Cascade reservoir optimal water level determination method and system based on medium and long term power supply guarantee rate

Abstract

The invention discloses a method and system for determining the optimal water level of a cascade reservoir based on a medium and long term power supply guarantee rate, and the method comprises the steps: obtaining historical incoming water data, a load curve of a typical day of each month, a total interval flow and an end water storage discrete value of a representative month in a dry flood season, and an initial water level of each reservoir, and calculating a plurality of interval flow scenes; a slack variable is introduced, and a medium and long term optimization scheduling model containing load balance constraints is constructed; discretizing the initial total interval flow and the initial total water storage of the cascade reservoir, and dividing the initial total interval flow and the initial total water storage into 20 hydrological states; inputting the multiple interval flow scenes into the medium and long term optimization scheduling model, and carrying out medium and long term optimization scheduling to obtain a medium and long term optimization scheduling result; and obtaining a supply conservation rate matrix result under each hydrological state through a supply conservation rate algorithm, and determining the optimal water level range of the reservoir according to the matrix result. According to the method, medium-and-long-term power supply is guaranteed in actual engineering production, and meanwhile, the reservoir level with higher implementability and comprehensiveness is accurately determined.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower optimization scheduling, and in particular to a method and system for determining the optimal water level of cascade reservoirs based on a medium- and long-term power supply guarantee rate. Background Art

[0002] Hydropower is the clean energy with the most mature development technology and the largest development scale in the world. As the global climate situation becomes increasingly severe, clean resources such as hydropower, wind power, and photovoltaics need to increase their share of power generation and gradually realize the clean transformation of the power system. my country has many rivers and lakes and is one of the countries with the largest water resources reserves in the world. The terrain difference is large, which is conducive to large-scale hydropower development. From the initial development of hydropower in the middle of the last century to the present, relying on my country's 13 major hydropower energy bases, my country has vigorously promoted the planning and construction of hydropower projects in the upper reaches of the Yangtze River, Jinsha River, and Lancang River basins, and built and put into operation a large number of cascade reservoirs to achieve the overall development of the basin cascade. Compared with a single reservoir, the joint dispatching and operation of the cascade reservoir group in the basin has excellent storage capacity regulation and compensation capabilities. The rational use of optimized dispatching technology can achieve efficient management of water resources, use limited water resources to give full play to the comprehensive benefits of flood control and power generation in the basin as a whole, and enhance the comprehensive utilization value of each cascade reservoir. At the same time, the optimal dispatching of hydropower stations needs to consider the joint operation mode of the cascade of the river basin, adjust the water storage and release process of each reservoir, use the flexibility of hydropower units, improve the safety and stability of the power system, and fully enhance the social and economic benefits. Reservoir dispatching can be divided into medium- and long-term dispatching and short-term dispatching according to different time scales. The medium- and long-term dispatching of reservoirs mainly focuses on the optimal operation mode of long-term scales such as months, seasons, years and years. Due to the large optimization scale, under the premise of seasonal differences in water inflow, medium- and long-term dispatching needs to comprehensively consider the power generation and flood control benefits in different periods. With the reform of the power system and the in-depth development of the power market, the short-term dispatching of reservoirs has gradually changed from traditional unified dispatching to dispatching by the results of spot market clearing. In essence, it has changed from planned resource optimization to market resource optimization. Both traditional short-term dispatching and spot market clearing are resource allocation on a short-term time scale. Resource allocation on a long-term time scale has added the dimension of time compared to the short-term scale, such as the reservoir storing water at the end of the flood season for power generation in the dry season. Regarding the connection between medium- and long-term scheduling and short-term scheduling, short-term scheduling of reservoirs usually uses the results of medium- and long-term scheduling as boundary conditions for resource allocation, usually with water level as the boundary. However, using water level as the boundary essentially still reflects the planned optimization of resource allocation. This connection method between different time scales is too single and rigid, which is not conducive to the development of market-oriented reform of the power system. How to optimize the utilization of hydropower resources and optimize the allocation of power resources in the context of the power market is the main direction of continuous research. How to ensure medium- and long-term power supply in actual engineering production while proposing a more feasible and comprehensive method for determining reservoir water levels is the main problem that the hydropower scheduling department needs to solve urgently. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate to solve the problem of inaccurate reservoir water level determination.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for determining the optimal water level of cascade reservoirs based on a medium- and long-term power supply guarantee rate, comprising:

[0008] Obtain historical water inflow data, load curves for typical days of each month, total interval flow and final water storage discrete values ​​for representative months during the dry and flood seasons, and initial water levels for each reservoir, and calculate multiple interval flow scenarios;

[0009] Based on the acquired and calculated data, slack variables are introduced to build a medium- and long-term optimal scheduling model with load balance constraints;

[0010] Based on the constructed medium- and long-term optimal dispatching model, the initial total interval flow and initial total water storage of the cascade reservoirs are discretized and divided into 20 hydrological states respectively;

[0011] Based on the hydrological status, various interval flow scenarios are input into the medium- and long-term optimization scheduling model to perform medium- and long-term optimization scheduling and obtain medium- and long-term optimization scheduling results;

[0012] According to the dispatching results, the supply guarantee rate matrix results under each hydrological state are obtained through the supply guarantee rate algorithm, and the optimal water level range of the reservoir is determined according to the matrix results.

[0013] As a preferred solution of the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate described in the present invention, wherein:

[0014] The medium- and long-term optimization scheduling model includes an objective function and constraints, where the objective function is expressed as:

[0015]

[0016] Among them, NR is the total number of reservoirs, M is the number of months, T is the number of time periods in a day, F is the guaranteed output, and P is the total number of reservoirs. jm The output of reservoir j in m months, Extra output for a typical day in month m, SL mt is the load balance relaxation during period t on a typical day in month m, W 1 , W 2 , W 3 , W 4 is the weight value, W 4 >>W 1 >>W 2 , W 3 .

[0017] As a preferred solution of the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate described in the present invention, wherein:

[0018] The constraints include water balance constraints, upper and lower limit constraints of water storage and outflow, equal constraints of initial and final water storage, output expression constraints and typical day load balance constraints;

[0019] The water balance constraint is expressed as:

[0020]

[0021] Among them, V jm is the water storage capacity of reservoir j at the beginning of month, is the inflow of reservoir j in month m, is the outflow of reservoir j in month m, Δm is the length of the period m, is the flow rate of reservoir j in the m-month interval, and Ω(J) is the set of connected reservoirs upstream of reservoir j.

[0022] As a preferred solution of the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate described in the present invention, wherein:

[0023] The upper and lower limit constraints of water storage and outflow flow are expressed as:

[0024]

[0025] Among them, V j min (·) is the minimum water storage capacity of reservoir j; is the minimum water level; V j max (·) is the maximum water storage capacity of reservoir j; is the maximum water level; are the minimum and maximum outflows of reservoir j respectively;

[0026] The initial and final water storage equality constraint is expressed as:

[0027] V j,0 =V j,M

[0028] Among them, Vj,0 represents the initial water storage capacity of reservoir j in the first month, V j,M It represents the final water storage capacity of reservoir j in the last month.

[0029] As a preferred solution of the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate described in the present invention, wherein:

[0030] The output expression constraint is expressed as:

[0031]

[0032] in, is the kth set of convex hull parameters of reservoir j whose output is limited by the outflow flow, is the kth set of convex hull parameters of reservoir j whose output is limited by the maximum power generation flow, is the average water storage capacity of reservoir j in month m.

[0033] As a preferred solution of the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate described in the present invention, wherein:

[0034] The typical day load balance constraint is expressed as:

[0035]

[0036] in, is the total output of cascade hydropower stations on a typical day, D mt is the typical daily load curve in month m, SL mt is the slack variable for period t on a typical day in the mth month, is the extra output that can be stably provided every hour on a typical day in month m, P jm This is the output of the J reservoir in m months.

[0037] As a preferred solution of the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate described in the present invention, wherein:

[0038] The supply guarantee rate algorithm includes the following steps:

[0039] Initialization: Set s, y, and n to 0, where s represents the current hydrological state number, y represents the interval flow scenario number, and n represents the number of years to meet the power supply demand;

[0040] The total interval flow and initial total water storage of the cascade reservoirs are discretized into NS hydrological states, and the total number of hydrological states NS = 20 × 20;

[0041] Perform medium- and long-term optimization scheduling for each interval traffic scenario, assuming that the number of interval traffic scenarios NY = 50;

[0042] Determine whether y has reached the total number of interval traffic scenarios NY requirement;

[0043] If y does not meet the total number of interval traffic scenarios NY requirement, then determine whether the slack variable SL in the typical day load balance constraint is equal to 0;

[0044] If SL is equal to 0, it means that the power supply is guaranteed under the current interval traffic scenario, and the number of years n to meet the power supply demand is increased by 1;

[0045] If SL is not equal to 0, n remains unchanged, and the interval traffic scenario y is switched to calculate whether each scenario meets the supply guarantee demand;

[0046] If y reaches the total number of interval flow scenarios NY, then calculate the supply guarantee rate F = n / NY under the hydrological state s, and continue to determine whether the current hydrological state number s is greater than or equal to the total number of hydrological states;

[0047] If the current hydrological state number s is less than the total number of hydrological states NS, then return to the medium- and long-term optimization scheduling solution;

[0048] If the current hydrological state number s is greater than or equal to the total number of hydrological states NS, the calculation is terminated and the supply guarantee rate matrix result is output.

[0049] In a second aspect, the present invention provides a system for determining the optimal water level of cascade reservoirs based on a medium- and long-term power supply guarantee rate, comprising:

[0050] The acquisition module is used to obtain historical water inflow data, load curves of typical days of each month, total interval flow and final water storage discrete values ​​of representative months in the dry and flood seasons, and the initial water level of each reservoir, and calculate various interval flow scenarios;

[0051] A construction module is used to introduce slack variables based on the acquired and calculated data to construct a medium- and long-term optimization scheduling model with load balancing constraints;

[0052] The division module is used to discretize the initial total interval flow and initial total water storage of the cascade reservoirs based on the constructed medium- and long-term optimization scheduling model, and divide each into 20 hydrological states;

[0053] An input module is used to input various interval flow scenarios into the medium- and long-term optimization scheduling model based on the hydrological state, perform medium- and long-term optimization scheduling, and obtain medium- and long-term optimization scheduling results;

[0054] The determination module is used to obtain the supply rate matrix results under each hydrological state through the supply rate algorithm based on the scheduling results, and determine the optimal water level range of the reservoir according to the matrix results.

[0055] In a third aspect, the present invention provides a computing device, comprising:

[0056] Memory, used to store programs;

[0057] A processor is used to execute the computer executable instructions, which, when executed by the processor, implement the steps of the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate.

[0058] In a fourth aspect, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate are implemented.

[0059] Beneficial effects of the present invention: This method adds a load balance constraint on typical days of each month on the basis of the traditional monthly-scale reservoir optimization scheduling, so that the water distribution is more in line with the changes in load demand. The concept of supply guarantee rate is proposed, that is, the probability that the average output of the reservoir on a medium- and long-term scale meets the load on a medium- and long-term scale, and it is believed that the supply guarantee rate is related to the water storage capacity in the reservoir and the water inflow of the reservoir. Then, a slack variable is added to the load balance constraint to determine whether the load demand is met. The supply guarantee rate is statistically calculated by simulating a variety of water inflow scenarios, and the functional relationship between the supply guarantee rate and the two variables of water storage capacity and water inflow (interval flow) is expressed by discrete point fitting. Finally, due to the one-to-one correspondence between water storage capacity and water level, the optimal water level corridor that can meet the supply guarantee rate under a certain water inflow or a certain water inflow range can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0061] Figure 1 A basic flow chart of a method for determining the optimal water level of cascade reservoirs based on a medium- and long-term power supply guarantee rate provided by an embodiment of the present invention;

[0062] Figure 2 A flow chart of a power supply guarantee rate algorithm for a method for determining an optimal water level of cascade reservoirs based on a medium- and long-term power supply guarantee rate provided in one embodiment of the present invention;

[0063] Figure 3 A reservoir topology relationship diagram of a method for determining the optimal water level of cascade reservoirs based on a medium- and long-term power supply guarantee rate provided by an embodiment of the present invention;

[0064] Figure 4A method for determining the optimal water level of cascade reservoirs based on a medium- and long-term power supply guarantee rate is provided as an embodiment of the present invention, which includes historical data of total interval flow in a certain year and a scaled total interval flow curve graph. DETAILED DESCRIPTION

[0065] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0066] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0067] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0068] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0069] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0070] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0071] Example 1

[0072] Reference Figure 1-2 , which is an embodiment of the present invention, provides a method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate, such as Figure 1 As shown, the following steps are included:

[0073] S1: Obtain historical water inflow data, load curves for typical days of each month, total interval flow and final water storage discrete values ​​for representative months during the dry and flood seasons, and initial water levels for each reservoir, and calculate multiple interval flow scenarios;

[0074] It should be noted that using the load curve of a typical day of each month instead of just the load curve of a typical day of the month during the dry and flood seasons can make the model more comprehensive and accurate in reflecting the changes in electricity demand, thus helping to formulate more effective dispatching strategies, ensure the stable operation of the power system and the efficient use of water resources. Using the load curve of a typical day of each month can more comprehensively reflect the characteristics of electricity demand in different months of the year. This can better capture seasonal changes in the model, including the difference in demand during the dry and flood seasons.

[0075] Specifically, how to obtain the load curve of a typical day of each month includes the following steps:

[0076] S1.1: Collect historical load data

[0077] Obtain historical load data from the power system operation records, which are usually collected by grid companies or electricity market operators, including hourly or more granular (such as every 15 minutes) load data.

[0078] S1.2: Data preprocessing

[0079] The collected historical load data are cleaned to remove outliers and missing data, and if necessary, the data are standardized to make them comparable on the same scale.

[0080] S1.3: Seasonality Analysis

[0081] Use time series analysis methods (such as STL decomposition) to identify trends, seasonality, and random components in the data and identify typical load patterns in different seasons (such as dry and wet seasons).

[0082] S1.4: Select a typical day

[0083] Use cluster analysis methods (such as K-means clustering) to group similar load curves in order to identify typical load patterns, and select the most representative day from each cluster as a typical day. This day should be able to well reflect the average load behavior of the season.

[0084] S1.5: Construct a typical daily load curve

[0085] Use the selected typical days to construct load curves, which should cover all periods of the day and reflect the load changes on a typical day. Smooth the constructed typical day load curves to remove short-term fluctuations and retain the main trends and patterns.

[0086] S1.6: Verification and Adjustment

[0087] By comparing the typical day load curve with historical data, verify whether the selected typical day can accurately reflect the load characteristics of the season. If necessary, fine-tune the typical day to better match the actual situation.

[0088] It should be noted that the selection of representative months for the dry and flood seasons is usually based on the results of statistical analysis of historical water inflow data, which can help identify the typical characteristics of water inflow in different seasons. By analyzing the water inflow data over the years, it is possible to find out which months have the lowest and highest water inflows throughout the year, and these months are usually selected as representative months for the dry and flood seasons. The representative months for the dry and flood seasons may also vary from region to region. For example, in some regions, spring may be the dry season, while in other regions, winter may be the dry season.

[0089] For example, assuming that the dry season in a certain area is from November to March of the following year, and the flood season is from July to September, then the representative month of the dry season may be December, because it is one of the months with the least water inflow during the dry season; the representative month of the flood season may be August, because it is one of the months with the most water inflow during the flood season.

[0090] S2: Based on the acquired and calculated data, slack variables are introduced to build a medium- and long-term optimal scheduling model with load balance constraints;

[0091] In the embodiment of the present application, the medium- and long-term optimization scheduling model includes an objective function and constraints;

[0092] The medium- and long-term optimal scheduling is to make full use of the water energy efficiency, with the goal of ensuring the maximum output, total output and additional output beyond the load. In order to measure whether the supply is guaranteed, a slack variable is added to the typical daily load balance, and the slack variable is optimized to be minimized, where the objective function is expressed as:

[0093]

[0094] Among them, NR is the total number of reservoirs, M is the number of months, T is the number of time periods in a day, F is the guaranteed output, and P is the total number of reservoirs. jm The output of reservoir j in m months, Extra output for a typical day in month m, SL mt is the load balance relaxation during period t on a typical day in month m, W 1 , W 2 , W 3 , W 4 is the weight value, W 4 >>W 1 >>W 2 , W 3 .

[0095] In the embodiment of the present application, the constraints include water balance constraints, upper and lower limit constraints of water storage and outflow flow, equal constraints of initial and final water storage, output expression constraints and typical day load balance constraints;

[0096] In the embodiment of the present application, the water balance constraint is expressed as:

[0097]

[0098] Among them, V jm is the water storage capacity of reservoir j at the beginning of month, is the inflow of reservoir j in month m, is the outflow of reservoir j in month m, Δm is the length of the period m, is the flow rate of reservoir j in the m-month interval, and Ω(J) is the set of connected reservoirs upstream of reservoir j.

[0099] In the embodiment of the present application, the upper and lower limit constraints of water storage and outflow flow are expressed as:

[0100]

[0101] Among them, V j min (·) is the minimum water storage capacity of reservoir j; is the minimum water level; V j max (·) is the maximum water storage capacity of reservoir j; is the maximum water level; are the minimum and maximum outflows of reservoir j respectively;

[0102] In the embodiment of the present application, the initial and final water storage equality constraint is expressed as:

[0103] V j,0 =V j,M

[0104] Among them, V j,0 represents the initial water storage capacity of reservoir j in the first month, V j,M It represents the final water storage capacity of reservoir j in the last month.

[0105] In the embodiment of the present application, the output expression constraint is expressed as:

[0106]

[0107] The reservoir output is linearly calculated using the convex hull plane method, where: is the kth set of convex hull parameters of reservoir j whose output is limited by the outflow flow, is the kth set of convex hull parameters of reservoir j whose output is limited by the maximum power generation flow, is the average water storage capacity of reservoir j in month m.

[0108] In the embodiment of the present application, the typical daily load balance constraint is expressed as:

[0109]

[0110] in, is the total output of cascade hydropower stations on a typical day, D mt is the typical daily load curve in month m, SL mt is the slack variable for period t on a typical day in month m. mt When it is equal to 0, it means that the power supply is satisfied. mt When it is greater than 0, it means that the power supply is not met. is the extra output that can be stably provided every hour on a typical day in month m, P jm This is the output of the J reservoir in m months.

[0111] S3: Based on the constructed medium- and long-term optimal dispatching model, the initial total interval flow and initial total water storage of the cascade reservoirs are discretized and divided into 20 hydrological states respectively;

[0112] S4: Based on the hydrological status, various interval flow scenarios are input into the medium- and long-term optimization scheduling model to perform medium- and long-term optimization scheduling and obtain the medium- and long-term optimization scheduling results;

[0113] S5: Based on the dispatching results, the supply guarantee rate matrix results under each hydrological state are obtained through the supply guarantee rate algorithm, and the optimal water level range of the reservoir is determined according to the matrix results.

[0114] In the embodiment of the present application, the supply guarantee rate algorithm is as follows: Figure 2 As shown, the following steps are included:

[0115] Initialization: Set s, y, and n to 0, where s represents the current hydrological state number, y represents the interval flow scenario number, and n represents the number of years to meet the power supply demand;

[0116] The total interval flow and initial total water storage of the cascade reservoirs are discretized into NS hydrological states, and the total number of hydrological states NS = 20 × 20;

[0117] Perform medium- and long-term optimization scheduling for each interval traffic scenario, assuming that the number of interval traffic scenarios NY = 50;

[0118] Determine whether y has reached the total number of interval traffic scenarios NY requirement;

[0119] If y does not meet the total number of interval traffic scenarios NY requirement, then determine whether the slack variables SL in the typical day load balance constraint are all equal to 0;

[0120] If SL is equal to 0, it means that the power supply is guaranteed under the current interval traffic scenario, and the number of years n to meet the power supply demand is increased by 1;

[0121] If SL is not equal to 0, n remains unchanged, and the interval traffic scenario y is switched to calculate whether each scenario meets the supply guarantee demand;

[0122] If y reaches the total number of interval flow scenarios NY, then calculate the supply guarantee rate F = n / NY under the hydrological state s, and continue to determine whether the current hydrological state number s is greater than or equal to the total number of hydrological states;

[0123] If the current hydrological state number s is less than the total number of hydrological states NS, then return to the medium- and long-term optimization scheduling solution;

[0124] If the current hydrological state number s is greater than or equal to the total number of hydrological states NS, the calculation is terminated and the supply guarantee rate matrix result is output.

[0125] In the embodiment of the present application, the supply guarantee rate algorithm further includes the following steps:

[0126] S5.1: Discretize the total interval flow Q and the initial total water storage V of the cascade reservoirs in the first month of the dispatching period into 20 hydrological states, V = V 1 ,V 2 ,…,V k ,…,V 20 , Q=Q 1 ,Q 2 ,…,Q m ,…,Q 20 , hydrological state refers to (V k ,Q m ), V = V k ,Q=Q m; Take a combination of discrete values ​​for each of them. When calculating the supply guarantee rate matrix of month m, the scheduling period is from month m to month m-1 of the second year; prepare 50 years of historical interval flow data, and select a typical day every month for 12 months to prepare the 24-hour load curve data of that day.

[0127] S5.2: For each hydrological state, each water inflow scenario is independently optimized and dispatched in the medium and long term. For each medium and long term optimized dispatch of cascade reservoirs, the total interval flow in the first month of the dispatch period is set as the discrete value of the total interval flow of the cascade reservoirs under the hydrological state. The discrete value of the total interval flow of the hydrological state is allocated to each reservoir according to the proportion of the interval flow of each reservoir in the first month in the historical interval flow data of that year. According to the ratio of the discrete value of the total interval flow to the actual total interval flow in history, the interval flow of other months is scaled to the discrete value level. The discrete value of the hydrological state of the initial total water storage of the cascade reservoirs is used as the total water storage in the first month of the dispatch period.

[0128] S5.3: In the 50 independent medium- and long-term optimization scheduling models under each hydrological state, a typical daily load balance constraint related to power supply is added. For the medium- and long-term optimization scheduling independently performed for each water inflow scenario, if the scheduling result shows that the load balance is met, the power supply demand is met that year, otherwise the power supply demand is not met; the power supply rate under this hydrological state is the ratio of the number of years that meet the power supply demand to the total number of years, 50.

[0129] S5.4: Switch to the next hydrological state and continue with S5.2 and S5.3 until the supply guarantee rate of each hydrological state is calculated.

[0130] This embodiment also provides a system for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate, including:

[0131] The acquisition module is used to obtain historical water inflow data, load curves of typical days in the representative month of the dry and flood season, total interval flow and final water storage discrete values ​​in the representative month of the dry and flood season, and the initial water level of each reservoir, and calculate various interval flow scenarios;

[0132] A construction module is used to introduce slack variables based on the acquired and calculated data to construct a medium- and long-term optimization scheduling model with load balancing constraints;

[0133] The division module is used to discretize the initial total interval flow and initial total water storage of the cascade reservoirs based on the constructed medium- and long-term optimization scheduling model, and divide each into 20 hydrological states;

[0134] An input module is used to input various interval flow scenarios into the medium- and long-term optimization scheduling model based on the hydrological state, perform medium- and long-term optimization scheduling, and obtain medium- and long-term optimization scheduling results;

[0135] The determination module is used to obtain the supply rate matrix results under each hydrological state through the supply rate algorithm based on the scheduling results, and determine the optimal water level range of the reservoir according to the matrix results.

[0136] Furthermore, it also includes:

[0137] Memory, used to store programs;

[0138] A processor is used to load the program to execute the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate.

[0139] This embodiment also provides a computer-readable storage medium storing a program, which, when executed by a processor, implements the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate.

[0140] The storage medium proposed in this embodiment and the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0141] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0142] Example 2

[0143] Reference Figure 3-4 Tables 1-4 are an embodiment of the present invention, which provides a method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate. In order to verify its beneficial effects, scientific demonstration is carried out through specific implementation methods and implementation effects.

[0144] The present embodiment is as follows:

[0145] 39 major cascade hydropower stations in the Lancang River Basin, Jinsha River Basin, Red River Basin, Irrawaddy River Basin and Pearl River Basin in Yunnan Province were selected. The reservoir topological relationship is as follows: Figure 3 As shown. The historical multi-year average load curve is selected as the typical daily load curve. According to the water inflow, a year can be divided into dry season and flood season. Yunnan Province usually has a flood season from June to October, and a dry season from November to May of the following year. February and August are selected as representatives to discuss the scheduling of dry season and flood season. The model input data includes the historical 50-year water inflow, the load curve of typical days of each month, the total interval flow and the final water storage discrete value of the representative month (February and August), and the initial water level of each reservoir.

[0146] Data preparation:

[0147] Table 1 Discrete value table of hydrological status in dry season

[0148]

[0149] Table 2 Discrete value table of hydrological state in flood season

[0150]

[0151]

[0152] As shown in Tables 1 and 2, the total initial water storage of all reservoirs participating in the dispatching is discretized into 20 values ​​at equal intervals from the minimum to the maximum value; the natural water inflow data uses 50 years of historical data, and the discrete interval flow hydrological state is used as the total interval flow of the first month. It is proportionally distributed to each hydropower station according to the first month data in the historical data, and the interval flow data of the remaining months are scaled according to the ratio of the discrete interval flow hydrological state to the total water inflow of the first month in history, as shown in Figure 4 When calculating the supply guarantee rate matrix for month m, the scheduling period is from month m to month m-1 of the second year.

[0153] like Figure 4 As shown in the figure, the optimized scheduling calculation uses scaled interval flow data, and the ratio of the values ​​of the two curves per month is the same, which is the scaling ratio. The total interval flow of the scaled curve in January is the discrete value of the hydrological state, and the total interval flow of the remaining months can be determined based on the known historical total interval flow curve. Then, based on the ratio of the interval flow of each power station to the total interval flow and the total interval flow curve after discretization, the interval flow of each power station after discretization is determined.

[0154] Calculation results of supply guarantee rate:

[0155] Table 3 Matrix table of supply guarantee rate results in the dry season in February

[0156]

[0157]

[0158] Table 4 Matrix table of supply guarantee rate results in August flood season

[0159]

[0160] As shown in Table 3 and Table 4, the supply guarantee rate increases with the increase of water inflow, whether in the dry season or the flood season, and increases first and then decreases with the increase of the total initial water storage of the cascade. The reason why the supply guarantee rate decreases with the increase of the total initial water storage of the cascade is that the model sets the equal constraint of initial and final water storage. If the initial water storage increases, the final water storage will also increase. The supply guarantee rate calculated by this model only guarantees the power supply within the next year, and the final water storage represents the supply guarantee for a longer period of time after one year. Therefore, the increase in the final water storage will cause the reservoir to give up power generation within one year in order to store water, that is, the supply guarantee rate calculated by this model decreases. Table 3 shows that the best water storage hydrological state in February is the 12th and 13th hydrological state. Under these two water storage hydrological states, no matter how much water is inflow, the maximum supply guarantee rate under the same water inflow hydrological state will be achieved. Therefore, the optimal water level range is between the water levels corresponding to the two water storage amounts. Table 4 shows that the best water storage hydrological conditions in August are the 9th and 10th hydrological conditions. Under these two water storage hydrological conditions, no matter how much water is flowing, the maximum supply rate under the same water flow hydrological condition will be achieved. Therefore, the optimal water level range is between the water levels corresponding to the two water storage capacities.

[0161] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate, characterized in that: include: Obtain historical water inflow data, load curves for typical days of each month, total interval flow and final water storage discrete values ​​for representative months during the dry and flood seasons, and initial water levels for each reservoir, and calculate multiple interval flow scenarios; Based on the acquired and calculated data, slack variables are introduced to build a medium- and long-term optimal scheduling model with load balance constraints; Based on the constructed medium- and long-term optimal dispatching model, the initial total interval flow and initial total water storage of the cascade reservoirs are discretized and divided into 20 hydrological states respectively; Based on the hydrological status, various interval flow scenarios are input into the medium- and long-term optimization scheduling model to perform medium- and long-term optimization scheduling and obtain medium- and long-term optimization scheduling results; According to the dispatching results, the supply guarantee rate matrix results under each hydrological state are obtained through the supply guarantee rate algorithm, and the optimal water level range of the reservoir is determined according to the matrix results.

2. The method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate according to claim 1, characterized in that: The medium- and long-term optimization scheduling model includes an objective function and constraints, where the objective function is expressed as: Among them, NR is the total number of reservoirs, M is the number of months, T is the number of time periods in a day, F is the guaranteed output, and P is the total number of reservoirs. jm The output of reservoir j in m months, Extra output for a typical day in month m, SL mt is the load balance relaxation in period t on a typical day in month m. W1, W2, W3, and W4 are weight values, and W4>>W1>>W2, W3.

3. The method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate according to claim 1 or 2, characterized in that: The constraints include water balance constraints, upper and lower limit constraints of water storage and outflow, equal constraints of initial and final water storage, output expression constraints and typical day load balance constraints; The water balance constraint is expressed as: Among them, V jm is the water storage capacity of reservoir j at the beginning of month, is the inflow of reservoir j in month m, is the outflow of reservoir j in month m, Δm is the length of the period m, is the flow rate of reservoir j in the m-month interval, and Ω(J) is the set of connected reservoirs upstream of reservoir j.

4. The method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate according to claim 3 is characterized in that: The upper and lower limit constraints of water storage and outflow flow are expressed as: in, is the minimum water storage capacity of reservoir j; is the minimum water level; is the maximum water storage capacity of reservoir j; is the maximum water level; are the minimum and maximum outflows of reservoir j respectively; The initial and final water storage equality constraint is expressed as: V j,0 =V j,M Among them, V j,0 represents the initial water storage capacity of reservoir j in the first month, V j,M It represents the final water storage capacity of reservoir j in the last month.

5. The method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate according to claim 4, characterized in that: The output expression constraint is expressed as: in, is the kth set of convex hull parameters of reservoir j whose output is limited by the outflow flow, is the kth set of convex hull parameters of reservoir j whose output is limited by the maximum power generation flow, is the average water storage capacity of reservoir j in month m.

6. The method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate according to claim 5, characterized in that: The typical day load balance constraint is expressed as: in, is the total output of cascade hydropower stations on a typical day, D mt is the typical daily load curve in month m, SL mt is the slack variable for period t on a typical day in the mth month, is the extra output that can be stably provided every hour on a typical day in month m, P jm This is the output of the J reservoir in m months.

7. The method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate according to claim 6, characterized in that: The supply guarantee rate algorithm includes the following steps: Initialization: Set s, y, and n to 0, where s represents the current hydrological state number, y represents the interval flow scenario number, and n represents the number of years to meet the power supply demand; The total interval flow and initial total water storage of the cascade reservoirs are discretized into NS hydrological states, and the total number of hydrological states NS = 20 × 20; Perform medium- and long-term optimization scheduling for each interval traffic scenario, assuming that the number of interval traffic scenarios NY = 50; Determine whether y has reached the total number of interval traffic scenarios NY requirement; If y does not meet the total number of interval traffic scenarios NY requirement, then determine whether the slack variables SL in the typical day load balance constraint are all equal to 0; If SL is equal to 0, it means that the power supply is guaranteed under the current interval traffic scenario, and the number of years n to meet the power supply demand is increased by 1; If SL is not equal to 0, n remains unchanged, and the interval traffic scenario y is switched to calculate whether each scenario meets the supply guarantee demand; If y reaches the total number of interval flow scenarios NY, then calculate the supply guarantee rate F = n / NY under the hydrological state s, and continue to determine whether the current hydrological state number s is greater than or equal to the total number of hydrological states; If the current hydrological state number s is less than the total number of hydrological states NS, then return to the medium- and long-term optimization scheduling solution; If the current hydrological state number s is greater than or equal to the total number of hydrological states NS, the calculation is terminated and the supply guarantee rate matrix result is output.

8. A system based on the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate according to claim 1, characterized in that: The acquisition module is used to obtain historical water inflow data, load curves of typical days of each month, total interval flow and final water storage discrete values ​​of representative months in the dry and flood seasons, and the initial water level of each reservoir, and calculate various interval flow scenarios; A construction module is used to introduce slack variables based on the acquired and calculated data to construct a medium- and long-term optimization scheduling model with load balancing constraints; The division module is used to discretize the initial total interval flow and initial total water storage of the cascade reservoirs based on the constructed medium- and long-term optimization scheduling model, and divide each into 20 hydrological states; An input module is used to input various interval flow scenarios into the medium- and long-term optimization scheduling model based on the hydrological state, perform medium- and long-term optimization scheduling, and obtain medium- and long-term optimization scheduling results; The determination module is used to obtain the supply rate matrix results under each hydrological state through the supply rate algorithm based on the scheduling results, and determine the optimal water level range of the reservoir according to the matrix results.

9. An electronic device, characterized in that: include: Memory, used to store programs; A processor is used to load the program to execute the steps of the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program, characterized in that: When the program is executed by the processor, the steps of the method for determining the optimal water level of cascade reservoirs based on the medium- and long-term power supply guarantee rate as described in any one of claims 1 to 7 are implemented.

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