Water, wind, light and storage complementary optimization scheduling method and device in time-of-use electricity price mode

Abstract

The invention relates to the technical field of energy optimization scheduling, and discloses a water-wind-solar-energy-storage complementary optimization scheduling method and device in a time-of-use electricity price mode. According to the method, a medium-and-long-term and short-term coupled water-wind-solar-energy-storage complementary scheduling model in the time-of-use electricity price mode is constructed; according to the method, an intra-year objective function including intra-year electricity abandonment rate minimization and intra-year source load difference minimization and an intra-day objective function including intra-day total output minimization and intra-day income condition maximization are comprehensively considered, and complementary optimization scheduling of the water-wind-solar-storage complementary system can be more comprehensively realized. Furthermore, by constructing a medium-and-long-term and short-term coupled water-wind-solar-energy-storage complementary scheduling model, scheduling requirements on different time scales can be better coordinated, and the flexibility and efficiency of overall scheduling are improved. And finally, obtaining a water-wind-light-storage target complementation optimization scheduling result by solving the water-wind-light-storage complementation scheduling model, and improving the accuracy of optimization 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-storage complementarity under a time-of-use electricity price mode. Background Art

[0002] With the rapid development of renewable energy and the continuous changes in the power market, the time-of-use electricity price model has gradually become an important electricity pricing mechanism. However, under this model, methods that can effectively achieve medium- and long-term optimization and dispatch of water, wind, light and storage complementarity are relatively scarce. As important components of renewable energy, hydropower, wind power and photovoltaics each have different characteristics and limitations. The output of hydropower is relatively stable, but it is constrained by water resources and geographical conditions; wind power and photovoltaics are intermittent and volatile, and depend on natural conditions. The emergence of energy storage systems provides a new way to solve these problems, but how to give full play to the advantages of water, wind, light and storage complementarity under the time-of-use electricity price model to maximize economic benefits and achieve safe and stable operation of the power grid is still an urgent problem to be solved. 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 realize the maximization of economic benefits and the safe and stable operation of the power grid.

[0004] In a first aspect, the present invention provides a method for optimizing the scheduling of water-wind-solar-storage complementary systems under a time-of-use electricity price mode, which is used in a water-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 abandonment 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 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 water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price mode provided by the present invention takes the annual objective function and the daily objective function as the optimization objectives under the constraints of the preset constraint condition set, and uses the historical output data set to construct the medium- and long-term coupled water-wind-solar-storage complementary scheduling model under the time-of-use electricity price mode, comprehensively considers the annual objective function including minimizing the annual power abandonment rate and minimizing the annual source-load difference, and the daily objective function including minimizing the total daily output and maximizing the daily revenue, and can more comprehensively realize the complementary optimization scheduling of the water-wind-solar-storage complementary system. Further, by constructing the medium- and long-term coupled water-wind-solar-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-storage complementary scheduling model, the accuracy of the obtained water-wind-solar-storage target complementary optimization scheduling results is improved. Therefore, by implementing the present invention, under the time-of-use electricity price mode, the advantages of water-wind-solar-storage complementarity are fully utilized, and by comprehensively considering the annual objectives and the daily objectives, on the basis of achieving maximum economic benefits and safe and stable operation of the power grid, the efficiency and accuracy of the water-wind-solar-storage target complementary optimization scheduling are further improved.

[0007] In an optional implementation, based on the historical output data set, determining the annual objective function and the daily objective function 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 water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price mode provided by the present invention calculates the transaction loss data set of the water-wind-solar-storage complementary system through the historical output data set, and then determines the intraday target function in combination with the historical output data set and the transaction loss data set, taking into account the economic benefits while considering the safe and stable operation of the power grid, and providing support for the subsequent realization of flexible scheduling of the water-wind-solar-storage complementary system in the short term. Furthermore, the annual target function is determined in combination with the output data set, taking into account the operating characteristics of the water-wind-solar-storage complementary system on a longer time scale, and providing support for the subsequent realization of flexible scheduling of the water-wind-solar-storage complementary system in the medium and long term.

[0010] In an optional implementation, the water-wind-solar-storage complementary system is connected to the energy storage system; based on a preset constraint set, an annual objective function and a daily objective function, and using a historical output data set, a medium- and long-term coupled and short-term water-wind-solar-storage complementary scheduling model under a time-of-use electricity price mode is constructed, including:

[0011] Based on the preset constraint set, annual objective function and daily objective function, a medium- and long-term coupled and short-term water-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 mode; based on the preset constraint set and the water-wind-solar complementary scheduling model, a water-wind-solar-storage complementary scheduling model that meets the annual objective function and the daily objective function is constructed using the energy storage output and the historical output data set.

[0012] The water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price mode provided by the present invention, under the constraints of a preset set of constraints, uses historical output data sets to construct a medium- and long-term coupled water-wind-solar-storage complementary scheduling model that meets the annual objective function and the daily objective function. It comprehensively considers the operating requirements and limitations of the water-wind-solar-storage complementary system on different time scales, and can better coordinate the scheduling needs on different time scales. Furthermore, on the basis of the water-wind-solar complementary scheduling model, a water-wind-solar-storage complementary scheduling model that meets 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, light and energy storage systems, efficient utilization and complementarity of energy can be achieved, and the reliability and sustainability of the system can be improved. At the same time, by reasonably arranging the energy storage output, economic benefits can be maximized and system operating costs can be reduced.

[0013] In an optional implementation, based on a preset constraint set, an annual objective function and a daily objective function, a medium- and long-term coupled hydro-wind-solar complementary scheduling model is constructed using a historical output data set, including:

[0014] Based on the preset set of constraints 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 set of constraints 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 implementation, based on the historical output data set, after being processed by the preset calculation logic in the time-of-use electricity price mode, the energy storage output of the energy storage system in different scenarios is obtained, including:

[0016] Obtain the theoretical total output of hydropower, wind and solar power and the capacity of the transmission channel in the historical output data set; based on the time-of-use electricity price model, use the theoretical total output of hydropower, wind and solar power and the capacity of the transmission channel 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 and solar power complementary system; use the theoretical total output of hydropower, wind and solar power, the capacity of the transmission channel, 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 water-wind-solar-storage complementary optimization scheduling method provided by the present invention under the time-of-use electricity price mode can fully consider the impact of the difference in electricity prices in different time periods on the operation of the energy storage system by combining the theoretical total output of water, wind and solar power and the capacity of the transmission channel 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. Further, by calculating the energy storage output of the energy storage system in different scenarios, support is provided for the subsequent optimization scheduling of the water-wind-solar-storage complementary system.

[0018] In an optional implementation, 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:

[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 mode provided by the present invention 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 by combining the POA algorithm and the genetic algorithm to solve the water-wind-solar-storage complementary optimization scheduling results.

[0021] In a second aspect, the present invention 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; the device comprises:

[0022] An acquisition module is used to acquire the historical output data set and the real-time output data set of the water-wind-solar-storage complementary system; a determination module is used to determine the annual target function and the daily target function based on the historical output data set. The annual target function is used to characterize the annual power abandonment rate and the annual source-load difference of the water-wind-solar-storage complementary system, and the daily target function is used to characterize the intraday total output fluctuation and the intraday profit of the water-wind-solar-storage complementary system; a construction module is used to construct a medium- and long-term coupled and short-term water-wind-solar-storage complementary scheduling model based on a preset constraint set, the annual target function and the daily target function using the historical output data set; a solution module 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.

[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, the memory storing computer instructions, and the processor executing the computer instructions to execute the water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price model of the 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 a time-of-use electricity price model of the 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 implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 It is a flow chart of a method for optimizing the scheduling of water-wind-solar-storage complementarity under a time-of-use electricity price mode according to an embodiment of the present invention;

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

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

[0030] Figure 4 is a structural block diagram of a water-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 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the 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 creative work are within the scope of protection of the present invention.

[0033] The 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 scheduling of complementary water-wind-solar-storage power 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 herein.

[0035] In this embodiment, a water-wind-solar-storage complementary optimization scheduling method under a time-of-use electricity price mode is provided, which can be used for a water-wind-solar-storage complementary system. Figure 1 is a flow chart of a method for optimizing the scheduling of water-wind-solar-storage complementarity under a 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, obtaining 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 collection 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, based on the historical output data set, determine the annual target function and the daily target function.

[0040] Among them, the annual objective function may include the annual power abandonment 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, combined with 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 abandonment, and source-load difference in different time periods is compiled.

[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 power curtailment rate: Calculate the ratio of power curtailment in historical data to total power generation, and set minimizing the annual power 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 difference within a year, so that the system can better meet 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 power curtailment rate and 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 in the following way:

[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 the fluctuation of total daily output: In order to ensure the stable operation of the power grid and reduce the impact on the power grid, minimizing the fluctuation of total daily output is one of the goals. For example, the fluctuation can be measured by calculating the standard deviation or variance of the output at different times of the day.

[0057] (2) Maximizing intraday revenue: Combined with the time-of-use electricity price model, the revenue from electricity sales at different times is considered. By rationally arranging the output of hydropower, wind power, solar power and energy storage, increasing electricity sales during high electricity price periods and storing energy during low electricity price periods, the intraday revenue can be maximized.

[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, by constructing an intraday objective function through a weighted summation method.

[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 operating efficiency, stability and economic benefits of the system.

[0062] Step S103, based on the preset constraint condition set, the annual objective function and the daily objective function, and using the historical output data set, a medium- and long-term coupled and short-term water-wind-solar-storage complementary scheduling model under the 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]

[0070]

[0071] V t+1 =V t +(I t -Q t -S t )Δt×60 (8)

[0072]

[0073]

[0074] SOC min ≤SOC t ≤SOC max (11)

[0075]

[0076] Wherein, 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 operation specification, 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 in period t, MW; represents the wind power output in period t, MW; Represents the photovoltaic output during period t; I t Represents the reservoir flow rate during period t, m3 / h; Indicates the energy storage charging power during period t; represents the energy storage discharge power in 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.

[0077] 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 water-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.

[0078] 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.

[0079] Specifically, the real-time output data set can be input into the constructed water-wind-solar-storage complementary scheduling model, and the model can be solved using an intelligent algorithm, that is, the water-wind-solar-storage target complementary optimization scheduling result of the corresponding water-wind-solar-storage complementary system can be output. Among them, the intelligent algorithm can be a particle swarm optimization algorithm, a POA algorithm, a genetic algorithm, etc.

[0080] The water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price mode provided in this embodiment takes the annual objective function and the daily objective function as the optimization objectives under the constraints of the preset constraint condition set, and uses the historical output data set to construct the medium- and long-term coupled water-wind-solar-storage complementary scheduling model under the time-of-use electricity price mode, comprehensively considering the annual objective function including minimizing the annual power abandonment rate and minimizing the annual source-load difference, and the daily objective function including minimizing the total daily output and maximizing the daily revenue, which can more comprehensively realize the complementary optimization scheduling of the water-wind-solar-storage complementary system. Further, by constructing the medium- and long-term coupled water-wind-solar-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-storage complementary scheduling model, the accuracy of the obtained water-wind-solar-storage target complementary optimization scheduling results is improved. Therefore, by implementing the present invention, under the time-of-use electricity price mode, the advantages of water-wind-solar-storage complementarity are fully utilized, and by comprehensively considering the annual objectives and the daily objectives, on the basis of achieving maximum economic benefits and safe and stable operation of the power grid, the efficiency and accuracy of the water-wind-solar-storage target complementary optimization scheduling are further improved.

[0081] In this embodiment, a water-wind-solar-storage complementary optimization scheduling method under a time-of-use electricity price mode is provided, which can be used for a water-wind-solar-storage complementary system, and the water-wind-solar-storage complementary system is connected to an energy storage system.

[0082] Figure 2 is a flow chart of a method for optimizing the scheduling of water-wind-solar-storage complementarity under a time-of-use electricity price mode according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0083] Step S201, obtain the historical output data set and real-time output data set of the water-wind-solar-storage complementary system. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0084] Step S202, based on the historical output data set, determine the annual target function and the daily target function.

[0085] Specifically, the above step S202 includes:

[0086] Step S2021, based on the historical output data set, calculate the transaction loss data set of the hydro-wind-solar-storage complementary system.

[0087] Among them, the transaction loss data set can include the electricity sales revenue F y With abandonment penalty F ab .

[0088] Specifically, the electricity sales revenue F y It can be calculated by the following relations (13) to (14):

[0089]

[0090]

[0091] Where: q t represents the electricity price at time t under the time-of-use electricity price mechanism, yuan / (kW·h); q val represents the valley electricity price; q peak represents the peak electricity price; q av Indicates the average electricity price.

[0092] Furthermore, the power abandonment penalty F ab It can be calculated by the following relations (15) to (16):

[0093]

[0094] P net =P hwp +P sto (16)

[0095] Where: φ represents the penalty cost coefficient for power abandonment; P net represents the theoretical total output of the water-wind-solar-storage complementary system, MW; P sto Represents energy storage output, MW.

[0096] Step S2022: determining the annual objective function based on the historical output data set.

[0097] Specifically, according to the description of step S102, the annual objective function may include an annual power curtailment rate minimization objective function F1 of the hydro-wind-solar-storage complementary system and an annual source-load difference minimization objective function F2.

[0098] Furthermore, the objective function F1 for minimizing the annual power curtailment rate can be determined by combining the historical output data set, as shown in the following equation (17):

[0099]

[0100] Furthermore, the objective function F2 for minimizing the annual source load difference is expressed as follows:

[0101]

[0102] Step S2023, determining the intraday objective function based on the historical output data set and the transaction loss data set.

[0103] 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.

[0104] 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):

[0105]

[0106] Where: P represents the variance of the total output of hydropower, wind power, solar power and energy storage on the dth day; 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, 10,000 kW.

[0107] 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):

[0108] F4=max(F y -F ab ) (20)

[0109] Step S203, based on the preset constraint condition set, the annual objective function and the daily objective function, and using the historical output data set, a medium- and long-term coupled and short-term water-wind-solar-storage complementary scheduling model under the time-of-use electricity price mode is constructed.

[0110] Specifically, the above step S203 includes:

[0111] Step S2031, based on the preset constraint condition set, the annual objective function and the daily objective function, a medium- and long-term coupled water-wind-solar complementary scheduling model is constructed using the historical output data set.

[0112] Specifically, under the constraints of multiple constraints shown in the above 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 and short-term coupled water-wind-solar complementary scheduling model that satisfies the objective functions shown in the above equations (17) to (20) is obtained.

[0113] In some optional implementations, the above step S2031 includes:

[0114] 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.

[0115] Step a2, based on the preset constraint set and the intraday objective function, using the historical output data set and the medium- and long-term hydro-wind-solar complementary scheduling model, construct a medium- and long-term coupled hydro-wind-solar complementary scheduling model.

[0116] Specifically, under the constraints of multiple constraints shown in the above equations (1) to (12), with the minimization of the annual power abandonment 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 equations (17) and (18) is constructed by training the historical output data set.

[0117] Furthermore, based on the constructed medium- and long-term hydro-wind-solar complementary scheduling model, the historical output data set is continuously used for model training with the optimization objectives of minimizing the intraday total output fluctuation and maximizing the intraday profit, until a medium- and long-term coupled hydro-wind-solar complementary scheduling model that satisfies the objective functions shown in the above equations (19) and (20) is obtained.

[0118] Step S2032, based on the historical output data set, after being processed by the preset calculation logic in the time-of-use electricity price mode, the energy storage output of the energy storage system in different scenarios is obtained.

[0119] 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.

[0120] In some optional implementations, the above step S2032 includes:

[0121] Step b1, obtaining the theoretical total output of water, wind and solar power and the transmission channel capacity in the historical output data set.

[0122] Step b2, based on the time-of-use electricity price model, using the theoretical total output of water, wind 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 water, wind, solar and storage complementary system.

[0123] Step b3, 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, calculate the energy storage output of the energy storage system under different scenarios.

[0124] Among them, the energy storage data set is used to characterize the remaining energy storage space ΔSOC of the energy storage system in different scenarios, as shown in the following equation (21):

[0125] ΔSOC=SOC max -SOC curt (twenty one)

[0126] 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; SCO curt Indicates the current capacity status of the energy storage system.

[0127] Furthermore, the energy storage demand data set 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):

[0128] ΔSOC′=(P hwp -C line )η c

[0129] Specifically, the calculation process may include the following steps:

[0130] 1. When the total output of water, wind and solar power in the complementary system is P hwp At its peak:

[0131] (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:

[0132] a. If ΔSOC>ΔSOC′, then: P act =C line At this time, the energy storage output P sto =P hwp -C line ;

[0133] 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.

[0134] (2) If Phwp Greater than C line , then the energy storage system should play a discharging role, at this time, then: P act =P hwp +P sto At this time, the energy storage output P sto =(SOC curt -SOC min S Wh ) / η d Δt.

[0135] 2. When the total output of water, wind and solar power of the complementary system is P hwp When you are at a low point:

[0136] (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:

[0137] a. If ΔSOC>ΔSOC′, then: P act =C line At this time, the energy storage output P sto =P hwp -C line ;

[0138] 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.

[0139] (2) If P hwp Greater 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 water-wind-solar-storage complementary system reaches the transmission channel capacity C line :

[0140] 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 linr ;

[0141] b. If P hwp +P sto <C line , you can consider time-of-use electricity prices:

[0142] (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);

[0143] (b2) If q t η d peak Then P act =P hwp At this time, the energy storage output P sto =0.

[0144] Step S2033, 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 annual objective function and the daily objective function is constructed using the energy storage output and historical output data set.

[0145] Specifically, based on the constructed water-wind-solar complementary scheduling model, the annual optimization objectives are to minimize the power abandonment rate and the source-load difference within the year, and the daily optimization objectives are to minimize the total output fluctuation within the day 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 water-wind-solar-storage complementary scheduling model that satisfies the objective functions shown in the above equations (17) to (20) is obtained. In the process of model construction, multiple constraints shown in the above equations (1) to (12) need to be satisfied at the same time.

[0146] Step S204: Solve the water-wind-solar-storage complementary scheduling model based on the real-time output data set to obtain the water-wind-solar-storage complementary target optimized scheduling result of the water-wind-solar-storage complementary system. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0147] ​The water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price mode provided in this embodiment calculates the transaction loss data set of the water-wind-solar-storage complementary system through the historical output data set, and then determines the intraday objective function in combination with the historical output data set and the transaction loss data set, taking into account the safe and stable operation of the power grid while taking into account the economic benefits. Further, the annual objective function is determined in combination with the output data set, taking into account the operating characteristics of the water-wind-solar-storage complementary system on a longer time scale. Further, under the constraints of the preset constraint condition set, the historical output data set is used to construct a medium- and long-term coupled water-wind-solar-storage complementary scheduling model that meets the annual objective function and the intraday objective function, which fully considers the operating requirements and restrictions of the water-wind-solar-storage complementary system on different time scales, and can better coordinate the scheduling needs on different time scales. Further, on the basis of the water-wind-solar complementary scheduling model, a water-wind-solar-storage complementary scheduling model that meets the annual objective function and the intraday objective function is constructed in combination with the energy storage output of the energy storage system in different scenarios. By integrating the advantages of water, wind, light and energy storage systems, efficient utilization and complementarity of energy can be achieved, and the reliability and sustainability of the system can be improved. At the same time, by rationally arranging energy storage output, economic benefits can be maximized and system operating costs can be reduced.

[0148] In this embodiment, a water-wind-solar-storage complementary optimization scheduling method under a time-of-use electricity price mode is provided, which can be used for a water-wind-solar-storage complementary system. Figure 3 is a flow chart of a method for optimizing the scheduling of water-wind-solar-storage complementarity under a time-of-use electricity price mode according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0149] Step S301, obtain the historical output data set and real-time output data set of the water-wind-solar-storage complementary system. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0150] 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.

[0151] Step S303: Based on the preset constraint set, annual objective function and daily 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. Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0152] Step S304, 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.

[0153] Specifically, the above step S304 includes:

[0154] 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.

[0155] Among them, the POA (Peafowl Optimization Algorithm) algorithm represents a swarm intelligence optimization algorithm, which mainly includes the following steps:

[0156] (1) Population initialization: First, a certain number of individuals are randomly generated, each of which represents a potential solution to the problem.

[0157] (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.

[0158] (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 excellent individuals.

[0159] (4) Individual update stage: Each individual is updated according to certain rules. The update method may include learning from excellent individuals, random perturbations, etc. This can increase the diversity of the population and prevent the algorithm from falling into a local optimum.

[0160] (5) Repeated iteration: Repeat the fitness evaluation, peacock display, and individual update stages 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.

[0161] Specifically, combined with the annual distribution characteristics of wind and solar resources, the POA algorithm can be used to perform sliding optimization within two time periods based on the original operation mode of the hydropower station, so as to obtain the daily water level and water-wind-solar output process. Furthermore, all time periods throughout the year are traversed until the accuracy requirements are met, and the optimized daily water level and water-wind-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.

[0162] Step S3042, based on the initial complementary optimization scheduling result of water, wind, solar and storage, a genetic algorithm is used to solve the complementary scheduling model of water, wind, solar and storage to obtain the complementary optimization scheduling result of water, wind, solar and storage that meets the intra-day objective function.

[0163] Specifically, a genetic algorithm can be used to solve the daily output process of the objective function of the integrated complementary operation of water, wind and solar. The initial population of the daily output process of the hydropower station is randomly generated, and the corresponding fitness of the individuals is calculated according to the daily objective function. Through reproduction, crossover, mutation and other operations, the daily output distribution process of water, wind and solar is finally obtained to achieve the daily complementarity of water, wind and solar.

[0164] Furthermore, under the time-of-use electricity price mode, the charging and discharging function of the energy storage system is used to smooth the peaks and fill the valleys of the total output curve of water, wind and solar. Furthermore, the genetic algorithm can be used to further optimize the intraday output process of the water, wind, solar and storage integrated complementary operation objective function, and obtain the most economically optimal water, wind, solar and storage complementary optimization scheduling result, that is, the water, wind, solar and storage complementary optimization scheduling result that meets the intraday objective function is obtained.

[0165] 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 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 by combining the POA algorithm and the genetic algorithm to solve the optimization scheduling results of the complementary water-wind-solar-storage system.

[0166] In this embodiment, a water-wind-solar-storage complementary optimization scheduling device under a time-of-use electricity price mode is also provided, which is used to implement the above embodiments and preferred implementation modes, and will not be repeated here. 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, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0167] 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 comprises:

[0168] The acquisition module 401 is used to acquire the historical output data set and the real-time output data set of the water-wind-solar-storage complementary system.

[0169] 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 annual power abandonment 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.

[0170] The construction module 403 is used to construct a medium- and long-term coupled and short-term coupled water-wind-solar-storage complementary scheduling model based on a preset constraint set, an annual objective function and a daily objective function, using a historical output data set.

[0171] 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.

[0172] In some optional implementations, the determining module 402 includes:

[0173] The calculation submodule is used to calculate the transaction loss data set of the hydro-wind-solar-storage complementary system based on the historical output data set.

[0174] The first determination submodule is used to determine the annual objective function based on the historical output data set.

[0175] 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.

[0176] In some optional implementations, the water-wind-solar-storage complementary system is connected to the energy storage system; the building block 403 includes:

[0177] 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 annual objective function and a daily objective function using a historical output data set.

[0178] 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.

[0179] 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 sets.

[0180] In some optional embodiments, the first building block includes:

[0181] 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.

[0182] 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.

[0183] In some optional implementations, the processing submodule includes:

[0184] The acquisition unit is used to obtain the theoretical total output of water, wind and solar power and the capacity of the transmission channel in the historical output data set.

[0185] The determination unit is used to determine the energy storage data set of the energy storage system under 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 using the theoretical total output of water, wind and solar power and the transmission channel capacity.

[0186] The calculation unit is used to calculate the energy storage output of the energy storage system in different scenarios by 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.

[0187] In some optional implementations, the solution module 404 includes:

[0188] 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.

[0189] 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-storage using a genetic algorithm to obtain the water-wind-solar-storage complementary optimization scheduling results that meet the intra-day objective function.

[0190] 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.

[0191] 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.

[0192] The embodiment of the present invention also provides a computer device having the above Figure 4 The water-wind-solar-storage complementary optimization scheduling device under the time-of-use electricity price model is shown.

[0193] See also Figure 5 , Figure 5 is a schematic diagram of the structure 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 are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the 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. Similarly, multiple computer devices can be connected, and each device provides some 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.

[0194] 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 a dedicated 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.

[0195] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0196] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to 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 arranged 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.

[0197] 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.

[0198] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0199] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, 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 hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. 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.

[0200] A part 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 existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and 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 accessible to the computer.

[0201] Although the embodiments of the present invention have been described in conjunction with 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, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price mode, characterized in that: Used in a water-wind-solar-storage complementary system; the method comprises: Obtaining a historical output data set and a real-time output data set of the water-wind-solar-storage complementary system; Based on the historical output data set, determine an annual objective function and a daily objective function, wherein the annual objective function includes an objective function for minimizing the annual power abandonment rate and an objective function for minimizing the annual source-load difference of the water-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 water-wind-solar-storage complementary system; 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 water-wind-solar-storage complementary scheduling model under the time-of-use electricity price mode is constructed; 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.

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: Based on the historical output data set, calculating the transaction loss data set of the hydro-wind-solar-storage complementary system; 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 The water-wind-solar-storage complementary system is connected to the energy storage system; based on the preset constraint condition set, the annual objective function and the daily objective function, and using the historical output data set, a medium- and long-term coupled and short-term coupled water-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 annual objective function and the daily objective function, a medium- and long-term coupled water-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 annual objective function and the daily objective function is constructed using the energy storage output and the historical output data set.

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

5. The method according to claim 3, characterized in that: Based on the historical output data set, after the preset calculation logic processing under the time-of-use electricity price mode, the energy storage output of the energy storage system in different scenarios is obtained, including: Obtain the theoretical total output of water, wind and solar power and the capacity of the transmission channel in the historical output data set; Based on the time-of-use electricity price model, using the theoretical total output of water, wind and solar power 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 water, wind, solar 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.

6. The method according to claim 1, characterized in that 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 water-wind-solar-storage complementary scheduling model is solved using the POA algorithm 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.

7. A water-wind-solar-storage complementary optimization scheduling device under the time-of-use electricity price mode, characterized in that: Used in a water-wind-solar-storage complementary system; the device comprises: An acquisition module, used to acquire a historical output data set and a real-time output data set of the water-wind-solar-storage complementary system; A determination module, configured to determine an annual objective function and an intraday objective function based on the historical output data set, wherein the annual objective function includes an objective function for minimizing the annual power abandonment rate and an objective function for minimizing the annual source-load difference of the hydro-wind-solar-storage complementary system, and the intraday 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, for constructing a medium- and long-term coupled water-wind-solar-storage complementary scheduling model based on a preset constraint set, the annual objective function and the daily objective function, using the historical output data set; A solution module 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.

8. 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 described in any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price model described in any one of claims 1 to 6.

10. A computer program product, characterized in that It includes computer instructions, which are used to enable a computer to execute the water-wind-solar-storage complementary optimization scheduling method under the time-of-use electricity price model described in any one of claims 1 to 6.

Images