Hydroelectric generation simulation method, device and equipment of integrated energy base and medium

Abstract

The invention discloses a hydroelectric generation simulation method, device and equipment of an integrated energy base and a medium, and relates to the technical field of planning design and operation scheduling of the integrated energy base. The output weight of the hydroelectric power plant is determined based on the output fluctuation coefficient and the power abandoning coefficient, and the generating capacity of the hydroelectric power plant is corrected based on the output weight, so that the generating capacity of the hydroelectric power plant can be reduced when the output fluctuation coefficient and the power abandoning coefficient of the wind-solar power plant are relatively large; and when the output fluctuation coefficient and the power abandoning coefficient of the wind-solar power station are small, the generating capacity of the hydroelectric power station is increased. When the generating capacity of the hydroelectric power plant is determined, the output fluctuation coefficient and the power abandoning coefficient of the wind-solar power plant are considered, so that the finally obtained generating capacity of the hydroelectric power plant can be relatively accurate, and the problem that the scheduling of the hydroelectric power plant is not accurate enough can be solved.

Description

Technical Field

[0001] This application relates to the field of integrated energy base planning, design and operation scheduling technology, and in particular to hydropower generation simulation methods, devices, equipment and media for integrated energy bases. Background Technology

[0002] In integrated energy bases that combine hydropower, wind power, and solar power, continuous time-series simulation based on 8760 hours is a crucial step in verifying the joint dispatch performance of hydropower and wind / solar power stations. To reduce the difficulty of solving these continuous time-series simulations, a multi-timescale hierarchical nested simulation method is typically employed. This method involves decomposing complex long-cycle optimization problems into multiple time scales (such as year, month, ten-day period, day, and hour) and refining the calculations layer by layer through hierarchical coupling (upper-level results constrain lower-level inputs).

[0003] Currently, in some technologies, when performing multi-timescale layered nested simulations, the wind and solar power generation over long timescales (such as years to ten-day periods) is averaged, and its volatility and intermittency are flattened. This results in inaccurate scheduling of hydropower stations over long timescales, which in turn leads to problems of insufficient or excessive scheduling capacity for hydropower stations when simulating short timescales (such as ten-day periods to hours). Summary of the Invention

[0004] This application provides a hydropower generation simulation method, a hydropower generation simulation device, an electronic device, a computer-readable storage medium, and a computer program product for an integrated energy base, in order to at least solve the problem of inaccurate scheduling of hydropower stations in related technologies.

[0005] This application provides a method for simulating hydropower generation in an integrated energy base, the method comprising: Obtain simulation reference information, which includes the operating parameters of the hydropower station, the wind and solar power generation sequence obtained with the first duration as the statistical dimension, and the maximum transmission capacity supported by the transmission channel; Using the second duration as a statistical dimension, the wind and solar power generation sequence is statistically analyzed to obtain the power output fluctuation coefficient of the wind and solar power station. The second duration is greater than or equal to the first duration. Using the second duration as the statistical dimension and the maximum transmission capacity supported by the transmission channel as the constraint, the wind and solar power generation sequence is statistically analyzed to obtain the curtailment coefficient of the wind and solar power station. Based on the power output fluctuation coefficient and the curtailment coefficient of the wind and solar power station, the power output weight of the hydropower station is determined. Using the operating parameters of the hydropower station and the maximum power transmission capacity supported by the transmission channel as constraints, and taking the maximum on-grid power generation of the integrated energy base as the target, the power generation of the hydropower station is corrected based on the output weight to obtain the target power generation of the hydropower station, and the operation of the hydropower station is simulated based on the target power generation.

[0006] This application also provides a hydropower generation simulation device for an integrated energy base, comprising: The information acquisition module is used to acquire simulation reference information, which includes the operating parameters of the hydropower station, the wind and solar power generation sequence obtained with the first duration as the statistical dimension, and the maximum transmission capacity supported by the transmission channel. The first coefficient determination module is used to perform statistical analysis on the wind and solar power generation sequence using the second duration as a statistical dimension to obtain the output fluctuation coefficient of the wind and solar power station, wherein the second duration is greater than or equal to the first duration. The second coefficient determination module is used to statistically analyze the wind and solar power generation sequence using the second duration as the statistical dimension and the maximum power transmission capacity supported by the transmission channel as the constraint condition, so as to obtain the curtailment coefficient of the wind and solar power station. The third coefficient determination module is used to determine the output weight of the hydropower station based on the output fluctuation coefficient and the curtailment coefficient of the wind and solar power station. The simulation module is used to take the operating parameters of the hydropower station and the maximum power transmission capacity supported by the transmission channel as constraints, and the maximum on-grid power generation of the integrated energy base as the target, to correct the power generation of the hydropower station based on the output weight, so as to obtain the target power generation of the hydropower station, and to simulate the operation of the hydropower station based on the target power generation.

[0007] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described integrated energy base hydropower generation simulation method.

[0008] This application also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of the above-described integrated energy base hydropower generation simulation method.

[0009] In some embodiments of this application, a second duration is used as a statistical dimension to statistically analyze the output fluctuation coefficient and curtailment coefficient of wind and solar power plants. Based on these coefficients, the output weight of hydropower plants is determined, and the power generation of the hydropower plants is adjusted accordingly. This allows for a reduction in hydropower generation when the output fluctuation coefficient and curtailment coefficient of wind and solar power plants are high, and an increase in hydropower generation when they are low. Because the output fluctuation coefficient and curtailment coefficient of wind and solar power plants are considered when determining the power generation of hydropower plants, the final power generation figure is more accurate, thus solving the problem of inaccurate scheduling of hydropower plants in related technologies. Attached Figure Description

[0010] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating a hydropower generation simulation method provided for some embodiments of this application; Figure 2 A schematic diagram of a multi-timescale hierarchical nested simulation provided for one embodiment of this application; Figure 3 Schematic diagrams of a hydroelectric power generation simulation device provided for some embodiments of this application; Figure 4 A schematic diagram of the modules of an electronic device provided for some embodiments of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0013] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "first," "second," etc., in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0014] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] The output of a power plant refers to the instantaneous power generation of the plant at a specific moment. In a comprehensive energy base integrating hydropower, wind power, and solar power, at least wind and solar power plants and hydropower plants are included, with wind and solar power plants further comprising wind power plants and photovoltaic power plants. The power generation capacity of wind power plants is affected by wind speed, while the power generation capacity of photovoltaic power plants is affected by day and night sunlight. The power generation capacity of hydropower plants is relatively stable and flexible. By rationally scheduling hydropower, wind power, and solar power plants, energy utilization can be maximized, wind and solar energy waste can be reduced, and the stability of the power grid can be improved. For example, when wind speed is high or sunlight is abundant, water can be stored in hydropower plants to reduce their power generation, thus fully utilizing wind and solar power generation and reducing wind and solar power curtailment. Conversely, when wind speed is low or sunlight is insufficient, the power generation of hydropower plants can be increased to supplement wind and solar power generation and ensure the power grid's supply capacity.

[0016] In the design or operation of integrated energy bases, hourly simulations of the base's operation over 8760 hours throughout the year can determine or verify scheduling strategies for wind, solar, and hydropower stations. However, hydropower station operation simulations involve nonlinear constraints such as water level-reservoir capacity curves, discharge flow-tailwater level curves, and head-expected output curves, as well as quadratic functions of head flow to calculate output. This is a typical mixed-integer nonlinear programming problem; if a continuous simulation is performed for 8760 hours, there are at least 8760 variables, making the solution exceptionally difficult. To reduce the difficulty, multi-timescale layered nested simulations are typically employed. For example, on a long timescale, the hydropower station's power generation in each ten-day period can be calculated based on the wind and solar power generation in each ten-day period of the year. Then, on a short timescale, the calculation results from each ten-day period are used as constraints to calculate the hydropower station's hourly power generation within each ten-day period, and the hydropower station is controlled based on the calculated power generation. In this way, the number of variables that need to be solved can be reduced to 36 on a long time scale and to 240 on a short time scale, which can greatly reduce the difficulty of solving the problem.

[0017] Currently, in some technologies, when conducting multi-timescale nested simulations, the wind and solar power generation over long timescales (e.g., year-ten-day periods) is represented as an average, masking its volatility and intermittency. This leads to inaccurate scheduling of hydropower stations over long timescales, and consequently, problems of insufficient or excessive scheduling capacity for hydropower stations in short timescale simulations (e.g., ten-day-hour periods). For example, assuming that over long timescales, the average power generation of wind power stations is 50 kW, 55 kW, ..., 40 kW in each ten-day period, and the average power generation of photovoltaic power stations is 20 kW, 30 kW, ..., 15 kW in each ten-day period, the power generation of hydropower stations in each ten-day period can be calculated based on the average power generation of wind and solar power stations. This average-based scheduling strategy is not accurate enough. For example, a photovoltaic power station may generate 60 kW during the day, but its power generation at night may be close to 0 kW. After averaging the power generation, the average ten-day power generation of the photovoltaic power station could be 30 kW. When a hydroelectric power station is scheduled to operate at 30 kilowatts, the integrated energy base may generate excess power during the day but insufficient power at night.

[0018] In view of this, this application provides a hydropower generation simulation method for integrated energy bases, which can solve the problem of inaccurate scheduling of hydropower stations in related technologies. The hydropower generation simulation method can be applied to electronic devices. These electronic devices may include, but are not limited to, tablet computers, desktop computers, laptop computers, servers, control circuit boards, etc. (See also...) Figure 1 This is a flowchart illustrating a hydropower generation simulation method provided in some embodiments of this application. Figure 1 In this context, the hydropower generation simulation method may include the following steps: Step S101: Obtain simulation reference information, which includes the operating parameters of the hydropower station, the wind and solar power generation sequence obtained with the first duration as the statistical dimension, and the maximum transmission capacity supported by the transmission channel.

[0019] Specifically, the operating parameters of a hydroelectric power station may include the following information: 1) Characteristic parameters of hydropower stations, such as installed capacity, characteristic water levels, minimum discharge flow, and overall power output coefficient. Among them, characteristic water levels can include the reservoir level, dead water level, flood control limit water level, ecological water level, and design flood level of the hydropower station.

[0020] 2) Characteristic curves of hydropower stations, such as water level-reservoir capacity curve, discharge flow-tailwater level curve, head-expected output curve, and water level-maximum discharge flow curve.

[0021] 3) Water balance of hydropower stations and constraints such as water level, flow rate, and output at different times.

[0022] Based on the operating parameters of a hydroelectric power station, it is possible to simulate the power station. Using this simulated hydroelectric power station, the operational status of a comprehensive energy base can be simulated over 8760 hours throughout the year.

[0023] In this embodiment, the first duration is 1 hour. The wind and solar power generation sequence includes the wind power generation sequence and photovoltaic power generation sequence of the integrated energy base in a typical year. The wind power generation sequence includes 8760 wind power generation data points calculated hourly, and the photovoltaic power generation sequence includes 8760 photovoltaic power generation data points calculated hourly. It is understood that in practical applications, the first duration can be determined according to actual needs. For example, the first duration can also be 1 day. The wind power generation sequence includes 365 wind power generation data points calculated daily, and the photovoltaic power generation sequence includes 365 photovoltaic power generation data points calculated daily. This application does not limit the specific value of the first duration.

[0024] Step S102: Using the second duration as the statistical dimension, perform statistics on the wind and solar power generation sequence to obtain the power output fluctuation coefficient of the wind and solar power station. The second duration is greater than or equal to the first duration.

[0025] Specifically, the power output fluctuation coefficient represents the magnitude of fluctuations in wind and solar power generation. For any second time period, the standard deviation and mean of wind and solar power generation within that second time period can be determined, and based on the standard deviation and mean, the power output fluctuation coefficient of the wind and solar power station within that second time period can be determined.

[0026] In this embodiment, the second duration is 10 days. Based on the wind power generation sequence and photovoltaic power generation sequence obtained in step S101, the wind power generation and photovoltaic power generation at corresponding time points can be added together to obtain the wind and solar power generation at the corresponding time points. For example, the wind power generation and photovoltaic power generation in the first hour are added together to obtain the wind and solar power generation in the first hour; the wind power generation and photovoltaic power generation in the second hour are added together to obtain the wind and solar power generation in the second hour.

[0027] Based on the hourly wind and solar power generation, the standard deviation and mean of wind and solar power generation within the t-th ten-day period can be calculated. Based on the standard deviation and mean of wind and solar power generation within the t-th ten-day period, the output fluctuation coefficient of the wind and solar power station within the t-th ten-day period can be determined. Here, t is an integer between 1 and 36 (inclusive). Specifically, the output fluctuation coefficient of the wind and solar power station within the t-th ten-day period can be calculated based on expression (1).

[0028] (1) in, This represents the output fluctuation coefficient of a wind and solar power station within ten days t. This represents the standard deviation of wind and solar power generation within the t-th period. This represents the average wind and solar power generation within the t-th ten-day period. This represents the nth wind and solar power generation within the t-th ten-day period. This represents the amount of wind and solar power generated within the t-th ten-day period, where n is an integer between 1 and T3 (inclusive).

[0029] After normalizing the output fluctuation coefficients for each ten-day period, the output fluctuation coefficient sequence for a typical year can be obtained. .

[0030] Understandably, in practical applications, the second duration can be determined according to actual needs. For example, the second duration could also be one month. This application does not impose any restrictions on the specific value of the second duration.

[0031] Step S103: Using the second duration as the statistical dimension and the maximum transmission capacity supported by the transmission channel as the constraint, the wind and solar power generation sequence is statistically analyzed to obtain the curtailment coefficient of the wind and solar power station.

[0032] Specifically, the curtailment factor includes a characterization of the amount of electricity curtailed by wind and solar power plants. For any second time period, the sum of wind and solar power generation within that second time period can be determined, and based on the sum of wind and solar power generation and the maximum transmission capacity supported by the transmission channel, the curtailment factor of the wind and solar power plant within that second time period can be determined.

[0033] In this embodiment, the curtailment factor of the wind and solar power station in the t-th ten-day period can be calculated based on expression (2).

[0034] (2) in, This represents the curtailment coefficient of wind and solar power stations within the t-th ten-day period. This represents the amount of electricity wasted by wind and solar power plants within the t-th ten-day period. This represents the amount of wind and solar power generated by the wind and solar power station within the t-th ten-day period.

[0035] After normalizing the curtailment coefficients for each ten-day period, the curtailment coefficient sequence for wind and solar power plants in a typical year can be obtained. .

[0036] Step S104: Determine the output weight of the hydropower station based on the output fluctuation coefficient and curtailment coefficient of the wind and solar power station.

[0037] Specifically, the output weight is used to correct the power generation of a hydroelectric power station. The corrected power generation is less than the uncorrected power generation. The larger the output weight, the greater the correction to the power generation. In other words, the larger the output weight, the smaller the corrected power generation.

[0038] The output fluctuation coefficient and curtailment coefficient of wind and solar power plants can be directly proportional to the output weight of hydropower plants. The reasons for this proportionality are explained below.

[0039] 1) A large output fluctuation coefficient indicates that the power generation of wind and solar power plants is relatively high. In this case, the power generation of hydropower plants can be further reduced (i.e., the output weight needs to be greater). This avoids excessive power waste and natural resource waste when the power generation of wind and solar power plants is high, as this would also lead to high power generation of hydropower plants. Of course, it is understandable that when the output fluctuation coefficient is large, the power generation of wind and solar power plants may also be relatively low. When the power generation of wind and solar power plants is low, in order to ensure normal power supply, the power generation of other power plants in the power system (such as thermal power plants) can be controlled. In this way, on the one hand, the normal power supply of the grid can be guaranteed, and on the other hand, natural energy sources such as wind, solar and hydropower can be fully utilized, avoiding the waste of natural energy.

[0040] 2) When the curtailment coefficient is relatively large, it indicates that the power generation of wind and solar power stations is already excessive. In this case, the power generation of hydropower stations can be further reduced (i.e., the output weight needs to be greater). In this way, we can avoid the situation where the power generation of wind and solar power stations is excessive, and the power generation of hydropower stations is also high, thus causing excessive curtailment and waste of natural resources.

[0041] In this embodiment, for any second time period, the output weight of the wind and solar power station within that second time period can be determined based on the output fluctuation coefficient and curtailment coefficient of the wind and solar power station within that second time period. That is, based on the output fluctuation coefficient and curtailment coefficient of the wind and solar power station within ten-day periods t, the output weight of the wind and solar power station within ten-day periods t can be determined. Thus, the output weight sequence of the hydropower station in a typical year can be obtained. .

[0042] Based on the output weight within ten days t, the power generation of the hydroelectric power station within ten days t can be adjusted.

[0043] Furthermore, the output fluctuation coefficient and curtailment coefficient of wind and solar power plants can each have their corresponding weights. These weights characterize the influence of the output fluctuation coefficient and curtailment coefficient of wind and solar power plants on the output weight of hydropower plants. When determining the output weight of a hydropower plant based on the output fluctuation coefficient and curtailment coefficient of wind and solar power plants, a first weight for the output fluctuation coefficient and a second weight for the curtailment coefficient can be obtained. Based on the first and second weights, a weighted fusion calculation can be performed on the output fluctuation coefficient and curtailment coefficient of wind and solar power plants to obtain the output weight.

[0044] Specifically, the output weight of the hydroelectric power station in the t-th ten-day period can be determined based on expression (3). .

[0045] (3) in, Indicates the first weight. Indicates the second weight. .

[0046] Step S105: Using the operating parameters of the hydropower station and the maximum transmission capacity supported by the transmission channel as constraints, and the maximum grid-connected power of the integrated energy base as the target, the power generation of the hydropower station is corrected based on the output weight to obtain the target power generation of the hydropower station, and the operation of the hydropower station is simulated based on the target power generation.

[0047] In this embodiment, the target power generation of the hydroelectric power station within each first time period (i.e., per hour) can be determined through multi-timescale hierarchical nested simulation. (See also...) Figure 2 This is a schematic diagram of a multi-timescale hierarchical nested simulation provided in one embodiment of this application. Figure 2 In this process, multi-timescale hierarchical nested simulations include the following steps: Step S201: The third duration is used as the first scheduling cycle, the second duration is used as the first scheduling period, the maximum on-grid power of the integrated energy base is used as the target, and the operating parameters of the hydropower station and the maximum power transmission capacity supported by the transmission channel are used as constraints. The power generation of the hydropower station is corrected based on the output weight to obtain the target water level of the hydropower station at the beginning and end of each first scheduling period. The third duration is greater than or equal to the second duration.

[0048] Specifically, the third duration is 1 year. Based on the output weights, a first objective function can be constructed as shown in expression (4): (4) in, T1 represents the amount of electricity generated by the integrated energy base during the first dispatch cycle, and T1 represents the number of dispatch periods in the first dispatch cycle. This represents the output weight of the hydroelectric power station during the t-th first scheduling period. This represents the grid-connected power output of the hydroelectric power station during the t-th first dispatch period. This represents the grid-connected power output of the wind power station during the t-th first dispatch period. This represents the grid-connected power output of the photovoltaic power station during the t-th first scheduling period. This represents the curtailment rate of wind and solar power generation stations during the t-th first dispatch period. This represents the duration of the t-th first scheduling period.

[0049] By using the operating parameters of the hydropower station as constraints, the first objective function can be solved to obtain the target water level of the hydropower station at the beginning and end of each first scheduling period. The constraints may include the following expressions (5) to (18).

[0050] (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) In the above expressions (5) to (9), k represents the overall power output coefficient of the hydroelectric power station. This represents the power generation flow rate of the hydroelectric power station during the t-th first scheduling period. This represents the head of water used for power generation at the hydroelectric power station during the t-th first scheduling period. This represents the upstream water level at the beginning of the t-th first scheduling period of the hydroelectric power station. This represents the upstream water level at the end of the t-th first scheduling period of the hydroelectric power station. This represents the average downstream water level of the hydroelectric power station during the t-th first scheduling period. This represents the curve showing the relationship between the discharge flow and the tailrace level of a hydroelectric power station. This represents the water level-reservoir capacity curve of a hydroelectric power station. This represents the discharge flow of the hydroelectric power station during the t-th first scheduling period. This represents the water discharge rate of the hydropower station during the t-th first scheduling period.

[0051] In the above expressions (10)~(11), The minimum power supported by the transmission channel. The maximum power supported by the transmission channel.

[0052] In the above expression (12), This represents the reservoir capacity of the hydroelectric power station during the t-th first scheduling period. This represents the reservoir capacity of the hydroelectric power station during the (t-1)th first scheduling period. This represents the outflow from the reservoir of a hydroelectric power station during the t-th first scheduling period.

[0053] In the above expressions (13)~(14), This indicates the initial water level at the beginning of the first scheduling cycle for the hydroelectric power station. This indicates the final water level at the end of the first scheduling cycle for the hydroelectric power station.

[0054] In the above expression (15), This represents the lower limit of the upstream water level of the hydroelectric power station during the t-th first scheduling period. This represents the upper limit of the water level in front of the dam at the hydroelectric power station during the t-th first scheduling period.

[0055] In the above expression (16), This represents the lower limit of the discharge flow of the hydroelectric power station during the t-th first scheduling period. This represents the upper limit of the discharge flow of the hydroelectric power station during the t-th first scheduling period.

[0056] In the above expression (17), This indicates the permissible range of water level fluctuation in the reservoir of a hydroelectric power station within a single first scheduling period.

[0057] In the above expression (18), This represents the minimum power generation of the hydroelectric power station during the t-th first scheduling period. This represents the maximum power generation of the hydroelectric power station during the t-th first scheduling period.

[0058] Step S202: Using the second duration as the second scheduling cycle, the first duration as the second scheduling period, and the target water level at the beginning and end of each first scheduling period as a constraint, determine the target power generation of the hydropower station in each second scheduling period.

[0059] Specifically, using the target water level at the beginning and end of each of the first scheduling periods of the hydropower station as a constraint, a second objective function as shown in expression (19) can be constructed.

[0060] (19) in, T2 represents the amount of electricity generated by the integrated energy base within one of the second scheduling cycles, and T2 represents the number of second scheduling periods within one of the second scheduling cycles. This represents the output weight of the hydroelectric power station during the m-th second scheduling period. This represents the grid-connected power output of the hydroelectric power station during the m-th second scheduling period. This represents the grid-connected power output of the wind power station during the m-th second scheduling period. This represents the grid-connected power output of the photovoltaic power station during the m-th second scheduling period. This represents the curtailment rate of the wind and solar power station during the m-th second scheduling period. This represents the duration of the m-th second scheduling period.

[0061] Combining the constraints shown in expressions (20) to (33), the second objective function can be solved to obtain the target power generation of the hydropower station in each second scheduling period and the abandoned power of the wind and solar power station in each second scheduling period.

[0062] (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) (27) (28) (29) (30) (31) (32) (33) In the above expressions (20) to (24), k represents the overall power output coefficient of the hydroelectric power station. This represents the power generation flow rate of the hydroelectric power station during the m-th second scheduling period. This represents the head of water used for power generation at the hydroelectric power station during the m-th second scheduling period. This represents the upstream water level at the beginning of the m-th second scheduling period of the hydroelectric power station. This represents the upstream water level at the end of the m-th second scheduling period of the hydroelectric power station. This represents the average downstream water level of the hydroelectric power station during the m-th second scheduling period. This represents the curve showing the relationship between the discharge flow and the tailrace level of a hydroelectric power station. This represents the water level-reservoir capacity curve of a hydroelectric power station. This represents the discharge flow of the hydroelectric power station during the m-th second scheduling period. This represents the water discharge flow rate of the hydroelectric power station during the m-th second scheduling period.

[0063] In the above expressions (25)~(26), The minimum power supported by the transmission channel. The maximum power supported by the transmission channel.

[0064] In the above expression (27), This represents the reservoir capacity of the hydroelectric power station during the m-th second scheduling period. This represents the reservoir capacity of the hydroelectric power station during the (m-1)th second scheduling period. This represents the outflow from the reservoir of a hydroelectric power station during the m-th second scheduling period.

[0065] In the above expressions (28)~(29), This indicates the initial water level at the beginning of the second scheduling cycle for the hydroelectric power station. This indicates the final water level at the end of the second scheduling cycle for the hydroelectric power station.

[0066] In the above expression (30), This represents the lower limit of the upstream water level of the hydroelectric power station during the m-th second scheduling period. This represents the upper limit of the water level in front of the dam at the hydroelectric power station during the m-th second scheduling period.

[0067] In the above expression (31), This represents the lower limit of the discharge flow of the hydroelectric power station during the m-th second scheduling period. This represents the upper limit of the discharge flow of the hydroelectric power station during the m-th second scheduling period.

[0068] In the above expression (32), This indicates the permissible range of water level fluctuation in the reservoir of a hydroelectric power station during a single second scheduling period.

[0069] In the above expression (33), This represents the minimum power generation of the hydroelectric power station during the m-th second scheduling period. This represents the maximum power generation of the hydroelectric power station during the m-th second scheduling period.

[0070] In summary, in the technical solutions of some embodiments of this application, a second duration is used as a statistical dimension to statistically analyze the output fluctuation coefficient and curtailment coefficient of wind and solar power plants. Based on these coefficients, the output weight of hydropower plants is determined, and the power generation of the hydropower plants is corrected accordingly. This allows for a reduction in the power generation of hydropower plants when the output fluctuation coefficient and curtailment coefficient of wind and solar power plants are large, and an increase in their power generation when they are small. Because the output fluctuation coefficient and curtailment coefficient of wind and solar power plants are considered when determining their power generation, the final power generation of the hydropower plants is more accurate, thus solving the problem of inaccurate scheduling of hydropower plants in related technologies.

[0071] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0072] Corresponding to the method, this application also provides a hydropower generation simulation device for an integrated energy base. (See also...) Figure 3 This is a schematic diagram of a hydroelectric power generation simulation device provided in some embodiments of this application. Figure 3 The hydroelectric power generation simulation device includes: The information acquisition module 301 is used to acquire simulation reference information, which includes the operating parameters of the hydropower station, the wind and solar power generation sequence obtained with the first duration as the statistical dimension, and the maximum transmission capacity supported by the transmission channel. The first coefficient determination module 302 is used to statistically analyze the wind and solar power generation sequence using the second duration as the statistical dimension to obtain the output fluctuation coefficient of the wind and solar power station. The second duration is greater than or equal to the first duration. The second coefficient determination module 303 is used to statistically analyze the wind and solar power generation sequence using the second duration as the statistical dimension and the maximum power transmission capacity supported by the transmission channel as the constraint condition, so as to obtain the curtailment coefficient of the wind and solar power station. The third coefficient determination module 304 is used to determine the output weight of hydropower stations based on the output fluctuation coefficient and curtailment coefficient of wind and solar power stations. The simulation module 305 is used to adjust the power generation of the hydropower station based on the power output weight, with the operating parameters of the hydropower station and the maximum power transmission capacity supported by the transmission channel as constraints, and the maximum on-grid power generation of the integrated energy base as the target, to obtain the target power generation of the hydropower station, and to simulate the operation of the hydropower station based on the target power generation.

[0073] See also Figure 4The embodiments of this application also provide an electronic device, including a memory 10 and a processor 20, wherein the memory 10 stores a computer program and the processor 20 is configured to run the computer program to perform the steps in any of the above embodiments of the hydropower generation simulation method for an integrated energy base.

[0074] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the embodiments of the hydropower generation simulation method for an integrated energy base described above when running.

[0075] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0076] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the hydropower generation simulation method for an integrated energy base described above.

[0077] Embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in any of the above embodiments of the integrated energy base hydropower generation simulation method.

[0078] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0079] The foregoing has provided a detailed description of the hydropower generation simulation method, apparatus, equipment, and medium for an integrated energy base provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A method for simulating hydropower generation in an integrated energy base, characterized in that, The method includes: Obtain simulation reference information, which includes the operating parameters of the hydropower station, the wind and solar power generation sequence obtained with the first duration as the statistical dimension, and the maximum transmission capacity supported by the transmission channel; Using the second duration as a statistical dimension, the wind and solar power generation sequence is statistically analyzed to obtain the power output fluctuation coefficient of the wind and solar power station. The second duration is greater than or equal to the first duration. Using the second duration as the statistical dimension and the maximum transmission capacity supported by the transmission channel as the constraint, the wind and solar power generation sequence is statistically analyzed to obtain the curtailment coefficient of the wind and solar power station. Based on the power output fluctuation coefficient and the curtailment coefficient of the wind and solar power station, the power output weight of the hydropower station is determined. Using the operating parameters of the hydropower station and the maximum power transmission capacity supported by the transmission channel as constraints, and taking the maximum on-grid power generation of the integrated energy base as the target, the power generation of the hydropower station is corrected based on the output weight to obtain the target power generation of the hydropower station, and the operation of the hydropower station is simulated based on the target power generation.

2. The method according to claim 1, characterized in that, The process of obtaining the target power generation of the hydroelectric power station includes: The third duration is used as the first scheduling cycle, the second duration is used as the first scheduling period, the maximum on-grid power generation of the integrated energy base is used as the target, and the operating parameters of the hydropower station and the maximum power transmission capacity supported by the transmission channel are used as constraints. The power generation of the hydropower station is corrected based on the output weight to obtain the target water level of the hydropower station at the beginning and end of each first scheduling period. The third duration is greater than or equal to the second duration. Using the second duration as the second scheduling cycle, the first duration as the second scheduling period, and the target water level at the beginning and end of each first scheduling period as a constraint, the target power generation of the hydropower station in each second scheduling period is determined.

3. The method according to claim 2, characterized in that, The wind and solar power station includes a wind power station and a photovoltaic power station; obtaining the target water level of the hydropower station at the beginning and end of each of the first scheduling periods includes: Based on the output weight, a first objective function is constructed as shown below. Using the operating parameters of the hydropower station as constraints, the first objective function is solved to obtain the target water level of the hydropower station at the beginning and end of each of the first scheduling periods: 1 in, , , , , , T1 represents the amount of electricity generated by the integrated energy base during the first scheduling cycle, and T1 represents the number of the first scheduling periods within the first scheduling cycle. This represents the output weight of the hydroelectric power station during the t-th first scheduling period. This represents the grid-connected power output of the hydroelectric power station during the t-th first scheduling period. This represents the grid-connected power output of the wind power station during the t-th first scheduling period. This represents the grid-connected power output of the photovoltaic power station during the t-th first scheduling period. This represents the curtailment rate of the wind and solar power station during the t-th first scheduling period. Let represent the duration of the t-th first scheduling period, and k represent the overall output coefficient of the hydropower station. This represents the power generation flow rate of the hydroelectric power station during the t-th first scheduling period. This represents the head of the hydroelectric power station during the t-th first scheduling period. This represents the upstream water level at the beginning of the t-th first scheduling period of the hydroelectric power station. This represents the upstream water level at the end of the t-th first scheduling period of the hydroelectric power station. This represents the average downstream water level of the hydroelectric power station during the t-th first scheduling period. This represents the curve showing the relationship between the discharge flow and the tailrace level of the hydroelectric power station. This represents the water level-reservoir capacity relationship curve of the hydroelectric power station. This represents the discharge flow of the hydroelectric power station at the end of the t-th first scheduling period. This represents the flow rate of the hydroelectric power station at the end of the t-th first scheduling period.

4. The method according to claim 2, characterized in that, The wind and solar power station includes a wind power station and a photovoltaic power station; determining the target power generation of the hydropower station in each of the second scheduling periods includes: Using the target water level at the beginning and end of each of the first scheduling periods as constraints, a second objective function is constructed as shown below. Solving the second objective function yields the target power generation of the hydroelectric power station within each of the second scheduling periods: in, , , , , , T2 represents the amount of electricity generated by the integrated energy base within one of the second scheduling cycles, and T2 represents the number of second scheduling periods within one of the second scheduling cycles. This represents the output weight of the hydroelectric power station during the m-th second scheduling period. This represents the grid-connected power output of the hydroelectric power station during the m-th second scheduling period. This represents the grid-connected power output of the wind power station during the m-th second scheduling period. This represents the grid-connected power output of the photovoltaic power station during the m-th second scheduling period. This represents the curtailment rate of the wind and solar power station during the m-th second scheduling period. Let m represent the duration of the m-th second scheduling period, and k represent the overall output coefficient of the hydropower station. This represents the power generation flow rate of the hydroelectric power station during the m-th second scheduling period. This represents the generating head of the hydroelectric power station during the m-th second scheduling period. This represents the upstream water level at the beginning of the m-th second scheduling period of the hydroelectric power station. This represents the upstream water level at the end of the m-th second scheduling period of the hydroelectric power station. This represents the average downstream water level of the hydroelectric power station during the m-th second scheduling period. This represents the curve showing the relationship between the discharge flow and the tailrace level of the hydroelectric power station. This represents the water level-reservoir capacity relationship curve of the hydroelectric power station. This represents the discharge flow rate of the hydroelectric power station at the end of the m-th second scheduling period. This represents the flow rate of the hydroelectric power station at the end of the m-th second scheduling period.

5. The method according to any one of claims 1 to 4, characterized in that, The method of using the second duration as a statistical dimension to statistically analyze the wind and solar power generation sequence to obtain the power output fluctuation coefficient of the wind and solar power station includes: For any second duration, determine the standard deviation and mean of wind and solar power generation within the second duration, and based on the standard deviation and the mean, determine the output fluctuation coefficient of the wind and solar power station within the corresponding second duration.

6. The method according to any one of claims 1 to 4, characterized in that, The method of using the second duration as a statistical dimension and the maximum transmission capacity supported by the transmission channel as a constraint to statistically analyze the wind and solar power generation sequence and obtain the curtailment coefficient of the wind and solar power station includes: For any second duration, the sum of wind and solar power generation within the second duration is determined, and based on the sum of wind and solar power generation and the maximum transmission capacity supported by the transmission channel, the curtailment factor of the wind and solar power station within the corresponding second duration is determined.

7. The method according to any one of claims 1 to 4, characterized in that, The determination of the output weight of the hydropower station based on the output fluctuation coefficient and the curtailment coefficient of the wind and solar power station includes: Obtain the first weight of the power output fluctuation coefficient and the second weight of the power curtailment coefficient; Based on the first weight and the second weight, the power output fluctuation coefficient and the curtailment coefficient of the wind and solar power station are weighted and fused to obtain the power output weight.

8. A hydropower generation simulation device for an integrated energy base, characterized in that, The device includes: The information acquisition module is used to acquire simulation reference information, which includes the operating parameters of the hydropower station, the wind and solar power generation sequence obtained with the first duration as the statistical dimension, and the maximum transmission capacity supported by the transmission channel. The first coefficient determination module is used to perform statistical analysis on the wind and solar power generation sequence using the second duration as a statistical dimension to obtain the output fluctuation coefficient of the wind and solar power station, wherein the second duration is greater than or equal to the first duration. The second coefficient determination module is used to statistically analyze the wind and solar power generation sequence using the second duration as the statistical dimension and the maximum power transmission capacity supported by the transmission channel as the constraint condition, so as to obtain the curtailment coefficient of the wind and solar power station. The third coefficient determination module is used to determine the output weight of the hydropower station based on the output fluctuation coefficient and the curtailment coefficient of the wind and solar power station. The simulation module is used to take the operating parameters of the hydropower station and the maximum power transmission capacity supported by the transmission channel as constraints, and the maximum on-grid power generation of the integrated energy base as the target, to correct the power generation of the hydropower station based on the output weight, so as to obtain the target power generation of the hydropower station, and to simulate the operation of the hydropower station based on the target power generation.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the hydropower generation simulation method of the integrated energy base as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the hydropower generation simulation method for an integrated energy base as described in any one of claims 1 to 7.

Images