A method and system for constructing watershed hydro-wind-solar complementary regulation rules to enhance short-term grid flexibility.

Abstract

This invention belongs to the field of power system generation dispatching and discloses a method and system for constructing watershed hydro-wind-solar complementary control rules to enhance the short-term flexibility support of the power grid. Based on the typical intraday peak and off-peak output process of hydropower stations, and combining regulation capacity and regulation duration, the short-term flexibility requirements of the power grid are quantified. With grid demand as a constraint, a short-time series simulation optimization model is constructed to generate hydropower power control boundaries and a long-term monthly peak-shaving function that reflects the short-term peak-shaving response characteristics of the system. On this basis, a long-term multi-objective optimization model that considers both power generation benefits and system peak-shaving requirements is established. Under the long-term hydropower power boundary constraint, a long-term multi-objective long-time series optimization scheduling model for hydro-wind-solar complementary systems is calculated to improve the regulation capacity of the watershed hydro-wind-solar complementary system. Finally, the optimal long-term control rules of the watershed hydro-wind-solar complementary system are determined by fitting the monthly scheduling function through a BP neural network and combining the calculation results of the optimal energy storage interval.

Description

Technical Field

[0001] This invention belongs to the field of power system dispatching and relates to a method and system for constructing watershed hydro-wind-solar complementary control rules to enhance the short-term flexibility of the power grid. Background Technology

[0002] With the continuous increase in the penetration rate of fluctuating renewable energy sources such as wind and solar power in the power system, their inherent intermittency and anti-peak-shaving characteristics have significantly exacerbated net load fluctuations, leading to increasingly severe pressure on the power grid's peak-shaving capacity. Against this backdrop, the peak-shaving capacity of river basin hydro-wind-solar hybrid systems, with their flexible adjustment capabilities, is crucial for ensuring the safe and stable operation of the power grid. Hydropower, as the core regulating power source of these systems, often uses peak-shaving mode to smooth fluctuations in wind and solar output during short-term operation. However, the short-term regulation performance of hydropower is profoundly constrained by the long-term dispatch framework, particularly the rationality of the monthly power generation plan, which directly determines its peak-shaving performance: excessively high monthly power generation plans will crowd out the unit's adjustment margin, making it difficult to cope with peak and valley demands; while excessively low plans will weaken its support capacity, resulting in insufficient peak-shaving capacity.

[0003] Currently, research on the long-term operation of watershed hydro-wind-solar hybrid systems mainly focuses on the generation of scheduling schemes or the extraction of traditional rules. In terms of long-term peak shaving, existing studies usually treat peak shaving as a static optimization objective, failing to fully consider the dynamic peak shaving demand of the power grid that evolves daily within a month (Jiang J, Ming B, Liu P, et al. Refining long-term operation of large hydro–photovoltaic–wind hybrid systems by nesting response functions[J]. Renewable Energy, 2023, 204: 359-371). This limitation may lead to two types of problems: First, excessive pursuit of peak shaving targets may sacrifice the overall power generation efficiency of the system, resulting in suboptimal economic performance (Lin MK, Shen JJ, Cheng CT, et al. Long-term multi-objective optimalscheduling for large cascaded hydro-wind-photovoltaic complementary systems considering short-term peak-shaving demands[J]. Energy Conversion and Management, 2024, 301: 118063); Second, the actual peak shaving demand of the power grid may not be met, resulting in a deviation between long-term planning and short-term operation, which not only causes economic losses but also increases the risk of system operation (Wen Xin, Sun Yuanliang, Tan Qiaofeng, et al. Risk and benefit analysis of wind-solar-hydro multi-energy complementary system scheduling considering prediction uncertainty[J]. Engineering Science and Technology, 2020, 52(3):32-41).

[0004] It should be further noted that the peak-shaving demand of the power grid itself exhibits a dynamic growth trend. With the continuous expansion of the installed capacity of fluctuating renewable energy, the peak-to-valley load difference is expected to widen further, and the power grid's peak-shaving requirements for river basin hydro-wind-solar hybrid systems will become increasingly stringent, thus exacerbating the aforementioned risks. Therefore, effectively embedding dynamically evolving peak-shaving demands into long-term regulation rules has become a key challenge for improving system performance.

[0005] To address the aforementioned issues, this invention, using a large-scale hydro-wind-solar hybrid system in a typical river basin in Southwest China as an engineering background, proposes a method and system for constructing river basin hydro-wind-solar hybrid control rules to enhance short-term grid flexibility. This method combines dynamic grid peak-shaving demand quantitative modeling, refined short-term operation simulation, and long-term optimized scheduling to reshape the monthly power output process of hydropower, thereby extracting control rules that balance long-term power generation benefits with short-term flexibility. Finally, through practical case testing, the effectiveness and superiority of the proposed control rules are verified from two dimensions: long-term economic benefits and short-term peak-shaving capacity. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a method and system for constructing watershed hydro-wind-solar complementary control rules to enhance the support for short-term power grid flexibility. The purpose is to address the growing short-term flexibility requirements of new power systems, explore the flexibility potential of watershed hydro-wind-solar complementary systems, and create a set of watershed hydro-wind-solar complementary control rules and application system by integrating the short-term power flexibility requirements of the power grid, so as to improve the ability of watershed hydro-wind-solar complementary systems to support the power grid flexibility requirements throughout the year.

[0007] Technical solution of the present invention:

[0008] A method for constructing watershed hydro-wind-solar complementary regulation rules to enhance short-term grid flexibility includes the following steps:

[0009] (1) Characterize the short-term flexibility requirements of the power grid;

[0010] For a specific month, based on the typical daily peak and off-peak output process of a hydropower station, considering the regulation capacity... and adjust duration Quantify the minimum daily peak-shaving power demand of the power grid in that month. and minimum daily electricity demand as follows:

[0011] (1)

[0012] (2)

[0013] (3)

[0014] In the formula, These are the peak and off-peak outputs of a typical daily peak-valley power station.

[0015] (2) Construct a short-time series simulation optimization model to obtain the monthly peak-shaving function and the monthly hydropower boundary that meets the short-term flexibility requirements of the power grid; the objective function of the short-time series simulation optimization model is as follows:

[0016] (4)

[0017] (5)

[0018] In the formula, The target value for peak-shaving power; This is a collection of the number of days in that month that have been spent working diligently and beautifully over many years. The number of scenic scenes representing typical intraday power output processes; This represents the probability of corresponding landscape scenes; Indicates the length of the set of days; For the first Daytime and scenic scenes Peak-shaving power consumption; This is the set of time periods corresponding to the peak output of a typical daily peak and trough power output process of a hydropower station in that month. For the first Daytime and scenic scenes Within the next day The power output of the hydro-wind-solar hybrid system at any given moment; For the first Daytime and scenic scenes Average power output of the hydro-wind hybrid system during the daytime off-peak hours;

[0019] The monthly peak-shaving function and the monthly hydropower capacity boundary for meeting the short-term flexibility requirements of the power grid are simulated in two stages. The constraints include the corresponding constraints of the monthly peak-shaving function relationship simulation and the corresponding constraints of the monthly hydropower capacity boundary simulation.

[0020] 2.1) Simulate the corresponding constraints of the monthly peak-shaving function relationship;

[0021] Hydropower and wind / solar power output at different time scales need to satisfy multi-scale power conservation constraints, as follows:

[0022] (6)

[0023] (7)

[0024] In the formula, It is the collection of the number of days in that month that have been glorious and productive over many years. The Middle The set of days in a year; It is the collection of all months. ; Provide power to the hydropower station on a monthly scale; For the scenery in the first daily effort; and Hydropower and wind power were respectively ranked in the first place. Daytime and scenic scenes Within the next day Efforts made at all times; It is a collection of time periods during which the hydropower station typically experiences peak and off-peak output within a single day during that month.

[0025] 2.2) Constraints corresponding to the monthly hydropower generation boundary simulation;

[0026] The daily power generation and peak-shaving power generation of the hydro-wind-solar hybrid system within a month need to meet the short-term flexibility requirements of the power grid. The minimum daily peak-shaving power generation requirement obtained in step (1) is introduced. and minimum daily electricity demand The constraints are as follows:

[0027] (8)

[0028] (9)

[0029] (10)

[0030] In the formula, The duration of a time period is expressed in hours.

[0031] 2.3) During the two simulations, the following steps were followed: First, from the minimum hydropower output of 0 to the maximum hydropower output... Discretization is performed between the two to obtain the hydropower output sequence. And based on the hydropower output value in the hydropower output sequence Replace the monthly output of the hydropower station in equation (6) in sequence. Repeated optimization calculations are performed.

[0032] In the simulation of monthly peak-shaving function relationships: collect the peak-shaving power target values ​​obtained from each optimization of the short-time series simulation optimization model. Therefore, based on the hydropower output sequence and peak-shaving power target value The peak-shaving curves of the hydro-wind-solar hybrid system were plotted for each month, and the peak-shaving curves were fitted to the peak-shaving functions for each month. After monthly simulation calculations throughout the year, the monthly peak-shaving functions reflecting the changes throughout the year were obtained. ;

[0033] In the monthly hydropower output boundary simulation: if a certain hydropower output value in the hydropower output sequence If no solution is found during the optimization process, then the hydropower output value is considered to be... Not feasible; collect all solvable hydropower output values ​​to determine the feasible boundary interval for hydropower output in that month; through simulation calculations for each month of the year, finally obtain the monthly boundary interval for hydropower output throughout the year. ;

[0034] (3) Construct a long-term, multi-objective, long-time-series optimization scheduling model for water, wind, and solar power. Its objective function and constraints are as follows:

[0035] 3.1) The objective function is as follows:

[0036] (11)

[0037] (12)

[0038] In the formula, The total power generation of the hydro-wind-solar hybrid system during the entire dispatch cycle; A collection of hydroelectric power stations; It is a set of long-time scheduling periods; For hydroelectric power station exist Monthly output; For the scenery Monthly output; Duration in months; The peak-shaving power for the entire scheduling cycle of the hydro-wind-solar hybrid system; for Monthly peak adjustment function;

[0039] 3.2) The constraints are as follows:

[0040] (13)

[0041] In the formula, and They are respectively The lower and upper boundaries of the monthly hydropower output are the monthly hydropower output boundary intervals obtained in step (2);

[0042] (4) Construct water-wind-solar complementary regulation rules for the basin, including monthly scheduling functions and monthly regulation intervals for cascade energy storage;

[0043] Based on the optimization results of the long-term multi-objective long-term time-series optimization scheduling model for water, wind and solar power constructed in step (3), the initial reservoir capacity, runoff, and wind and solar power output of this month are used as inputs, and any variable such as hydropower output, reservoir capacity at the end of this month, or discharge is used as output. The BP neural network is used to fit the data month by month, and the monthly scheduling function is determined based on the fitting results. The lower and upper boundaries of the monthly reservoir capacity process in the optimization results of the long-term multi-objective long-term time-series optimization scheduling model for water, wind and solar power constructed in step (3) are used to calculate the energy storage monthly control interval of the cascade energy storage, forming the energy storage scheduling boundary. The energy storage calculation method is as follows:

[0044] (14)

[0045] (15)

[0046] In the formula, For hydroelectric power station exist Monthly energy storage; For hydroelectric power station exist Monthly storage capacity; For hydroelectric power station A collection of upstream hydropower stations; For hydroelectric power station exist Average water consumption rate for the month; It is a cascade hydropower station group in Total energy storage for the month.

[0047] A system for constructing watershed hydro-wind-solar complementary regulation rules to enhance short-term grid flexibility includes:

[0048] The grid short-term flexibility demand module is used to calculate the minimum daily peak-shaving power demand and minimum daily power demand of the power grid within a month, based on the typical intraday peak and off-peak output process of hydropower. The grid short-term flexibility demand module quantifies the grid's regulation capacity and regulation duration requirements for hydro-wind-solar hybrid systems by introducing a set of indicators for regulation capacity and regulation duration. It also calculates peak-shaving power and basic power through regulation capacity and regulation duration, thereby quantifying the grid's minimum daily peak-shaving power demand and daily power demand within a month.

[0049] The short-time series simulation optimization module is used to construct a short-time series simulation optimization model under the constraint of the grid short-term flexibility demand module, which considers the typical intraday output scenarios of multiple consecutive days and multiple wind and solar scenarios within a month. It realizes the simulation of monthly peak-shaving function relationship and monthly hydropower power boundary simulation. By taking the discrete elements of the hydropower output interval as input, repeated simulation optimization calculations are performed to obtain the monthly peak-shaving function and the monthly hydropower power boundary that meets the grid short-term flexibility demand.

[0050] The long-term multi-objective long-time series optimization scheduling module for water, wind and solar power is used to integrate the output results of the short-time series simulation optimization module. It introduces two objectives: maximizing the power generation of the water, wind and solar complementary system during the entire scheduling cycle and maximizing the peak power of the water, wind and solar complementary system during the entire scheduling cycle. It constructs a long-term multi-objective long-time series optimization scheduling model for water, wind and solar power to solve for the optimal multi-year monthly scheduling results of the watershed water, wind and solar complementary system at the long-time series scale.

[0051] The watershed hydro-wind-solar complementary control module utilizes the optimal multi-year monthly scheduling results obtained from the long-term multi-objective long-time series optimization scheduling module for water, wind, and solar power, and introduces a BP neural network to fit the monthly scheduling function of each cascade hydropower station. At the same time, based on the upper and lower boundaries of the hydropower output of the monthly optimal reservoir capacity process set obtained from the long-term multi-objective long-time series optimization scheduling module for water, wind, and solar power, the monthly control interval of cascade energy storage is calculated to achieve long-term optimal scheduling of the watershed hydro-wind-solar complementary system.

[0052] A computer device includes a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, it implements a watershed hydro-wind-solar complementary regulation rule construction system to enhance short-term grid flexibility.

[0053] A storage medium storing a computer program that, when executed by a processor, implements a watershed hydro-wind-solar complementary regulation rule construction system to enhance short-term grid flexibility.

[0054] The beneficial effects of this invention are as follows: This invention proposes a quantitative method for grid peak-shaving demand and systematically integrates it into the construction of long-term regulation rules for a watershed hydro-wind-solar hybrid system. By introducing long-term power boundary constraints on grid demand and a peak-shaving function guidance mechanism, the power allocation strategy for hydropower on a monthly scale throughout the year is optimized. This method significantly improves the rate at which the hybrid system meets the long-term peak-shaving demand of the grid while ensuring the overall operational efficiency of the system, and enhances its dynamic adaptation and proactive support capabilities for the grid's short-term flexibility needs. Attached Figure Description

[0055] Figure 1 This is a schematic diagram representing the short-term flexibility demand of the power grid;

[0056] Figure 2 This is a flowchart of short-time simulation optimization calculation;

[0057] Figure 3 The graphs show a comparison between the deterministic optimization results and the simulation results of the control rules; where (a) is the optimization result graph of Rule 1, (b) is the simulation result graph of Rule 2, (c) is the optimization result graph of Rule 2, and (d) is the simulation result graph of Rule 2.

[0058] Figure 4 This is a comparison rule verification result diagram;

[0059] Figure 5 This is a screenshot of the verification results for this rule;

[0060] Figure 6 The charts show the comparison of monthly average power output and peak-shaving power; (a) is the monthly average power output chart, (b) is the monthly cumulative peak-shaving power chart, and (c) is the monthly peak-shaving power chart.

[0061] Figure 7 This is a monthly short-term flexibility demand chart for the power grid;

[0062] Figure 8 This is a flowchart of the method of the present invention. Detailed Implementation

[0063] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings and technical solutions.

[0064] A method for constructing watershed hydro-wind-solar complementary regulation rules to enhance short-term grid flexibility is described below:

[0065] (1) Characterize the short-term flexibility requirements of the power grid;

[0066] For a specific month, based on the typical daily peak and off-peak output process of a hydropower station, considering the regulation capacity... and adjust duration Quantify the minimum daily peak-shaving power demand of the power grid in that month. and minimum daily electricity demand ,like Figure 1 As shown. The calculation formula is as follows:

[0067] (16)

[0068] (17)

[0069] (18)

[0070] Applying this method to all 12 months of the year yields the minimum daily peak-shaving power demand and minimum daily power demand for each month of the year.

[0071] (2) Construct a short-time simulation optimization model to obtain the monthly peak-shaving function and the monthly hydropower boundary that meets the short-term flexibility requirements of the power grid;

[0072] Processing daily wind and solar power generation data. Obtain the daily wind and solar power generation time series dataset for the calculated month over the past n years, denoted as [data missing]. y j Represents daily wind and solar power generation data for year j; cluster analysis of daily wind and solar power output processes in that month yields typical daily wind and solar power output processes (number of processes). For any given year and day, the daily power generation time series of wind and solar power is scaled proportionally based on the daily power generation value to obtain the corresponding wind and solar power output curves for a typical day.

[0073] A short-time series simulation optimization model is constructed, with the following objective function:

[0074] (19)

[0075] (20)

[0076] The monthly peak-shaving function and the monthly hydropower boundary to meet the short-term flexibility requirements of the power grid are simulated in two stages. The constraints include the corresponding constraints of the monthly peak-shaving function relationship simulation and the corresponding constraints of the monthly hydropower boundary simulation, as follows:

[0077] 2.1) Simulate the corresponding constraints of the monthly peak-shaving function relationship;

[0078] Hydropower and wind / solar power output at different time scales need to satisfy multi-scale power conservation constraints, as follows:

[0079] (twenty one)

[0080] (twenty two)

[0081] 2.2) Constraints corresponding to the monthly hydropower generation boundary simulation;

[0082] The daily power generation and peak-shaving power generation of the hydro-wind-solar hybrid system within a month need to meet the short-term flexibility requirements of the power grid. The minimum daily peak-shaving power generation requirement obtained in step (1) is introduced. and minimum daily electricity demand The constraints are as follows:

[0083] (twenty three)

[0084] (twenty four)

[0085] (25)

[0086] In addition, the constraints also include variable boundary constraints and synchronization adjustment constraints, as follows:

[0087] Variable boundary constraints:

[0088] (26)

[0089] (27)

[0090] In the formula, For the channel capacity of the water-wind-solar hybrid system; and These are the lower and upper boundaries of the hydropower output.

[0091] Synchronization adjustment constraints:

[0092] (28)

[0093] In the formula, Typical load During specific time periods; Auxiliary variables ( ).

[0094] 2.3) During the two simulations, the following steps were followed: First, from the minimum hydropower output of 0 to the maximum hydropower output... Discretization is performed between the two to obtain the hydropower output sequence. And based on the hydropower output value PH in the hydropower output sequence m The monthly output of the hydropower station in equation (6) is replaced sequentially. Repeated optimization calculations are performed.

[0095] In the simulation of monthly peak-shaving function relationships: collect the peak-shaving power target values ​​obtained from each optimization of the short-time series simulation optimization model. Therefore, based on the hydropower output sequence and peak-shaving power target value The peak-shaving curves of the hydro-wind-solar hybrid system were plotted for each month, and the peak-shaving curves were fitted to the peak-shaving functions for each month. After monthly simulation calculations throughout the year, the monthly peak-shaving functions reflecting the changes throughout the year were obtained. ;

[0096] In the monthly hydropower output boundary simulation: if a certain hydropower output value in the hydropower output sequence If no solution is found during the optimization process, then the hydropower output value is considered to be... Not feasible; collect all solvable hydropower output values ​​to determine the feasible boundary interval for hydropower output in that month; through simulation calculations for each month of the year, finally obtain the monthly boundary interval for hydropower output throughout the year. .

[0097] (3) Construct a long-term, multi-objective, long-time-series optimization scheduling model for water, wind, and solar power;

[0098] 3.1) Objective Function

[0099] (29)

[0100] (30)

[0101] 3.2) The constraints are as follows:

[0102] (31)

[0103] (4) Construction of regulatory rules;

[0104] Based on the optimization results of the long-term multi-objective long-term time-series optimization scheduling model for water, wind and solar power constructed in step (3), the initial reservoir capacity, runoff, and wind and solar power output of the current month are used as inputs, and any variable such as hydropower output, reservoir capacity at the end of the current month, or discharge is used as output. The BP neural network is used to fit the data month by month, and the monthly scheduling function is determined based on the fitting results. The lower and upper boundaries of the monthly reservoir capacity process in the optimization results of the long-term multi-objective long-term time-series optimization scheduling model for water, wind and solar power in step (3) are used to calculate the energy storage capacity monthly control interval of the cascade energy storage, forming the energy storage scheduling boundary. The energy storage calculation method is as follows:

[0105] (32)

[0106] (33)

[0107] This invention was verified using a hydro-wind-solar hybrid system consisting of two large reservoirs and surrounding wind and solar power stations in a river basin in southwestern my country. The specific configuration is as follows: the total installed capacity of hydropower is 10.05 million kilowatts, and the supporting installed capacity of wind and solar power is 6.5 million kilowatts. Based on statistical analysis of actual power output data, initial parameters... Set to 10 hours.

[0108] Two rules (including scheduling functions and energy storage intervals) were simulated using validation data from 2011 to 2020 to verify and evaluate the superiority of the proposed rule. The regulation rule proposed in this invention is denoted as Rule 1, and the comparison rule is denoted as Rule 2. The comparative analysis mainly focuses on the performance of these regulation rules, with an emphasis on their ability to generate electricity in the long term and meet the short-term flexibility requirements of the power grid.

[0109] (1) Long-term benefits:

[0110] Regarding long-term power generation performance, Rule 1 proposed in this invention provides bidirectional correction to the decision based on the monthly scheduling function by introducing an energy storage control interval. Specifically, the correction logic is as follows: when the calculated energy storage exceeds the upper limit of the interval, the upper limit is used as the constraint target; when it is below the lower limit, the lower limit is used as the standard. To achieve the energy storage control target, the system adjusts the outflow from the cascade reservoirs proportionally to increase or decrease the energy storage.

[0111] Table 1 compares the long-term simulation results under different control rules. Although Rule 1 incorporates short-term peak-shaving constraints in its short-term time-series simulation optimization model, its total long-term power generation still reaches 52.77 billion kWh, a decrease of only 225 million kWh compared to Rule 2, which does not consider this constraint. This small difference indicates that Rule 1, while introducing short-term flexibility, can still effectively guarantee the long-term power generation benefits of the system.

[0112] Further comparison of the two regulation rules reveals that Rule 2 aims to maximize power generation during the flood season, but this results in a relatively high amount of wasted hydropower, amounting to 207 million kWh; while Rule 1, through balanced optimization, only generates 39 million kWh of wasted power, demonstrating superior resource utilization efficiency. Simulation results based on a long series of hydrological data from 1954 to 2010 show that ( Figure 3 The multi-year average monthly storage capacity and output processes of both control rules are highly consistent with the deterministic optimization results, verifying the good execution capability of the proposed rules for global optimization.

[0113] In addition, from Figure 3 It can be seen that the simulation results of Rule 1 show a more strategic energy storage drawdown process, especially during the dry season from November to June of the following year, where increased energy storage release significantly boosted power generation during this period. This "dry season increase and flood season decrease" power redistribution pattern makes the system's annual output process more stable under the guidance of Rule 1, proving its effectiveness in coordinating long-term power generation benefits with short-term regulation needs.

[0114] Table 1 System power generation and curtailment under different control rules

[0115]

[0116] (2) Grid flexibility demand response capability:

[0117] To assess the responsiveness of control rules to dynamic grid demand, Figure 4-5 The monthly power output distribution of the two rules was compared during the ten-year verification period. The results showed that Rule 2 frequently exceeded the boundary limit in terms of power output from June to November, with monthly exceedance probabilities of 10%, 40%, 70%, 30%, 10%, and 30%, respectively. The overall exceedance rate for the year was as high as 15%, indicating a serious mismatch between its monthly decision-making and the short-term peak-shaving requirements of the power grid.

[0118] Conversely, rule 1 ( Figure 5 The power output distribution has been significantly improved. Although there were still a few instances of exceeding the limit in August (10%) and September (30%), the situation of output falling below the lower limit has been completely avoided, and the overall over-limit rate for the year has dropped significantly to 3%. This comparison fully demonstrates that Rule 1 can more reliably meet the grid's short-term demand for minimum daily power supply and basic peak-shaving capacity.

[0119] (3) Comparison of short-term peak-shaving power:

[0120] Figure 6 The performance differences between the two rules were further quantified from the perspective of peak-shaving power. Except for May and December, when it was slightly lower than Rule 2, Rule 1 significantly led in peak-shaving power contribution in all other months. Figure 6(b) Among them, November's advantage is most prominent, exceeding Rule 2 by 1.04 billion kWh. In terms of annual totals, Rule 1 and Rule 2 have cumulative peak-shaving power of 20.95 billion kWh and 16.09 billion kWh respectively, with Rule 1's peak-shaving power increasing by 30.2% compared to Rule 2. Figure 6 c).

[0121] This significant improvement stems from Rule 1's optimized strategy for annual power allocation: by increasing power generation during the dry season (when grid peak-shaving demand is typically high) and appropriately suppressing it during the flood season, the system's power output better aligns with the grid's intraday regulation needs. For example... Figure 7 As shown, the power grid faces high peak-shaving pressure from March to June, while Rule 1, because it incorporates dynamic peak-shaving demand in the modeling stage, exhibits superior short-term regulation performance during this period and throughout the year.

[0122] This application also provides a computer device, specifically a computer, server, network device, etc. The computer device includes a bus, processor, memory, and communication interface, and may also include input / output interfaces and a display device. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores location information. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the various method embodiments.

[0123] Those skilled in the art will understand that the structure of the computer device described above is only a partial structure related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. A specific computer device may include more or fewer components, or combine certain components, or have different component arrangements.

[0124] In one embodiment, a computer-readable storage medium is also provided, which may be non-volatile or volatile, and a computer program is stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0125] In one embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0126] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0127] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0128] Any references to memory, database, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc.

[0129] Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can take many forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0130] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

Claims

1. A method for constructing watershed hydro-wind-solar complementary regulation rules to enhance short-term grid flexibility, characterized in that, Includes the following steps: (1) Characterize the short-term flexibility requirements of the power grid; For a specific month, based on the typical daily peak and off-peak output process of a hydropower station, considering the regulation capacity... and adjust duration Quantify the minimum daily peak-shaving power demand of the power grid in that month. and minimum daily electricity demand as follows: (1) (2) (3) In the formula, These are the peak and off-peak outputs of a typical daily peak-valley power station. (2) Construct a short-time simulation optimization model to obtain the monthly peak-shaving function and the monthly hydropower boundary that meets the short-term flexibility requirements of the power grid; (3) Construct a long-term, multi-objective, long-time-series optimization scheduling model for water, wind, and solar power; (4) Construct water-wind-solar complementary regulation rules for the basin, including monthly scheduling functions and monthly regulation intervals for cascade energy storage; In step (2), the objective function for constructing the short-time simulation optimization model is as follows: (4) (5) In the formula, The target value for peak-shaving power; This is a collection of the number of days in that month that have been spent working diligently and beautifully over many years. The number of scenic scenes representing typical intraday power output processes; This represents the probability of corresponding landscape scenes; Indicates the length of the set of days; For the first Daytime and scenic scenes Peak-shaving power consumption; This is the set of time periods corresponding to the peak output of a typical daily peak and trough power output process of a hydropower station in that month. For the first Daytime and scenic scenes Within the next day The power output of the hydro-wind-solar hybrid system at any given moment; For the first Daytime and scenic scenes Average output of the hydro-wind-solar hybrid system during the day's off-peak hours.

2. The method for constructing watershed hydro-wind-solar complementary regulation rules to enhance short-term grid flexibility support as described in claim 1, characterized in that, In step (2), the monthly peak-shaving function and the monthly hydropower boundary that meets the short-term flexibility requirements of the power grid are simulated in two steps. The constraints include the corresponding constraints of the monthly peak-shaving function relationship simulation and the corresponding constraints of the monthly hydropower boundary simulation. 2.1) Simulate the corresponding constraints of the monthly peak-shaving function relationship; Hydropower and wind / solar power output at different time scales need to satisfy multi-scale power conservation constraints, as follows: (6) (7) In the formula, It is the collection of the number of days in that month that have been glorious and productive over many years. The Middle The set of days in a year; It is the set of all months. ; Provide power to the hydropower station on a monthly scale; For the scenery in the first daily effort; and Hydropower and wind power were respectively ranked in the first place. Daytime and scenic scenes Within the next day Efforts made at all times; It is a collection of time periods during which the hydropower station typically experiences peak and off-peak output within a single day during that month. 2.2) Constraints corresponding to the monthly hydropower generation boundary simulation; The daily power generation and peak-shaving power generation of the hydro-wind-solar hybrid system within a month need to meet the short-term flexibility requirements of the power grid. The minimum daily peak-shaving power generation requirement obtained in step (1) is introduced. and minimum daily electricity demand The constraints are as follows: (8) (9) (10) In the formula, The duration of a time period is expressed in hours. 2.3) During the two simulations, the following steps were followed: First, from the minimum hydropower output of 0 to the maximum hydropower output... Discretization is performed between the two to obtain the hydropower output sequence. And based on the hydropower output value PH in the hydropower output sequence m The monthly output of the hydropower station in equation (6) is replaced sequentially. Repeated optimization calculations are performed. In the simulation of monthly peak-shaving function relationships: collect the peak-shaving power target values ​​obtained from each optimization of the short-time series simulation optimization model. Therefore, based on the hydropower output sequence and peak-shaving power target value The peak-shaving curves of the hydro-wind-solar hybrid system were plotted for each month, and the peak-shaving curves were fitted to the peak-shaving functions for each month. After monthly simulation calculations throughout the year, the monthly peak-shaving functions reflecting the changes throughout the year were obtained. ; In the monthly hydropower output boundary simulation: if a certain hydropower output value in the hydropower output sequence If no solution is found during the optimization process, then the hydropower output value is considered to be... Not feasible; collect all solvable hydropower output values ​​to determine the feasible boundary interval for hydropower output in that month; through simulation calculations for each month of the year, finally obtain the monthly boundary interval for hydropower output throughout the year. .

3. The method for constructing watershed hydro-wind-solar complementary regulation rules to enhance short-term grid flexibility as described in claim 2, characterized in that, Step (3) is as follows: 3.1) The objective function is as follows: (11) (12) In the formula, The total power generation of the hydro-wind-solar hybrid system during the entire dispatch cycle; A collection of hydroelectric power stations; It is a set of long-time scheduling periods; For hydroelectric power station exist Monthly output; For the scenery Monthly output; Duration in months; The peak-shaving power for the entire scheduling cycle of the hydro-wind-solar hybrid system; for Monthly peak adjustment function; 3.2) The constraints are as follows: (13) In the formula, and They are respectively The lower and upper boundaries of the monthly hydropower output are the monthly hydropower output boundary intervals obtained in step (2).

4. The method for constructing watershed hydro-wind-solar complementary regulation rules to enhance short-term grid flexibility support according to claim 3, characterized in that, Step (4) is as follows: Based on the optimization results of the long-term multi-objective long-term time-series optimization scheduling model for water, wind and solar power constructed in step (3), the initial reservoir capacity, runoff, and wind and solar power output of the current month are used as inputs, and any variable such as hydropower output, reservoir capacity at the end of the current month, or discharge is used as output. The BP neural network is used to fit the data month by month, and the monthly scheduling function is determined based on the fitting results. The lower and upper boundaries of the monthly reservoir capacity process in the optimization results of the long-term multi-objective long-term time-series optimization scheduling model for water, wind and solar power in step (3) are used to calculate the energy storage capacity monthly control interval of the cascade energy storage, forming the energy storage scheduling boundary. The energy storage calculation method is as follows: (14) (15) In the formula, For hydroelectric power station exist Monthly energy storage; For hydroelectric power station exist Monthly storage capacity; For hydroelectric power station A collection of upstream hydropower stations; For hydroelectric power station exist Average water consumption rate for the month; It is a cascade hydropower station group in Total energy storage for the month.

5. A system for constructing watershed hydro-wind-solar complementary regulation rules to enhance short-term grid flexibility, characterized in that: The system is constructed using the method for constructing watershed hydro-wind-solar complementary regulation rules to enhance short-term grid flexibility as described in any one of claims 1 to 4. The system includes: The grid short-term flexibility demand module is used to calculate the minimum daily peak-shaving power demand and minimum daily power demand of the power grid within a month, based on the typical intraday peak and off-peak output process of hydropower. The grid short-term flexibility demand module quantifies the grid's regulation capacity and regulation duration requirements for hydro-wind-solar hybrid systems by introducing a set of indicators for regulation capacity and regulation duration. It also calculates peak-shaving power and basic power through regulation capacity and regulation duration, thereby quantifying the grid's minimum daily peak-shaving power demand and daily power demand within a month. The short-time series simulation optimization module is used to construct a short-time series simulation optimization model under the constraint of the grid short-term flexibility demand module, which considers the typical intraday output scenarios of multiple consecutive days and multiple wind and solar scenarios within a month. It realizes the simulation of monthly peak-shaving function relationship and monthly hydropower power boundary simulation. By taking the discrete elements of the hydropower output interval as input, repeated simulation optimization calculations are performed to obtain the monthly peak-shaving function and the monthly hydropower power boundary that meets the grid short-term flexibility demand. The long-term multi-objective long-time series optimization scheduling module for water, wind and solar power is used to integrate the output results of the short-time series simulation optimization module. It introduces two objectives: maximizing the power generation of the water, wind and solar complementary system during the entire scheduling cycle and maximizing the peak power of the water, wind and solar complementary system during the entire scheduling cycle. It constructs a long-term multi-objective long-time series optimization scheduling model for water, wind and solar power to solve for the optimal multi-year monthly scheduling results of the watershed water, wind and solar complementary system at the long-time series scale. The watershed hydro-wind-solar complementary control module utilizes the optimal multi-year monthly scheduling results obtained from the long-term multi-objective long-time series optimization scheduling module for water, wind, and solar power, and introduces a BP neural network to fit the monthly scheduling function of each cascade hydropower station. At the same time, based on the upper and lower boundaries of the hydropower output of the monthly optimal reservoir capacity process set obtained from the long-term multi-objective long-time series optimization scheduling module for water, wind, and solar power, the monthly control interval of cascade energy storage is calculated to achieve long-term optimal scheduling of the watershed hydro-wind-solar complementary system.

6. A computer device, characterized in that, The computer device includes a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, it implements the basin-wide water-wind-solar complementary regulation rule construction system for improving short-term grid flexibility as described in claim 5.

7. A storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the basin-wide water-wind-solar complementary regulation rule construction system for improving short-term grid flexibility as described in claim 5.

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