Method for evaluating competitive relationship between generating capacity and peak regulation target in reservoir dispatching model

Abstract

The invention discloses a method for evaluating the competitive relationship between the generating capacity and the peak regulation target in a reservoir dispatching model, and belongs to the technical field of reservoir dispatching models, and the method comprises the steps: constructing a cascade hydropower station medium-and-long-term optimal dispatching peak regulation model with the minimum residual load mean square error as a target function; constructing a cascade hydropower station medium-and-long-term optimization dispatching electric quantity model taking the maximum generating capacity as an objective function; calculating a cascade power station scheduling strategy according to the peak regulation model and the electric quantity model, and counting the generating capacity and residual load mean square error of the cascade power station according to the scheduling process; and normalizing the power generation amount and residual load mean square error of the cascade power station, and evaluating the competition relationship between the reservoir dispatching peak regulation and the maximum electric quantity target according to the mean square error normalization result. By comparing the peak regulation model with the minimum residual load mean square deviation as the target and the electric quantity model with the maximum electric energy production as the target, the competitive relation between the electric energy production and the peak regulation target in the reservoir dispatching model can be reasonably and effectively evaluated.

Description

Technical Field

[0001] The present invention belongs to the technical field of reservoir scheduling models, and more specifically, relates to a method for evaluating the competitive relationship between power generation and peak-shaving targets in a reservoir scheduling model. Background Art

[0002] As my country continues to promote green and low-carbon energy transformation, the installed capacity of new energy sources such as wind power and photovoltaic power generation continues to expand. However, the intermittent, random and volatile nature of new energy sources places higher demands on the regulation capacity of the power system. Grid peak regulation is the main measure to improve the regulation capacity of the power system, the key support for promoting the large-scale and high-proportion development of new energy, and an important part of building a new power system.

[0003] Peak load regulation is an important task in the operation of the power system. The integration of new energy into the power grid has also caused peak load regulation problems. Hydropower units have always been high-quality peak load regulation power sources for the power system due to their rapid start-stop capability, climbing capability, and deep peak load regulation capability. At present, thermal power still dominates the power supply side of the energy system. The remaining load after hydropower output is generally borne by thermal power. Keeping the remaining load smooth can effectively avoid the frequent start-stop of thermal power units, which is conducive to the economic operation of thermal power and ensures the safe and economic operation of the power grid. The peak load regulation problem of the remaining load of the power grid after hydropower output has always attracted much attention.

[0004] The optimal dispatching of traditional cascade hydropower stations usually adopts the maximum power generation objective function. Since the comprehensive benefits of a reservoir group include flood control, power generation, water supply, shipping, and peak load regulation, the dispatching of a reservoir group needs to meet the comprehensive utilization needs of water resources as much as possible. At present, there is extensive research on the single-objective or multi-objective dispatching of reservoirs (groups). The research model considers the single objective of peak load regulation or power generation, or considers multiple objectives at the same time. However, the competitive or synergistic relationship between the maximum power generation objective and the peak load regulation objective is unclear, and there is also a lack of application analysis of actual engineering cases. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention proposes a method for evaluating the competitive relationship between peak-shaving and maximum power generation targets in a reservoir scheduling model. By comparing the peak-shaving model with the minimum mean square error of the residual load and the power generation model with the maximum power generation target, it can effectively evaluate the competitive and coordinated relationship between power generation and peak-shaving targets in medium- and long-term scheduling.

[0006] To achieve the above object, according to one aspect of the present invention, a method for evaluating the competitive relationship between power generation and peak load regulation targets in a reservoir scheduling model is provided, comprising:

[0007] Construct a mid- and long-term optimal dispatching and peak-shaving model for cascade hydropower stations with the minimum mean square error of residual load as the objective function;

[0008] Construct a mid- and long-term optimal dispatching model for cascade hydropower stations with maximum power generation as the objective function;

[0009] The dispatching strategy of cascade hydropower stations is calculated based on the mid- and long-term optimal dispatching peak-shaving model of cascade hydropower stations and the mid- and long-term optimal dispatching electricity model of cascade hydropower stations. In the process of dispatching according to the dispatching strategy of cascade hydropower stations, the mean square error of the power generation and the residual load of the cascade hydropower stations is calculated.

[0010] The normalized mean square error of power generation and residual load of cascade hydropower stations is used to evaluate the competitive relationship between reservoir peak regulation and maximum power generation objectives.

[0011] In some optional implementation schemes, the mid- and long-term optimal scheduling peak-shaving model of the cascade hydropower stations is: in, f1 is the mean square error of the remaining load after output optimization, i and t are the power station number and the number of dispatching periods, n is the number of cascade power stations, T is the total number of periods, and N is the number of cascade power stations. t is the total load of the hydro-thermal power system in period t, N t ' is the residual load of the hydro-thermal power system in the tth period, P i,t It represents the power station output of reservoir i during period t.

[0012] In some optional implementation schemes, the medium- and long-term optimal dispatching electricity model of the cascade hydropower station is: f2 is the total power generation of the system, n is the number of cascade power stations, t is the dispatching period, T is the total number of periods, P i,t is the power station output of reservoir i in period t, and Δt represents the length of period t.

[0013] In some optional implementation schemes, the cascade power station dispatching strategy is calculated based on the mid- and long-term optimal dispatching peak-shaving model of the cascade hydropower stations and the mid- and long-term optimal dispatching electricity model of the cascade hydropower stations, including:

[0014] Solve and select a target reservoir for optimal dispatching, assuming that the states of other reservoirs remain unchanged, and calculate the medium- and long-term optimal dispatching peak-shaving model of cascade hydropower stations and the medium- and long-term optimal dispatching electricity model of cascade hydropower stations through successive approximation dynamic programming nested discrete differential dynamic programming algorithm, wherein the inputs of the medium- and long-term optimal dispatching peak-shaving model of cascade hydropower stations and the medium- and long-term optimal dispatching electricity model of cascade hydropower stations include reservoir water level, storage capacity, power generation coefficient, maximum flow, minimum flow, upstream and downstream relationship of reservoirs and inflow information, and the input of the medium- and long-term optimal dispatching peak-shaving model of cascade hydropower stations also includes system load;

[0015] Update the operating status of the target reservoir, repeat the solution to select a target reservoir for optimal scheduling, assume that the status of other reservoirs remains unchanged, and calculate the medium- and long-term optimal scheduling peak-shaving model of cascade hydropower stations and the medium- and long-term optimal scheduling electricity model of cascade hydropower stations through the steps of successive approximation dynamic programming nested discrete differential dynamic programming algorithm, optimize each reservoir in turn, cyclically solve and update the operating status of each reservoir until the objective functions of the medium- and long-term optimal scheduling peak-shaving model of cascade hydropower stations and the medium- and long-term optimal scheduling electricity model of cascade hydropower stations are stable and unchanged, and the scheduling strategy of cascade hydropower stations is obtained.

[0016] In some optional implementation schemes, the system load input into the mid- and long-term optimal scheduling peak-shaving model of the cascade hydropower stations includes the total load of the connected cascade hydropower stations and thermal power stations.

[0017] In some optional implementation schemes, the cascade power station dispatching strategy includes: the change process of the water level of the cascade hydropower station and the upstream water level of each reservoir during the control period, the output of each power station, and the residual load of the system.

[0018] In some optional implementation schemes, the evaluation of the competitive relationship between the peak load regulation of reservoir scheduling and the maximum power target based on the normalized power generation of the cascade hydropower stations and the mean square error of the remaining load includes:

[0019] According to the hydrological frequency analysis method, typical years of wet, normal and dry seasons of cascade reservoirs are obtained. The mean square error of residual load of cascade hydropower stations in typical years and the power generation of cascade hydropower stations calculated by the medium- and long-term optimal scheduling peak-shaving model of cascade hydropower stations and the medium- and long-term optimal scheduling electricity model of cascade hydropower stations are compared and analyzed. The mean square error of residual load and the power generation of cascade hydropower stations year by year are normalized to evaluate the relationship between the objective functions of the medium- and long-term optimal scheduling peak-shaving model of cascade hydropower stations and the medium- and long-term optimal scheduling electricity model of cascade hydropower stations in the medium- and long-term optimal scheduling of cascade hydropower stations.

[0020] In some optional embodiments, Normalize the residual load mean square error, where the smaller the mean square error, the more stable the residual load. α is the normalized mean square error value, ranging from 0 to 1. The closer to 1, the better the effect. f1 is the mean square error of the residual load year by year, f1 max is the maximum mean square error of residual load in all years, f1 min It is the smallest residual load mean square error among all years.

[0021] In some optional embodiments, Normalized cascade hydropower station power generation, where the larger the cascade hydropower station power generation, the better, β is the normalized cascade hydropower station power generation value, ranging from 0 to 1, the closer to 1, the better the effect, f2 is the annual cascade hydropower station power generation, f2 maxis the largest cascade hydropower station power generation in all years, f2 min This is the smallest power generation of cascade hydropower stations in all years.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0023] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0024] The method for evaluating the impact of new energy consumption on the operating water level of a reservoir group provided by the present application includes the following steps: constructing a medium- and long-term optimal dispatching peak-shaving model for cascade hydropower stations with the minimum mean square error of residual load as the objective function; constructing a medium- and long-term optimal dispatching electricity model for cascade hydropower stations with the maximum power generation as the objective function; calculating the cascade hydropower station water level and the upstream water level of each reservoir, the output of each power station, the change process of the system residual load and other cascade power station dispatching strategies during the control period according to the medium- and long-term optimal dispatching peak-shaving model and the electricity model of the cascade hydropower station, and statistically calculating the mean square error of the power generation and residual load of the cascade hydropower station according to the dispatching process; normalizing the mean square error of the power generation and residual load of the cascade power station calculated by the dispatching peak-shaving model and the electricity, and evaluating the competitive relationship between the peak-shaving and maximum electricity targets of reservoir dispatching according to the normalized result of the mean square error. The method for evaluating the competitive relationship between power generation and peak-shaving targets in the reservoir dispatching model provided by the present application can reasonably and effectively evaluate the competitive relationship between power generation and peak-shaving targets in the reservoir dispatching model by comparing the peak-shaving model with the minimum mean square error of residual load as the objective and the electricity model with the maximum power generation as the objective. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A flow chart of a method for evaluating the competitive relationship between power generation and peak load regulation targets in a reservoir scheduling model provided in an embodiment of the present application;

[0027] Figure 2 The annual average water level of Reservoir A in the method for evaluating the competitive relationship between power generation and peak-shaving targets in a reservoir scheduling model provided in an embodiment of the present application;

[0028] Figure 3The annual load curve of the cascade hydropower station in the method for evaluating the competitive relationship between power generation and peak-shaving targets in a reservoir scheduling model provided in an embodiment of the present application;

[0029] Figure 4 Typical annual output and residual load of cascade hydropower stations in the method for evaluating the competitive relationship between power generation and peak-shaving targets in a reservoir scheduling model provided in an embodiment of the present application;

[0030] Figure 5 The method for evaluating the competitive relationship between power generation and peak load regulation in the reservoir scheduling model provided in the embodiment of the present application provides the mean square error of the annually normalized power generation and residual load of different models of cascade hydropower stations;

[0031] Figure 6 In the method for evaluating the competitive relationship between power generation and peak load regulation target in the reservoir scheduling model provided in the embodiment of the present application Figure 5 Relationship between slope and average inflow flow. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0033] like Figure 1 As shown, the embodiment of the present application provides a method for evaluating the competitive relationship between power generation and peak load regulation targets in a reservoir scheduling model, which includes the following steps:

[0034] S1: Construct a mid- and long-term optimal dispatching and peak-shaving model for cascade hydropower stations with the minimum mean square error of residual load as the objective function;

[0035] Among them, hydropower units have always been high-quality peak-shaving power sources for power systems due to their rapid start-stop capability, climbing capability, and deep peak-shaving capability. In view of the large fluctuation of the residual load of the current power grid, a medium- and long-term optimal scheduling peak-shaving model for cascade hydropower stations is constructed, and the minimum mean square error of the residual load is used as the objective function as shown in the following equations (1) and (2);

[0036]

[0037] In formula (1), f1 is the mean square error of the remaining load of the system after output optimization; i and t are the power station number and the number of dispatching periods, respectively; n is the number of cascade power stations; T is the total number of time periods; N t 、N t ' are the total load of the hydro-thermal power system and the residual load of the hydro-thermal power system in the tth period. In formula (2), Pi,t It represents the power station output of reservoir i during period t.

[0038] S2: Construct a mid- and long-term optimal dispatching model for cascade hydropower stations with maximum power generation as the objective function;

[0039] Among them, the medium- and long-term dispatch of traditional cascade hydropower stations generally adopts the maximum power generation as the objective function. When the medium- and long-term optimal dispatch model of cascade hydropower stations is obtained, the runoff process within the given control period is taken as the objective function under the conditions of comprehensive utilization and constraints, as shown in the following formula (3):

[0040]

[0041] In formula (3), f2 is the total power generation of the system; n is the number of cascade power stations; t is the dispatching period; T is the total number of time periods; P i,t is the power station output of reservoir i in period t; Δt represents the length of period t.

[0042] S3: Based on the mid- and long-term optimal dispatching peak-shaving model and the electricity model of the cascade hydropower stations, the dispatching strategy of the cascade hydropower stations, such as the water level of the cascade hydropower stations and the change process of the upstream water level of each reservoir during the control period, the output of each power station, and the residual load of the system, is calculated. According to the dispatching process, the mean square error of the power generation and residual load of the cascade hydropower stations is calculated;

[0043] Among them, in step S3, the cascade power station dispatching strategy is calculated based on the mid- and long-term optimal dispatching peak-shaving and power model of the cascade hydropower station. The inputs of the cascade hydropower station peak-shaving model and the power model include reservoir water level, storage capacity, power generation coefficient, maximum flow, minimum flow, upstream and downstream relationship of the reservoir, inflow information and other data; the input of the peak-shaving model also includes system load. Both the peak-shaving model and the power model are solved by the successive approximation dynamic programming nested discrete differential dynamic programming algorithm, and the algorithm solution includes the following steps:

[0044] (1) Solve and select a reservoir to optimize its operation, assuming that the states of other reservoirs remain unchanged, and calculate through discrete differential dynamic programming algorithm;

[0045] (2) updating the operating status of the selected reservoir, repeating step (1) to optimize each reservoir in turn, cyclically solving and updating the operating status of each reservoir;

[0046] (3) The calculation is completed until the objective function becomes stable and unchanged. The obtained scheduling strategy is the optimal operation strategy.

[0047] S4: Normalize the mean square error of the power generation and residual load of the cascade power stations calculated by different models, and evaluate the competition relationship between the peak load regulation of reservoir scheduling and the maximum power consumption target based on the mean square error normalization result.

[0048] In step S4, when evaluating the competitive relationship between power generation and peak-shaving targets in the reservoir scheduling model, the typical years of high, medium and low seasons of the cascade reservoirs are obtained according to the hydrological frequency analysis method, and the mean square error of residual load and the total power generation of the cascade hydropower stations in typical years calculated by the peak-shaving model and the power model are compared and analyzed. The mean square error of residual load and the total power generation year by year are normalized to further evaluate the relationship between the two objective functions in the medium- and long-term optimal scheduling of cascade hydropower stations.

[0049] The smaller the mean square error, the more stable the residual load. The normalized mean square error of the residual load is calculated using the following formula (4):

[0050]

[0051] In formula (4), α is the normalized mean square error value, ranging from 0 to 1. The closer it is to 1, the better the effect. f1 is the mean square error of residual load year by year, f1 max 、f1 min is the maximum and minimum mean square error of residual load in all years.

[0052] The larger the total power generation, the better. The normalized total power generation is calculated using the following formula (5):

[0053]

[0054] In formula (5), β is the homogenized power generation value, ranging from 0 to 1. The closer it is to 1, the better the effect. f2 is the total power generation year by year, The maximum and minimum total power generation in all years.

[0055] The method for evaluating the competitive relationship between power generation and peak-shaving targets in a reservoir scheduling model provided in an embodiment of the present application can reasonably and effectively evaluate the competitive relationship between power generation and peak-shaving targets in a reservoir scheduling model by comparing a peak-shaving model with the minimum mean square error of residual load and an electricity model with the maximum power generation.

[0056] The features and performance of the method for evaluating the competitive relationship between power generation and peak-shaving targets in a reservoir scheduling model of the present application are further described in detail below in conjunction with the embodiments.

[0057] Taking a cascade hydropower station in the lower reaches of the Minjiang River in Fujian Province as an example, the cascade hydropower station is composed of four power stations connected in series, and the leading reservoir of the cascade hydropower station is Reservoir A. The historical water inflow process of each reservoir for a total of 47 years from 1974 to 2020 is used as input, and the interval inflow and the maximum value of the reservoir operating water are used as initial conditions. The above-mentioned method for evaluating the competitive relationship between power generation and peak-shaving targets in the reservoir scheduling model is used to evaluate the competitive relationship between power generation and peak-shaving targets as shown below, indicating that the method for evaluating the competitive relationship between power generation and peak-shaving targets in the reservoir scheduling model provided in this embodiment is reasonable and effective.

[0058] The system load as the input of the peak load regulation model needs to be reasonably estimated. Given the typical annual total load of the province, the load curves of cascade hydropower stations and thermal power stations can be obtained by deducting the output of wind and solar power and the output of other power stations such as nuclear power plants, pumped storage power stations, and hydropower stations, as shown in the following figure: Figure 2 As shown, Figure 2 (a) is the load saving curve, Figure 2 (b) is the load curve of cascade hydropower stations and thermal power stations.

[0059] According to the hydrological frequency analysis method, the wet, normal and dry years of the cascade reservoirs were 1976, 1989 and 1979 respectively. Figure 3 Display, in Figure 3 In (a) and (b), when the peak-shaving model is used, the total output of the cascade hydropower stations shows significantly stronger volatility than the electricity model, and the output fluctuation during the flood season is significantly higher than that in other periods. Comparison of the output change patterns of different models: the output of the electricity model changes with the inflow, showing a pattern of more output during the flood season rather than less output during the flood season; while the output of the electricity model is related to the change of the load curve, with output peaks and valleys corresponding to load curve peaks and valleys, and the peak-shaving period is mainly concentrated in March-July. Analysis of the reasons for the emergence of the pattern: the output of the cascade hydropower station is closely related to the objective function of the model. The electricity model pursues maximum power generation, so it chooses more output during the flood season; while the peak-shaving model focuses on the stability of the residual load and uses adjustable water volume to adjust the output. When the water volume is sufficient, the peak-shaving capacity is weak and it mainly bears the basic load. According to Figure 4 As shown in the figure, it is found that the water level calculated by the power model is higher than that calculated by the peak-shaving model in most years, but the overall trend of the water level change is the same. This is because the power model tries to raise the water level as much as possible in order to achieve the maximum power generation, so the water level of the peak-shaving model is lower than that of the power model.

[0060] The model is evaluated by the mean square error of residual load and the total power generation of cascade hydropower stations. The smaller the mean square error, the more stable the residual load. The corresponding results of different models in different typical years are shown in Table 1.

[0061] Table 1 Comparison of residual load mean square error and power generation of cascade hydropower station optimization model

[0062]

[0063] As shown in Table 1, the peak-shaving and power generation objective functions show a competitive relationship in a typical year. The residual load volatility calculated by the peak-shaving model in a typical flood year, a normal water year, and a dry year is reduced by 0.53%, 0.50%, and 0.51% respectively compared with the power model. The average value of the mean square error of the residual load in each year from 1974 to 2020 is reduced by 0.57%. The total power generation of cascade hydropower stations calculated by the power model in a typical flood year, a normal water year, and a dry year is increased by 7.73%, 6.55%, and 8.24% respectively compared with the peak-shaving model. The average power generation in each year from 1974 to 2020 is increased by 8.01%. In order to further analyze the relationship between the two objective functions in the medium- and long-term optimal scheduling of cascade hydropower stations, the scatter plots of the two models for 47 years are plotted as follows: Figure 5 By analyzing Figure 5 The distribution and slope of the scattered points in the graph lead to the conclusion that the peak-shaving model has more advantages than the power model. The peak-shaving model can improve the stability of the residual load mean square error while reducing the power generation. Although the power model can obtain higher power generation, its peak-shaving performance is poor. Figure 6 As shown in Figure 2, the inflow is inversely proportional to the slope: as the inflow increases, the slope decreases. The maximum slope in 2004 corresponds to the minimum average inflow. Figure 5 The data show that in this case, the power generation benefits of the two models are almost the same, but the peak-shaving model performs better in terms of residual load mean square error. This shows that in this case, the peak-shaving model has more advantages than the power model. Therefore, it can be concluded that as the inflow decreases, the greater the slope between the scatter points, the more significant the advantage of the peak-shaving model becomes.

[0064] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0065] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the competitive relationship between power generation and peak load regulation in a reservoir operation model, characterized in that: include: Construct a mid- and long-term optimal dispatching and peak-shaving model for cascade hydropower stations with the minimum mean square error of residual load as the objective function; Construct a mid- and long-term optimal dispatching model for cascade hydropower stations with maximum power generation as the objective function; The dispatching strategy of cascade hydropower stations is calculated based on the mid- and long-term optimal dispatching peak-shaving model of cascade hydropower stations and the mid- and long-term optimal dispatching electricity model of cascade hydropower stations. In the process of dispatching according to the dispatching strategy of cascade hydropower stations, the mean square error of the power generation and the residual load of the cascade hydropower stations is calculated. The normalized mean square error of power generation and residual load of cascade hydropower stations is used to evaluate the competitive relationship between reservoir peak regulation and maximum power generation objectives.

2. The method according to claim 1, characterized in that The mid- and long-term optimal dispatching peak-shaving model of the cascade hydropower stations is: in, f1 is the mean square error of the remaining load after output optimization, i and t are the power station number and the number of dispatching periods, n is the number of cascade power stations, T is the total number of periods, and N is the number of cascade power stations. t is the total load of the hydro-thermal power system in period t, N t ' is the residual load of the hydro-thermal power system in the tth period, P i,t It represents the power station output of reservoir i during period t.

3. The method according to claim 1, characterized in that The medium- and long-term optimal dispatching electricity model of the cascade hydropower stations is: f2 is the total power generation of the system, n is the number of cascade power stations, t is the dispatching period, T is the total number of periods, P i,t is the power station output of reservoir i in period t, and Δt represents the length of period t.

4. The method according to any one of claims 1 to 3, characterized in that: The cascade power station dispatching strategy is calculated based on the mid- and long-term optimal dispatching peak-shaving model of the cascade hydropower stations and the mid- and long-term optimal dispatching electricity model of the cascade hydropower stations, including: Solve and select a target reservoir for optimal dispatching, assuming that the states of other reservoirs remain unchanged, and calculate the medium- and long-term optimal dispatching peak-shaving model of cascade hydropower stations and the medium- and long-term optimal dispatching electricity model of cascade hydropower stations through successive approximation dynamic programming nested discrete differential dynamic programming algorithm, wherein the inputs of the medium- and long-term optimal dispatching peak-shaving model of cascade hydropower stations and the medium- and long-term optimal dispatching electricity model of cascade hydropower stations include reservoir water level, storage capacity, power generation coefficient, maximum flow, minimum flow, upstream and downstream relationship of reservoirs and inflow information, and the input of the medium- and long-term optimal dispatching peak-shaving model of cascade hydropower stations also includes system load; Update the operating status of the target reservoir, repeat the solution to select a target reservoir for optimal scheduling, assume that the status of other reservoirs remains unchanged, and calculate the medium- and long-term optimal scheduling peak-shaving model of cascade hydropower stations and the medium- and long-term optimal scheduling electricity model of cascade hydropower stations through the steps of successive approximation dynamic programming nested discrete differential dynamic programming algorithm, optimize each reservoir in turn, cyclically solve and update the operating status of each reservoir until the objective functions of the medium- and long-term optimal scheduling peak-shaving model of cascade hydropower stations and the medium- and long-term optimal scheduling electricity model of cascade hydropower stations are stable and unchanged, and the scheduling strategy of cascade hydropower stations is obtained.

5. The method according to claim 4, characterized in that The system load input into the mid- and long-term optimal dispatching peak-shaving model of the cascade hydropower stations includes the total load of the connected cascade hydropower stations and thermal power stations.

6. The method according to claim 5, characterized in that The cascade power station dispatching strategy includes: the change process of the water level of the cascade hydropower station and the upstream water level of each reservoir during the control period, the output of each power station, and the system residual load.

7. The method according to claim 6, characterized in that The evaluation of the competition relationship between the peak load regulation of reservoir scheduling and the maximum power target based on the normalized power generation of cascade hydropower stations and the mean square error of residual load includes: According to the hydrological frequency analysis method, typical years of wet, normal and dry seasons of cascade reservoirs are obtained. The mean square error of residual load of cascade hydropower stations in typical years and the power generation of cascade hydropower stations calculated by the medium- and long-term optimal scheduling peak-shaving model of cascade hydropower stations and the medium- and long-term optimal scheduling electricity model of cascade hydropower stations are compared and analyzed. The mean square error of residual load and the power generation of cascade hydropower stations year by year are normalized to evaluate the relationship between the objective functions of the medium- and long-term optimal scheduling peak-shaving model of cascade hydropower stations and the medium- and long-term optimal scheduling electricity model of cascade hydropower stations in the medium- and long-term optimal scheduling of cascade hydropower stations.

8. The method according to claim 7, characterized in that Depend on Normalize the residual load mean square error, where the smaller the mean square error, the more stable the residual load. α is the normalized mean square error value, ranging from 0 to 1. The closer to 1, the better the effect. f1 is the mean square error of the residual load year by year, f1 max is the maximum mean square error of residual load in all years, f1 min It is the smallest residual load mean square error among all years.

9. The method according to claim 8, characterized in that Depend on Normalized cascade hydropower station power generation, where the larger the cascade hydropower station power generation, the better, β is the normalized cascade hydropower station power generation value, ranging from 0 to 1, the closer to 1, the better the effect, f2 is the annual cascade hydropower station power generation, The largest cascade hydropower station power generation in all years, This is the smallest power generation of cascade hydropower stations in all years.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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