model to obtain an initial water-wind-solar-storage complementary optimization scheduling result that meets the annual objective function; Based on the initial water-wind-solar-storage complementary optimization scheduling result, the water-wind-solar-storage complementary scheduling model is solved using a genetic algorithm to obtain the water-wind-solar-storage complementary optimization scheduling result that meets the intra-day objective function.

6. A hydro-wind-solar-storage complementary optimization scheduling device under the time-of-use electricity price model, characterized in that: Used in a water-wind-solar-storage complementary system, the water-wind-solar-storage complementary system is connected to an energy storage system; the device includes: An acquisition module, configured to acquire a historical output data set and a real-time output data set of the hydro-wind-solar-storage complementary system; a determination module, configured to determine, based on the historical output data set, an annual objective function and a daily objective function, wherein the annual objective function includes an objective function for minimizing the annual power curtailment rate and an objective function for minimizing the annual source-load difference of the hydro-wind-solar-storage complementary system, and the daily objective function includes an objective function for minimizing the intraday total output fluctuation and an objective function for maximizing the intraday profit of the hydro-wind-solar-storage complementary system; A construction module is used to construct a medium- and long-term coupled water-wind-solar-storage complementary scheduling model based on a preset constraint set, the intra-year objective function and the intra-day objective function, and using the historical output data set; A solution module, configured to solve the water-wind-solar-storage complementary scheduling model based on the real-time output data set to obtain a target complementary optimization scheduling result of the water-wind-solar-storage complementary system; Wherein, the building blocks include: A first construction submodule is configured to construct a medium- to long-term coupled and short-term coupled hydro-wind-solar complementary scheduling model using the historical output data set based on the preset constraint condition set, the intra-year objective function, and the intra-day objective function; a processing submodule, configured to obtain the energy storage output of the energy storage system under different scenarios based on the historical output data set and through a preset calculation logic process under a time-of-use electricity price mode; The second construction submodule is used to construct the water-wind-solar-storage complementary scheduling model that meets the intra-year objective function and the intra-day objective function based on the preset constraint condition set and the water-wind-solar complementary scheduling model and using the energy storage output and the historical output data set.

7. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price model according to any one of claims 1 to 5 by executing the computer instructions.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the hydro-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price model according to any one of claims 1 to 5.

9. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price mode according to any one of claims 1 to 5.

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