Control method and device of water-light complementary power generation system

Abstract

The invention provides a control method and device for a water-light complementary power generation system, and relates to the technical field of hydropower and new energy dispatching management. The method comprises the following steps: evaluating an occurrence possibility grade and a consequence severity grade of an electricity abandoning risk based on hydropower output prediction data and photovoltaic output prediction data in a future preset time period; according to the occurrence possibility grade, the consequence severity grade and the risk matrix, determining an electricity abandoning risk score; and controlling hydropower output in a future preset time period based on the power abandonment risk score and a hydropower output adjustment strategy. According to the invention, the electricity abandoning risk in the preset time period can be accurately evaluated, and the corresponding hydropower output adjustment strategy is provided based on the electricity abandoning risk evaluation result to control the hydropower output. The electricity abandoning risk is reduced by improving hydropower output, and energy waste is reduced. The assessment of the electricity abandoning risk and the hydropower output adjustment strategy can also provide quantitative reference for scheduling personnel, and the scientificity and economy of photoelectric and hydropower bundled delivery are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of hydropower and new energy dispatching and management, and in particular to a control method and device for a hydro-photovoltaic complementary power generation system. Background Art

[0002] With the rapid development of the new energy industry, the construction and investment of integrated clean energy bases in river basins are rapidly evolving. New energy sources such as wind power and photovoltaics are being integrated into river basin hydropower and bundled with it for transmission. Leveraging the reservoir storage capacity of hydropower stations and the rapid and flexible regulation of hydroelectric units, these bases can respond to unstable and intermittent renewable energy output, increasing clean energy consumption while delivering smooth, stable, and high-quality electricity.

[0003] From a planning and design perspective, in most scenarios, integrating PV power into a bundled hydropower system can theoretically achieve a 1:1 ratio or even greater between PV and hydropower installed capacity. However, in practice, due to various factors, even when PV installed capacity is significantly lower than that of bundled hydropower, the risk of power curtailment has already emerged in some areas and at certain times of the year, with power supply exceeding the transmission capacity, resulting in an inability to export excess power. This problem will only become more severe as more renewable energy sources are integrated into and operated in conjunction with hydropower systems in the future. Summary of the Invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, a first embodiment of the present disclosure provides a control method for a hydro-photovoltaic complementary power generation system, wherein the hydro-photovoltaic complementary power generation system includes: a hydropower station and a photovoltaic power station, wherein the hydropower station and the photovoltaic power station share the same transmission channel, and the method includes:

[0006] Obtaining daily hydropower output forecast data of the hydropower station and daily photovoltaic output forecast data of the photovoltaic power station within a preset time period in the future;

[0007] Determining an average channel utilization rate for the future preset time period and a first channel utilization rate for the first day based on the hydropower output forecast data and the photovoltaic output forecast data;

[0008] Obtaining a first probability level of the risk of power curtailment within the future preset time period based on the first channel utilization rate and a probability criterion, wherein the probability criterion includes first channel utilization rate intervals corresponding to a plurality of probability levels;

[0009] Obtaining a first consequence severity level of the power curtailment risk within the future preset time period based on the average channel utilization and a consequence criterion, wherein the consequence criterion includes second channel utilization intervals corresponding to a plurality of consequence severity levels;

[0010] Mapping the first occurrence probability level and the first consequence severity level into a semi-quantitative risk matrix to determine a first power curtailment risk score; the semi-quantitative risk matrix includes a mapping relationship between the occurrence probability level, the consequence severity level, and the power curtailment risk score;

[0011] determining, according to a hydropower output regulation strategy, a first hydropower output increment corresponding to the first power abandonment risk score, wherein the power abandonment risk score and the hydropower output increment in the hydropower output regulation strategy are positively correlated;

[0012] The hydropower output of the hydropower station within the future preset time period is controlled according to the first hydropower output increment.

[0013] In some embodiments of the present disclosure, the semi-quantitative risk matrix is predetermined by the following steps: determining a possibility score corresponding to each of the occurrence possibility levels and a severity score corresponding to each of the consequence severity levels; constructing the semi-quantitative risk matrix based on the multiple occurrence possibility levels and the multiple consequence severity levels, the rows of the semi-quantitative risk matrix are the multiple consequence severity levels, the columns of the semi-quantitative risk matrix are the multiple occurrence possibility levels, and the elements in the semi-quantitative risk matrix are the products of the corresponding possibility scores and the severity scores.

[0014] In some embodiments of the present disclosure, obtaining daily hydropower output forecast data of the hydropower station within a preset future time period includes: determining multiple key node target water levels of the hydropower station within the preset future time period; calculating the monthly scale control water level of the hydropower station based on the multiple key node target water levels; determining the hydropower daily control strategy with the monthly scale control water level as the scheduling boundary; obtaining daily basin water inflow forecast data and the starting water level within the preset future time period; and determining the hydropower output forecast data based on the basin water inflow forecast data, the starting water level and the hydropower daily control strategy.

[0015] In some embodiments of the present disclosure, the first channel utilization rate is obtained by the following steps: adding the hydropower output forecast data and the photovoltaic output forecast data of the first day to obtain the total output value of the first day; and determining the ratio of the total output value of the first day to the maximum transmission capacity of the transmission channel as the first channel utilization rate.

[0016] A second aspect of the present disclosure provides a control device for a hydro-photovoltaic complementary power generation system, wherein the hydro-photovoltaic complementary power generation system includes a hydropower station and a photovoltaic power station, wherein the hydropower station and the photovoltaic power station share the same transmission channel, and the device includes:

[0017] An acquisition module, configured to acquire daily hydropower output forecast data of the hydropower station and daily photovoltaic output forecast data of the photovoltaic station within a preset time period in the future;

[0018] a first determining module, configured to determine an average channel utilization rate for the future preset time period and a first channel utilization rate on the first day based on the hydropower output forecast data and the photovoltaic output forecast data;

[0019] a second determining module, configured to obtain a first probability level of the risk of power curtailment within the future preset time period based on the first channel utilization rate and a probability criterion, wherein the probability criterion includes first channel utilization rate intervals corresponding to a plurality of probability levels;

[0020] a third determining module, configured to obtain a first consequence severity level of the power curtailment risk within the future preset time period based on the average channel utilization and a consequence criterion, wherein the consequence criterion includes second channel utilization intervals corresponding to a plurality of consequence severity levels;

[0021] a fourth determination module, configured to map the first occurrence likelihood level and the first consequence severity level into a semi-quantitative risk matrix to determine a first power curtailment risk score; the semi-quantitative risk matrix including a mapping relationship between the occurrence likelihood level, the consequence severity level, and the power curtailment risk score;

[0022] a fifth determining module, configured to determine, based on a hydropower output regulation strategy, a first hydropower output increment corresponding to the first power curtailment risk score, wherein the power curtailment risk score and the hydropower output increment in the hydropower output regulation strategy are positively correlated;

[0023] A control module is configured to control the hydropower output of the hydropower station within the future preset time period according to the first hydropower output increment.

[0024] In some embodiments of the present disclosure, the device also includes a construction module; wherein the framework module is used to: determine the possibility score corresponding to each of the occurrence possibility levels and the severity score corresponding to each of the consequence severity levels; construct the semi-quantitative risk matrix based on the multiple occurrence possibility levels and the multiple consequence severity levels, the rows of the semi-quantitative risk matrix are the multiple consequence severity levels, the columns of the semi-quantitative risk matrix are the multiple occurrence possibility levels, and the elements in the semi-quantitative risk matrix are the products of the corresponding possibility scores and the severity scores.

[0025] In some embodiments of the present disclosure, the acquisition module is specifically used to: determine the target water levels of multiple key nodes of the hydropower station within the future preset time period; calculate the monthly scale control water level of the hydropower station based on the target water levels of the multiple key nodes; determine the daily hydropower control strategy with the monthly scale control water level as the scheduling boundary; obtain the daily basin water inflow forecast data and the starting water level within the future preset time period; determine the hydropower output forecast data based on the basin water inflow forecast data, the starting water level and the hydropower daily control strategy.

[0026] In some embodiments of the present disclosure, the first channel utilization rate is obtained by the following steps: adding the hydropower output forecast data and the photovoltaic output forecast data of the first day to obtain the total output value of the first day; and determining the ratio of the total output value of the first day to the maximum transmission capacity of the transmission channel as the first channel utilization rate.

[0027] A third embodiment of the present disclosure provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0028] The memory stores computer-executable instructions;

[0029] The processor executes the computer-executable instructions stored in the memory to implement the method described in the first aspect.

[0030] The fourth aspect of the present disclosure provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-executable instructions, which are used to implement the method described in the first aspect when executed by a processor.

[0031] The control method of the hydro-photovoltaic complementary power generation system provided by the present disclosure can accurately assess the risk of power abandonment within a preset time period, and provide a corresponding hydropower output regulation strategy based on the power abandonment risk assessment result to control the hydropower output. By increasing the hydropower output, the risk of power abandonment is reduced, energy waste is reduced, clean energy consumption is promoted, and the comprehensive benefits of the cascade hydro-photovoltaic complementary system in the basin are improved. The assessment of power abandonment risk and the hydropower output regulation strategy can also provide a quantitative reference for dispatchers. The present disclosure can improve the scientificity and economy of connecting photovoltaic power sources to hydropower and bundling them with hydropower for transmission, and provide technical support for the construction and operation of future integrated water, wind, solar and storage clean energy bases in the basin.

[0032] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0034] Figure 1 A schematic flow chart of a control method for a water-photovoltaic complementary power generation system provided by an embodiment of the present disclosure;

[0035] Figure 2 A schematic diagram of a semi-quantitative risk matrix provided by an embodiment of the present disclosure;

[0036] Figure 3 A schematic diagram of the probability levels of occurrence of some dates within the inspection period provided by an embodiment of the present disclosure;

[0037] Figure 4 A schematic diagram of a control device for a water-photovoltaic complementary power generation system provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0038] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0039] Specifically, the following describes a control method and apparatus for a hydro-photovoltaic complementary power generation system according to an embodiment of the present disclosure with reference to the accompanying drawings. The hydro-photovoltaic complementary power generation system includes a hydropower station and a photovoltaic power station, which share a common transmission channel and transmit hydropower and photovoltaic power in bundles.

[0040] Figure 1 This is a flow chart of a control method for a water-photovoltaic complementary power generation system provided by an embodiment of the present disclosure. Figure 1 As shown, the control method of the water-photovoltaic complementary power generation system may include the following steps:

[0041] Step 101: Obtain daily hydropower output forecast data of a hydropower station and daily photovoltaic output forecast data of a photovoltaic power station within a preset time period in the future.

[0042] In some embodiments of the present disclosure, the temporal patterns of hydropower and photovoltaic output can be summarized based on historical hydropower and photovoltaic output data, and then the hydropower and photovoltaic output within a preset time period in the future can be predicted, resulting in daily hydropower and photovoltaic output forecast data. Alternatively, a deep learning model can be trained using historical hydropower and photovoltaic output data, respectively, to learn the mapping relationship between time and hydropower and photovoltaic output, and to predict hydropower and photovoltaic output within a preset time period in the future. In addition, hydropower and photovoltaic output forecast data can also be obtained based on meteorological forecast data within a preset time period in the future.

[0043] In one possible implementation, daily hydropower output forecast data can also be obtained based on the overall framework of the hydropower station's year-round water level control and the principle of rolling nested optimization scheduling. As an example, multiple key node target water levels for the hydropower station within a preset future time period are determined. It should be noted that these key point target water levels are the target water levels of the controlling reservoir in the hydropower station. Taking the upper reservoir of the hydropower station as an example, four key point target water levels can be given for the upper reservoir: at the beginning of the year, before the flood season, at the end of the flood season, and at the end of the year. Based on these multiple key node target water levels, the hydropower station's monthly control water level is calculated. Using the monthly control water level as the scheduling boundary, scheduling constraints are imposed on short-term hydropower calculations, and a daily hydropower control strategy (used to represent the hydropower station's daily scheduling plan) is determined. Daily basin water inflow forecast data and start-up water levels within a preset future time period are obtained. Based on the basin water inflow forecast data, start-up water levels, and the daily hydropower control strategy, hydropower output forecast data is determined. That is to say, based on the daily hydropower dispatching plan, the sum of the hydropower output generated by the daily inflow of water in the basin and the hydropower output caused by the water level changes in the future preset time period is predicted, provided that the hydropower station meets the target water level constraints of key nodes.

[0044] Step 102 : Determine the average channel utilization rate for a future preset time period and the first channel utilization rate for the first day based on the hydropower output forecast data and the photovoltaic output forecast data.

[0045] The channel utilization rate is the ratio of the total output power to the maximum output capacity of the channel. When the total output power is greater than the maximum output capacity of the channel, it means that power abandonment has occurred and there is excess power that cannot be output. In some embodiments of the present disclosure, the hydropower output forecast data and photovoltaic output forecast data of the first day can be added and calculated to obtain the total output value of the first day. The ratio of the total output value of the first day to the maximum transmission capacity of the sending channel is determined as the first channel utilization rate. The average channel utilization rate of the future preset time period can be obtained in the same way as mentioned above, and the channel utilization rate of each day in the future preset time period is calculated separately to obtain the average channel utilization rate in the future preset time period.

[0046] Step 103 : obtaining a first occurrence possibility level of the power curtailment risk within a future preset time period based on the first channel utilization and a possibility criterion, wherein the possibility criterion includes first channel utilization intervals corresponding to a plurality of occurrence possibility levels.

[0047] The possibility criterion is used to determine the possibility of power abandonment risk within a preset time period in the future based on the first channel utilization rate on the first day. As an example, multiple risk probability levels can be divided, and corresponding first channel utilization intervals are defined for each probability level based on empirical data. For example, the possibility criterion can be defined as four probability levels: level 4, level 3, level 2, and level 1, which correspond to the first channel utilization intervals: η>100%, 100%≥η>95%, 95%≥η>90%, 90%≥η>85% (η is the first channel utilization rate). Identify the "signals" of possible power abandonment risk based on the first channel utilization rate and the possibility criterion.

[0048] Step 104 : obtaining a first consequence severity level of the power curtailment risk within a future preset time period based on the average channel utilization and a consequence criterion, wherein the consequence criterion includes a second channel utilization interval corresponding to each of a plurality of consequence severity levels.

[0049] The consequence criterion is used to determine the severity of the consequences of the power abandonment risk in the future time period based on the average channel utilization in the time period. The higher the severity level of the consequence, the more electricity cannot be sent out through the channel. As an example, multiple severity levels of the consequence can be divided, and the corresponding second channel utilization interval is defined for each severity level of the consequence based on empirical data. For example, the consequence criterion can be defined as four severity levels of consequence, namely level 4, level 3, level 2, and level 1, which correspond to the second channel utilization intervals respectively: θ>90%, 90%≥θ>80%, 80%≥θ>70%, 70%≥θ>60% (θ is the average channel utilization).

[0050] Step 105: Map the first occurrence probability level and the first consequence severity level into a semi-quantitative risk matrix to determine a first curtailment risk score. The semi-quantitative risk matrix includes a mapping relationship between the occurrence probability level, the consequence severity level, and the curtailment risk score.

[0051] It should be noted that the semi-quantitative risk matrix is pre-constructed to provide a semi-quantitative description of the likelihood of occurrence and severity of consequences of power abandonment risk. In some embodiments of the present disclosure, the likelihood score corresponding to each likelihood level and the severity score corresponding to each severity level of consequences can be determined. In this embodiment, the level of the likelihood level can be used as the corresponding likelihood score, and the level of the severity level of consequences can be used as the corresponding severity score. A semi-quantitative risk matrix is constructed based on multiple likelihood levels and multiple severity levels of consequences. The rows of the semi-quantitative risk matrix are multiple severity levels of consequences, and the columns of the semi-quantitative risk matrix are multiple likelihood levels of occurrence. The elements in the semi-quantitative risk matrix are the products of the corresponding likelihood scores and the severity scores.

[0052] Figure 2 This is a schematic diagram of a semi-quantitative risk matrix provided by an embodiment of the present disclosure. Figure 2 As shown in FIG, for a level 4 likelihood of occurrence, the corresponding likelihood score is 4. In the case of a level 2 likelihood of occurrence and a level 3 consequence severity level, the corresponding severity score is 6.

[0053] Step 106 : Determine a first hydropower output increment corresponding to the first power abandonment risk score according to the hydropower output regulation strategy. The power abandonment risk score and the hydropower output increment in the hydropower output regulation strategy are positively correlated.

[0054] In some embodiments of the present disclosure, after a semi-quantitative assessment of the risk of power abandonment within a preset time period in the future, a corresponding strategy can be adopted to debug hydropower, aiming to reduce the power abandonment risk score within the preset time period in a rolling manner, promote the consumption of clean energy, and reduce the waste of electricity. It should be noted that the hydropower output increment is the increment within the preset time period. There is a positive correlation between the power abandonment risk score and the hydropower output increment in the hydropower output regulation strategy, indicating that the greater the power abandonment risk score, the greater the hydropower output increment, so as to reduce the power abandonment risk score in the later period, reduce or even eliminate the future power abandonment risk, and reduce energy waste.

[0055] by Figure 2Taking the semi-quantitative risk matrix shown in the figure as an example, multiple risk levels can be classified based on the curtailment risk score. Red risk is defined as R ≥ 16, indicating that the risk is unavoidable; orange risk is defined as R ≥ 8, indicating that the risk is difficult to avoid; yellow risk is defined as R ≥ 3, indicating that there is a certain risk; and blue risk is defined as R ≥ 1, indicating that the risk is possible but the probability is low. R represents the curtailment risk score. According to the collinear complementarity model, for red-level risks, curtailment of transmission channels is considered unavoidable. While available channel space can be promptly increased or even fully utilized to reduce curtailment losses within a preset time period. For orange-level risks, although the risk is unavoidable, appropriate measures can be taken to mitigate it. Hydropower output should be increased promptly to leave as much room for adjustment as possible during the dispatch period. For yellow-level risks, similar measures are taken as for orange-level risks, but the increase in hydropower output is lower than for orange-level risks, allowing for appropriate adjustment space for subsequent periods. For blue-level risks, the hydropower treatment increment can be set to 0, meaning no increase in hydropower output, and attention should be paid to potential escalation.

[0056] Step 107 : controlling the hydropower output of the hydropower station in a future preset time period according to the first hydropower output increment.

[0057] Select the past N years as the test period, obtain daily simulated hydropower and photovoltaic output data for N years, calculate daily channel utilization, and determine the daily occurrence probability level based on the daily channel utilization and probability criteria. Figure 3 This is a schematic diagram of the probability levels of occurrence of some dates within the inspection period provided by the embodiment of the present disclosure. The darker the color, the higher the probability level.

[0058] Based on the daily probability of occurrence, a curtailment risk with a probability of 3 is determined to occur on May 15th of a certain year. Taking May 15th as the starting point, the average channel utilization rate for the scheduling period after May 15th is calculated to be 93.5%, with a consequence severity level of 4. Based on the semi-quantitative risk matrix, the curtailment risk score for the period from May 15th to May 31st is determined to be 12, indicating an orange-level risk. Based on the hydropower output regulation strategy, the hydropower output increase corresponding to a curtailment risk score of 12 is determined, so that the average channel utilization rate from May 15th to May 21st approaches 99%. This allows the average channel utilization rate from May 22nd to May 31st to drop to 90%, and the curtailment risk score drops from 12 to 9. This demonstrates that controlling hydropower output based on the curtailment risk score can reduce the curtailment risk level and minimize curtailment.

[0059] By implementing the embodiments of the present disclosure, the risk of power abandonment within a preset time period can be accurately assessed, and a corresponding hydropower output regulation strategy can be provided based on the results of the power abandonment risk assessment to control the hydropower output. By increasing the hydropower output, the risk of power abandonment can be reduced, energy waste can be reduced, clean energy consumption can be promoted, and the overall benefits of the cascade hydro-photovoltaic complementary system in the basin can be improved. The assessment of power abandonment risk and the hydropower output regulation strategy can also provide a quantitative reference for dispatchers. The present disclosure can improve the scientificity and economy of connecting photovoltaic power sources to hydropower and bundling them with hydropower for transmission, and provide technical support for the construction and operation of future integrated water, wind, solar, and storage clean energy bases in the basin.

[0060] Figure 4 This is a schematic diagram of a control device for a water-photovoltaic complementary power generation system provided by an embodiment of the present disclosure. Figure 4 As shown, the control device of the hydro-photovoltaic complementary power generation system includes: an acquisition module 401 , a first determination module 402 , a second determination module 403 , a third determination module 404 , a fourth determination module 405 , a fifth determination module 406 and a control module 407 .

[0061] The acquisition module 401 is used to acquire the daily hydropower output forecast data of the hydropower station and the daily photovoltaic output forecast data of the photovoltaic power station within a preset time period in the future.

[0062] The first determination module 402 is configured to determine an average channel utilization rate for a future preset time period and a first channel utilization rate for a first day based on the hydropower output forecast data and the photovoltaic output forecast data.

[0063] The second determining module 403 is configured to obtain a first occurrence probability level of the power curtailment risk within a future preset time period based on the first channel utilization and a probability criterion, wherein the probability criterion includes first channel utilization intervals corresponding to a plurality of occurrence probability levels.

[0064] The third determination module 404 is configured to obtain a first consequence severity level of the power curtailment risk within a future preset time period based on the average channel utilization and a consequence criterion, wherein the consequence criterion includes a second channel utilization interval corresponding to each of a plurality of consequence severity levels.

[0065] The fourth determination module 405 is configured to map the first occurrence likelihood level and the first consequence severity level into a semi-quantitative risk matrix to determine a first power curtailment risk score. The semi-quantitative risk matrix includes a mapping relationship between the occurrence likelihood level, the consequence severity level, and the power curtailment risk score.

[0066] The fifth determining module 406 is configured to determine a first hydropower output increment corresponding to the first power curtailment risk score according to the hydropower output regulation strategy, wherein the power curtailment risk score and the hydropower output increment in the hydropower output regulation strategy are positively correlated.

[0067] The control module 407 is configured to control the hydropower output of the hydropower station within a future preset time period according to the first hydropower output increment.

[0068] In some embodiments of the present disclosure, Figure 4 Based on the illustrated embodiment, the control device for the hydro-photovoltaic hybrid power generation system may further include a construction module. The construction module is configured to: determine a probability score corresponding to each occurrence probability level and a severity score corresponding to each consequence severity level; and construct a semi-quantitative risk matrix based on the multiple occurrence probability levels and the multiple consequence severity levels, wherein the rows of the semi-quantitative risk matrix represent the multiple consequence severity levels, the columns of the semi-quantitative risk matrix represent the multiple occurrence probability levels, and the elements in the semi-quantitative risk matrix are the products of the corresponding probability scores and the severity scores.

[0069] In some embodiments of the present disclosure, the acquisition module 401 is specifically used to: determine the target water levels of multiple key nodes of the hydropower station within a preset time period in the future; calculate the monthly scale control water level of the hydropower station based on the target water levels of multiple key nodes; determine the daily hydropower control strategy with the monthly scale control water level as the scheduling boundary; obtain the daily basin water inflow forecast data and the starting water level within a preset time period in the future; determine the hydropower output forecast data based on the basin water inflow forecast data, the starting water level and the daily hydropower control strategy.

[0070] In some embodiments of the present disclosure, the first channel utilization rate is obtained by the following steps: adding the hydropower output forecast data and photovoltaic output forecast data of the first day to obtain the total output value of the first day; and determining the ratio of the total output value of the first day to the maximum transmission capacity of the transmission channel as the first channel utilization rate.

[0071] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0072] In order to implement the above embodiments, the present disclosure also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0073] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0074] In order to implement the above embodiments, the present disclosure further provides a computer program product, including a computer program, which implements the methods provided in the above embodiments when executed by a processor.

[0075] In the descriptions of the aforementioned embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0076] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0077] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0078] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0079] It should be understood that various parts of the present disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0080] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0081] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0082] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. A person of ordinary skill in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A control method for a water-photovoltaic complementary power generation system, the water-photovoltaic complementary power generation system comprising: A hydropower station and a photovoltaic power station, wherein the hydropower station and the photovoltaic power station share the same delivery channel, are characterized in that the method comprises the following steps: Obtaining daily hydropower output forecast data of the hydropower station and daily photovoltaic output forecast data of the photovoltaic power station within a preset time period in the future; Determining an average channel utilization rate for the future preset time period and a first channel utilization rate for the first day based on the hydropower output forecast data and the photovoltaic output forecast data; Obtaining a first probability level of the risk of power curtailment within the future preset time period based on the first channel utilization rate and a probability criterion, wherein the probability criterion includes first channel utilization rate intervals corresponding to a plurality of probability levels; Obtaining a first consequence severity level of the power curtailment risk within the future preset time period based on the average channel utilization and a consequence criterion, wherein the consequence criterion includes second channel utilization intervals corresponding to a plurality of consequence severity levels; Mapping the first occurrence probability level and the first consequence severity level into a semi-quantitative risk matrix to determine a first power curtailment risk score; the semi-quantitative risk matrix includes a mapping relationship between the occurrence probability level, the consequence severity level, and the power curtailment risk score; determining, according to a hydropower output regulation strategy, a first hydropower output increment corresponding to the first power abandonment risk score, wherein the power abandonment risk score and the hydropower output increment in the hydropower output regulation strategy are positively correlated; The hydropower output of the hydropower station within the future preset time period is controlled according to the first hydropower output increment.

2. The method according to claim 1, wherein The semi-quantitative risk matrix is predetermined by the following steps: Determining a likelihood score corresponding to each of the occurrence likelihood levels and a severity score corresponding to each of the consequence severity levels; The semi-quantitative risk matrix is constructed based on the multiple occurrence probability levels and the multiple consequence severity levels, the rows of the semi-quantitative risk matrix are the multiple consequence severity levels, the columns of the semi-quantitative risk matrix are the multiple occurrence probability levels, and the elements in the semi-quantitative risk matrix are the products of the corresponding probability scores and the severity scores.

3. The method according to claim 1, wherein The obtaining of daily hydropower output forecast data of the hydropower station within a preset future time period includes: Determining target water levels of multiple key nodes of the hydropower station within the future preset time period; Calculating the monthly scale control water level of the hydropower station according to the target water levels of the multiple key nodes; Determine the daily hydropower control strategy based on the monthly scale controlled water level as the dispatching boundary; Obtaining daily water inflow forecast data and water level adjustment for the future preset time period; The hydropower output forecast data is determined based on the basin water inflow forecast data, the start water level and the hydropower daily control strategy.

4. The method according to any one of claims 1 to 3, wherein The first channel utilization rate is obtained by the following steps: Adding and calculating the hydropower output forecast data and the photovoltaic output forecast data for the first day to obtain a total output value for the first day; The ratio of the total output value on the first day to the maximum transmission capacity of the sending channel is determined as the first channel utilization rate.

5. A control device for a water-photovoltaic complementary power generation system, the water-photovoltaic complementary power generation system comprising: A hydropower station and a photovoltaic power station, wherein the hydropower station and the photovoltaic power station share the same delivery channel, wherein the device comprises: An acquisition module, configured to acquire daily hydropower output forecast data of the hydropower station and daily photovoltaic output forecast data of the photovoltaic station within a preset time period in the future; a first determining module, configured to determine an average channel utilization rate for the future preset time period and a first channel utilization rate on the first day based on the hydropower output forecast data and the photovoltaic output forecast data; a second determining module, configured to obtain a first probability level of the risk of power curtailment within the future preset time period based on the first channel utilization rate and a probability criterion, wherein the probability criterion includes first channel utilization rate intervals corresponding to a plurality of probability levels; a third determining module, configured to obtain a first consequence severity level of the power curtailment risk within the future preset time period based on the average channel utilization and a consequence criterion, wherein the consequence criterion includes second channel utilization intervals corresponding to a plurality of consequence severity levels; a fourth determination module, configured to map the first occurrence likelihood level and the first consequence severity level into a semi-quantitative risk matrix to determine a first power curtailment risk score; the semi-quantitative risk matrix including a mapping relationship between the occurrence likelihood level, the consequence severity level, and the power curtailment risk score; a fifth determining module, configured to determine, based on a hydropower output regulation strategy, a first hydropower output increment corresponding to the first power curtailment risk score, wherein the power curtailment risk score and the hydropower output increment in the hydropower output regulation strategy are positively correlated; A control module is configured to control the hydropower output of the hydropower station within the future preset time period according to the first hydropower output increment.

6. The device according to claim 5, characterized in that The device further comprises a building module; wherein the building module is configured to: Determining a likelihood score corresponding to each of the occurrence likelihood levels and a severity score corresponding to each of the consequence severity levels; The semi-quantitative risk matrix is constructed based on the multiple occurrence probability levels and the multiple consequence severity levels, the rows of the semi-quantitative risk matrix are the multiple consequence severity levels, the columns of the semi-quantitative risk matrix are the multiple occurrence probability levels, and the elements in the semi-quantitative risk matrix are the products of the corresponding probability scores and the severity scores.

7. The device according to claim 5, characterized in that The acquisition module is specifically used for: Determining target water levels of multiple key nodes of the hydropower station within the future preset time period; Calculating the monthly scale control water level of the hydropower station according to the target water levels of the multiple key nodes; Determine the daily hydropower control strategy based on the monthly scale controlled water level as the dispatching boundary; Obtaining daily water inflow forecast data and water level adjustment for the future preset time period; The hydropower output forecast data is determined based on the basin water inflow forecast data, the start water level and the hydropower daily control strategy.

8. The device according to any one of claims 5 to 7, characterized in that The first channel utilization rate is obtained by the following steps: Adding and calculating the hydropower output forecast data and the photovoltaic output forecast data for the first day to obtain a total output value for the first day; The ratio of the total output value on the first day to the maximum transmission capacity of the sending channel is determined as the first channel utilization rate.

9. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 4 when executed by a processor.

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