Hydroelectric power generation capacity optimization method and system based on incoming water characteristics of dry-flood-dry three stages

Abstract

The invention discloses a hydropower generating capacity optimization method and system based on the incoming water characteristics of the dry-flood-dry three stages, and is used for solving the technical problem that the existing hydropower generating capacity optimization method does not fully consider the core characteristics of annual regulation and a hydropower station above, so that the accuracy of system balance and new energy consumption results is low. The method comprises the following steps: acquiring drainage basin incoming water characteristic data, a new energy annual hour time sequence power generation curve and a power system annual hour time sequence load curve; calculating a power system net load curve according to the last two types of curves; dividing a first dry period stage, a flood period stage and a second dry period stage based on drainage basin incoming water characteristic data, and calculating total electric quantity fixed values of annual regulation and above hydropower stations in the power system in each stage; then combining the net load curve and the total electric quantity fixed value of each stage to construct a hydroelectric generating capacity optimization model; and finally, optimizing the hydropower generating capacity based on the model, and generating an annual electric power and electric quantity balance optimization result and a new energy consumption result.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, and in particular to a method and system for optimizing hydropower generation based on the inflow characteristics of the three stages of dry season, flood season, and dry season. Background Technology

[0002] When conducting annual power generation analysis, it is necessary to comprehensively consider the technical and economic parameters of hydropower, coal power, gas power, nuclear power, wind power, photovoltaic power, pumped storage, and energy storage, while also taking into account constraints such as power transmission and reception, cross-sectional area, and network, as well as load and reserve requirements, to ultimately obtain the system balance and renewable energy consumption results. For power systems with a large installed hydropower capacity, the rationality of hydropower operation is crucial to ensuring power supply and renewable energy consumption.

[0003] Hydropower stations utilize reservoirs to regulate and redistribute natural runoff to meet needs such as power generation and water supply. Based on the length of the regulation cycle, hydropower station runoff regulation can be categorized into runoff-based, daily, weekly, monthly, quarterly, annual, and multi-year regulation. A longer regulation cycle indicates a stronger ability to regulate runoff, i.e., better regulation performance. Reservoirs with longer regulation cycles also possess regulation capabilities with shorter cycles. Hydropower stations with longer regulation cycles generally have better energy and economic benefits.

[0004] Among existing methods for optimizing hydropower generation, power balance analysis is a widely used core approach. It performs calculations by integrating various power parameters and system constraints. However, the model used in this technique only focuses on the fixed constraints of monthly power generation and does not fully consider the core characteristics of hydropower stations with annual regulation or above. This neglect of this key regulation performance makes it impossible for hydropower output to be optimized and adapted across months according to the actual water inflow patterns and system load demand, resulting in low accuracy of system balance and new energy consumption results. Summary of the Invention

[0005] This invention provides a method and system for optimizing hydropower generation based on the three-stage water inflow characteristics of dry-flood-dry season. It addresses the technical problem that existing hydropower generation optimization methods do not fully consider the core characteristics of hydropower stations with annual regulation and above, resulting in low accuracy of system balance and new energy consumption results.

[0006] The first aspect of this invention provides a method for optimizing hydropower generation based on the three-stage inflow characteristics of dry-flood-dry season, comprising:

[0007] Acquire data on watershed inflow characteristics, annual hourly time-series power generation curves of new energy sources, and annual hourly time-series load curves of the power system;

[0008] Calculate the net load curve of the power system based on the annual hourly time-series power generation curve of the new energy source and the annual hourly time-series load curve of the power system.

[0009] Based on the watershed inflow characteristics data, the flood and dry seasons are divided, and the first dry season, flood season, and second dry season are output. The total fixed value of annual regulation and above hydropower stations in the power system is calculated for the first dry season, the flood season, and the second dry season, respectively.

[0010] Based on the net load curve of the power system and the fixed total electricity generation of the annual regulating and above hydropower stations in the first dry season, the flood season, and the second dry season, a hydropower generation optimization model is constructed.

[0011] Based on the aforementioned hydropower generation optimization model, hydropower generation is optimized to generate annual power balance optimization results and new energy consumption results.

[0012] Optionally, the step of optimizing hydropower generation based on the hydropower generation optimization model to generate annual power balance optimization results and new energy consumption results includes:

[0013] Solve the hydropower generation optimization model and output the daily hydropower generation data;

[0014] Using the daily hydropower generation data as the boundary, we conduct hourly time-series simulation calculations of the power system throughout the year, and output the annual power balance optimization results and new energy consumption results.

[0015] Optionally, the hydropower generation optimization model includes an objective function and hydropower optimization constraints.

[0016] Optionally, the objective function is specifically:

[0017] ;

[0018] in, Let be the net load value of the power system in time period t, and let represent the net load curve of the power system. The power generation value of the nth annual regulating or above hydropower station during time period t includes the fixed total power generation value of the annual regulating or above hydropower station during the first dry season, the flood season, and the second dry season.

[0019] Optionally, the hydropower optimization constraints include the expected output constraint of the hydropower station, the forced output constraint of the hydropower station, the three-stage power constraint of the reservoir of the annual regulating and above hydropower station, the monthly power adjustment constraint, the output constraint of the hydropower unit, and the sum of the output of the hydropower units constraint.

[0020] Optionally, the formula for calculating the net load curve of the power system is as follows:

[0021] ;

[0022] in, Let be the net load value of the power system in time period t, and let represent the net load curve of the power system. Let be the original load value of the power system in time period t, and let represent the hourly time-series load curve of the power system throughout the year; This represents the wind power generation value during time period t in the annual hourly time-series power generation curve of new energy sources. This represents the photovoltaic power generation value during time period t in the annual hourly time-series power generation curve of new energy.

[0023] The second aspect of this invention provides a hydropower generation optimization system based on the three-stage inflow characteristics of dry-flood-dry seasons, comprising:

[0024] The acquisition module is used to acquire watershed inflow characteristic data, annual hourly time-series power generation curves of new energy sources, and annual hourly time-series load curves of the power system.

[0025] The first calculation module is used to calculate the net load curve of the power system based on the annual hourly time-series power generation curve of the new energy source and the annual hourly time-series load curve of the power system.

[0026] The second calculation module is used to divide the flood and dry seasons based on the watershed inflow characteristic data, output the first dry season stage, the flood season stage, and the second dry season stage, and calculate the fixed total electricity generation of the annual regulating and above hydropower stations in the power system during the first dry season stage, the flood season stage, and the second dry season stage, respectively.

[0027] The construction module is used to construct a hydropower generation optimization model based on the net load curve of the power system and the fixed total power generation of the annual regulating and above hydropower stations in the first dry season, the flood season, and the second dry season, respectively.

[0028] The generation module is used to optimize hydropower generation based on the hydropower generation optimization model, and generate annual power balance optimization results and new energy consumption results.

[0029] A computer device provided by a third aspect of the present invention includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor causes the processor to perform the steps of the hydropower generation optimization method based on the three-stage water inflow characteristics of dry-flood-dry season as described in any of the preceding claims.

[0030] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the steps of the hydropower generation optimization method based on the three-stage inflow characteristics of dry-flood-dry season as described in any of the preceding claims.

[0031] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the steps of the hydropower generation optimization method based on the three-stage inflow characteristics of dry-flood-dry season as described in any of the preceding claims.

[0032] As can be seen from the above technical solutions, the present invention has the following advantages:

[0033] The above-mentioned technical solution of the present invention provides a method for optimizing hydropower generation based on the three-stage water inflow characteristics of dry-flood-dry season. This method acquires water inflow characteristic data of the basin, annual hourly time-series power generation curves of new energy sources, and annual hourly time-series load curves of the power system. Based on these curves, the net load curve of the power system is calculated. The basin inflow characteristic data is used to divide the flood and dry seasons, outputting the first dry season stage, the flood season stage, and the second dry season stage. The total fixed power generation of annual regulating and above-level hydropower stations in the power system is calculated for each of the three dry season stages. Based on the power system's net load curve and the total fixed power generation of annual regulating and above-level hydropower stations, a hydropower generation optimization model is constructed. Based on this model, hydropower generation is optimized to generate annual power balance optimization results and new energy consumption results. Based on the above scheme, this invention obtains watershed inflow characteristic data to divide the watershed into three stages: "first dry season - flood season - second dry season," calculates the fixed total power output of annual regulating and above-level hydropower stations in the power system for each stage, and calculates the net load curve by combining the annual hourly time-series load curve of new energy and the power system. It constructs a hydropower generation optimization model with "fixed total power output in three stages + monthly power balance" as its core, replacing the rigid monthly power output limit of traditional hydropower models. This allows hydropower output to be flexibly adjusted according to changes in watershed inflow and to match system load fluctuations based on the net load curve. It effectively fills the technical gap in existing methods that ignore the monthly power allocation capabilities of annual regulating and above-level hydropower stations. Finally, through model optimization, it generates annual power balance optimization results and new energy consumption results, significantly improving the accuracy of power system balance calculations, thereby improving the accuracy of system balance and new energy consumption results, and making the consumption results of new energy sources such as wind power and photovoltaics more consistent with actual operating scenarios. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart illustrating the steps of a hydropower generation optimization method based on the three-stage water inflow characteristics of dry season, flood season, and dry season, as provided in Embodiment 1 of the present invention.

[0036] Figure 2 This is a flowchart illustrating a method for optimizing hydropower generation based on the three-stage water inflow characteristics of dry season, flood season, and dry season, provided in Embodiment 1 of the present invention.

[0037] Figure 3 This is a structural block diagram of a hydropower generation optimization system based on the three-stage water inflow characteristics of dry season, flood season, and dry season, provided in Embodiment 2 of the present invention. Detailed Implementation

[0038] This invention provides a method and system for optimizing hydropower generation based on the three-stage water inflow characteristics of dry-flood-dry season. This method addresses the technical problem that existing hydropower generation optimization methods do not fully consider the core characteristics of annual regulation and above hydropower stations, resulting in low accuracy of system balance and new energy consumption results.

[0039] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0040] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a method for optimizing hydropower generation based on the three-stage water inflow characteristics of dry season, flood season, and dry season, as provided in Embodiment 1 of the present invention.

[0041] This invention provides a method for optimizing hydropower generation based on the three-stage inflow characteristics of dry-flood-dry season, comprising:

[0042] Step 101: Obtain water inflow characteristics data, annual hourly time-series power generation curves of new energy sources, and annual hourly time-series load curves of the power system.

[0043] Watershed inflow characteristic data is a dataset that reflects the spatiotemporal distribution patterns of natural water resources within a specific watershed. Its core information includes monthly runoff, start and end times of the flood / dry season, water abundance / dryness levels, and reservoir inflow / outflow. It is mainly used to determine the annual water inflow variation patterns of the watershed and is the core basis for the "dry-flood-dry" three-stage division.

[0044] The annual hourly time-series power generation curves for new energy sources are continuous time-series data curves recording the hourly power generation capacity of wind power, photovoltaic, and other new energy power plants within an 8760-hour period throughout the year. The data comes from the wind and solar resource simulation system and is used to deduct the self-generated power of new energy sources when calculating the net load of the system. Among them, the annual hourly time-series power generation curves for new energy sources include the annual hourly time-series power generation curves for wind power (i.e., the wind power 8760-hour time-series power generation curve) and the annual hourly time-series power generation curves for photovoltaics (i.e., the photovoltaic 8760-hour time-series power generation curve).

[0045] The annual hourly time-series load curve of the power system (i.e., the annual 8760-hour time-series load curve of the power system) is a continuous time-series data curve that records the total hourly power demand of the power system (including various user loads and external loads) within 8760 hours throughout the year. It is provided by the power dispatching agency and is the basic power demand data for calculating the net load of the system and matching hydropower output.

[0046] It should be noted that, to support the subsequent three-stage division of "dry-flood-dry" periods, the calculation of the system's net load curve, and the construction of a hydropower generation optimization model, three types of core basic data are required: First, watershed inflow characteristic data, which mainly comes from continuous time-series monitoring data for the past 10 years or more provided by the watershed management agency (including monthly natural runoff, records of the start and end of the flood season, and water abundance / dryness level assessment reports), as well as operational records such as reservoir inflow and outflow archived by hydropower station operation and maintenance units. By integrating these data, the spatiotemporal distribution pattern of watershed inflow throughout the year can be accurately reflected, providing a basis for the three-stage division; Second, the annual hourly time-series power generation curve of new energy sources, which is obtained through a wind and solar resource simulation system and covers wind power and photovoltaic power. The power generation data for the power station is collected for 8760 hours throughout the year. After acquisition, abnormal data caused by equipment failure and extreme weather (such as wind power outages with zero output and photovoltaic power exceeding rated power) must be removed to ensure the authenticity and continuity of the data, providing a benchmark for renewable energy output for subsequent net load calculations. Finally, the hourly time-series load curve of the power system throughout the year is collected. This curve is generated by the load management system of the power dispatching agency, recording the total hourly electricity demand of the system (including various types of loads such as residential, industrial, and commercial loads) for 8760 hours throughout the year. It must also include the system's external load data (because the external demand needs to be deducted for subsequent net load calculations). After acquisition, the data integrity must be verified to ensure that there is no missing or incomplete data, laying a data foundation for the smooth progress of subsequent steps.

[0047] Step 102: Calculate the net load curve of the power system based on the annual hourly time-series power generation curve of new energy sources and the annual hourly time-series load curve of the power system.

[0048] The net load curve of the power system refers to the continuous time-series data curve that reflects the actual load demand of the power system in each period of the 8760 hours of the year, after deducting the self-generated output of new energy sources (wind power, photovoltaic, etc.). Its core function is to provide a "matching load benchmark" for optimizing the output of power sources such as hydropower. The data unit is usually MW.

[0049] It should be noted that, firstly, the core calculation logic of net load should be clarified: the system net load should reflect "the actual load demand of the system after deducting the self-generated power of new energy sources, which requires supplementation from other power sources such as hydropower and coal power." Therefore, a time-by-time calculation method is adopted. For each time period t within the 8760 hours of the year, the formula "System Net Load (t) = Power System Annual Hourly Time-Series Load Curve (t) - New Energy Annual Hourly Time-Series Generation Curve (t)" is used for calculation. Among them, the new energy annual hourly time-series generation curve (t) needs to be combined with the total power generation of wind power and photovoltaic power in that time period (if the two types of new energy curves are recorded separately, they need to be summed first to obtain the new energy power generation curve for that time period). (Total energy output); During the calculation process, the rationality of the data needs to be verified simultaneously. If the calculation result is negative for a certain period (i.e., the output of new energy exceeds the system load), it is necessary to confirm whether it is a normal phenomenon during the load trough period (such as when photovoltaic power is suspended at night or wind power output is high) in combination with the actual scenario. If it is a data error, the original curve data needs to be backtracked and corrected to ensure that the net load value of each period truly reflects the supply and demand gap of the system. After the calculation of all periods is completed, the continuous 8760-hour time-series net load curve of the power system throughout the year is obtained. This curve will be directly used in the subsequent hydropower generation optimization model as the core basis for matching the trend of hydropower output and system load changes.

[0050] The formula for calculating the net load curve of the power system is as follows:

[0051] ;

[0052] In the formula, Let be the net load value of the power system in time period t, and let represent the net load curve of the power system. Let be the original load value of the power system in time period t, and let represent the hourly time-series load curve of the power system throughout the year; This represents the wind power generation value during time period t in the annual hourly time-series power generation curve of new energy sources. This represents the photovoltaic power generation value during time period t in the annual hourly time-series power generation curve of new energy.

[0053] Step 103: Divide the flood and dry seasons based on the watershed inflow characteristics data, output the first dry season stage, the flood season stage, and the second dry season stage, and calculate the total fixed value of the annual regulation and above hydropower stations in the power system in the first dry season stage, the flood season stage, and the second dry season stage respectively.

[0054] The three stages of the flood and dry seasons: Based on the characteristics of watershed inflow, the three time periods of the year include the first dry season (months with low runoff and stable inflow before the flood season), the flood season (months with concentrated and high runoff), and the second dry season (months with declining runoff after the flood season). The division needs to match the actual inflow patterns and flood control needs, and it is the time boundary for the phased power allocation of hydropower stations.

[0055] Hydropower stations with an annual regulation period of ≥1 year: refers to hydropower stations with a reservoir regulation period of ≥1 year, including annual regulation hydropower stations (regulation period of 1 year, which can store flood season water for power generation during the dry season) and multi-year regulation hydropower stations (regulation period of >1 year, which can allocate water across years). The core feature is that they have the ability to exchange electricity across months / years, and the total electricity for each stage needs to be calculated separately.

[0056] Fixed total power generation per stage: refers to the fixed total power generation that a hydropower station with annual regulation or above needs to complete in each of the three stages of the flood and dry seasons. It is calculated by combining the total designed power generation of the hydropower station in that year with the "stage power generation ratio" (adjusted according to the abundance or scarcity of water inflow), and must meet the constraints of safe reservoir operation. It is a key constraint parameter of the hydropower generation optimization model.

[0057] It should be noted that the core idea of ​​this invention is to "limit the annual hydropower output of reservoirs of hydropower stations with regulation and above in stages". Based on the traditional three-stage output of hydropower, the 12 months of the year are divided into three stages according to the water inflow of the basin: the first dry season (January-May), the flood season (June-October), and the second dry season (November-December). The sum of hydropower output in each stage remains unchanged, and a variable range of power output is set for each month (the specific value is determined according to the dispatching experience, and the range is appropriately reduced during the flood season). Hydropower generation is arranged according to the principle of peak shaving.

[0058] Specifically, based on the acquired watershed inflow characteristics data (including the average monthly runoff over the past 10 years, historical flood season start and end records, water abundance / dryness levels, and reservoir inflow data), the three-stage division of the flood and dry seasons is first carried out: First, the proportion of monthly runoff in the watershed to the total annual runoff is calculated, and months with continuous concentrated runoff and a cumulative proportion exceeding 60% are classified as the flood season (e.g., the monthly runoff proportion from June to October exceeds 10% and reaches a cumulative 65%, then it is determined as the flood season). Then, months with low runoff and stable inflow before the flood season are classified as the first dry season, and months with declining runoff after the flood season are classified as the second dry season. After the division, it needs to be verified in conjunction with historical rainstorm records and reservoir flood control scheduling requirements (e.g., ensuring that the flood season includes the main flood season and that the dry season avoids the reservoir water replenishment period). Finally, the specific monthly ranges of the first dry season stage, the flood season stage, and the second dry season stage are output. After completing the three-stage division, select hydropower stations with annual regulation or above in the power system: based on the "regulation cycle" (the regulation cycle of annual regulating hydropower stations is 1 year, and the regulation cycle of multi-year regulating hydropower stations is > 1 year) and the "ratio of total reservoir capacity to beneficial reservoir capacity" in the hydropower station design data, exclude run-of-river, daily / weekly / monthly regulating hydropower stations that do not have cross-month regulation capabilities, and determine the list of hydropower stations to be included in the calculation. Then, the fixed value of the total power generation of these hydropower stations in each stage is calculated: First, the average annual power generation of the hydropower station in the past 5 years and the actual power generation ratio of each stage are obtained. Combined with the adjustment ratio of the water level in the basin in the current year (high water year, normal water year, low water year) (e.g., the power generation ratio during the flood season in the high water year is increased to 60%, and decreased to 50% in the low water year), then according to the total designed power generation of the hydropower station given by the power grid planning department in the current year, the fixed value of the total power generation in each stage is calculated according to "fixed value of total power generation in each stage = total designed power generation in the current year × power generation ratio in each stage". At the same time, it is verified whether the value meets the reservoir safety constraints (e.g., the total power generation during the flood season needs to match the flood limit water level requirements to avoid excessive water storage). Finally, the fixed value of the total power generation of the annual regulation and above hydropower stations in each of the three stages is determined, which provides the basis for stage power generation constraints for the subsequent construction of the hydropower generation optimization model.

[0059] Step 104: Based on the net load curve of the power system and the fixed total power generation values ​​of annual regulating and above hydropower stations in the first dry season, flood season, and second dry season respectively, construct a hydropower generation optimization model.

[0060] The hydropower generation optimization model includes an objective function and hydropower optimization constraints. The hydropower optimization constraints include the expected output limit constraints of the hydropower station, the forced output limit constraints of the hydropower station, the three-stage power generation constraints of the reservoir of hydropower stations with annual regulation and above, the monthly power generation adjustment limit constraints, the power generation limit constraints of hydropower units, and the sum of power generation constraints of hydropower units.

[0061] It should be noted that, in order to scientifically generate the daily power generation boundary of hydropower stations for the 8760-hour time-series operation simulation calculation of the power system throughout the year, it is necessary to ensure that the daily power generation variation trend of hydropower stations is consistent with the daily load variation trend, and to consider the impact of hydropower maintenance during the dry season. Therefore, a hydropower generation optimization model is constructed:

[0062] 1) Objective Function: If the load power is relatively large, the allocated hydropower generation will be relatively large; if the load power is relatively small, the allocated hydropower generation will be relatively small. Therefore, the following mathematical expression for the objective function is constructed:

[0063] ;

[0064] in, Let be the net load value of the power system in time period t, and let represent the net load curve of the power system. The power generation value of the nth annual regulating or above hydropower station in time period t includes the fixed total power generation value of the annual regulating or above hydropower station in the first dry season, flood season, and second dry season respectively; N is the number of annual regulating or above hydropower stations.

[0065] 2) Constraints on the expected output of the hydropower station:

[0066] ;

[0067] In the formula, The expected output of the nth annual regulating or higher hydropower station during time period t.

[0068] 3) Forced output constraint conditions for hydropower stations:

[0069] ;

[0070] In the formula, The forced output of the nth annual regulating or higher hydropower station during time period t.

[0071] 4) Three-stage power generation constraints for reservoirs of hydropower stations with annual regulation capacity and above:

[0072] ;

[0073] In the formula, The power generation limit for the nth annual regulating or above hydropower station in the mth month; The index for the starting time period of month m; The index for the end period of month m; The power generation value of the nth annual regulating and above hydropower station during period k includes the fixed total power generation value of the annual regulating and above hydropower station during the first dry season, the flood season, and the second dry season, respectively; N is the number of annual regulating and above hydropower stations. Let i be the set of months for stage i, divided into the first dry season (January-May), the flood season (June-October), and the second dry season (November-December).

[0074] 5) Monthly electricity consumption adjustment restrictions and constraints:

[0075] ;

[0076] in, , This represents the adjustment range of power generation for the nth annual regulating or above hydropower station in the mth month.

[0077] 6) Output constraints of hydropower units (considering maintenance schedules):

[0078] ;

[0079] in, These represent the minimum and maximum technical outputs of the j-th hydropower unit during time period t, respectively. Let j be the variable for the maintenance of the j-th hydropower unit. This indicates that maintenance is not required. This indicates maintenance.

[0080] 7) Constraints on the sum of the output of hydropower units:

[0081] ;

[0082] in, The number of generating units in the nth hydropower station with an annual regulation capacity or above; The technical output of the j-th hydropower unit during time period t.

[0083] Step 105: Optimize hydropower generation based on the hydropower generation optimization model to generate annual power balance optimization results and new energy consumption results.

[0084] The annual power balance optimization results refer to the set of results obtained through 8760 hours of time-series simulation, reflecting the matching of power supply and consumption throughout the year. Core information includes time-period output allocation of each power source, power shortage / surplus statistics, and reserve fulfillment rate, used to assess the reliability of the system's power supply. The annual power balance optimization results include a time-period power source output allocation table, power shortage / surplus period statistics, and a reserve fulfillment report; these two results are used to guide the annual planning of the power system (such as power source expansion and dispatch strategy adjustments).

[0085] Renewable energy consumption results refer to the statistical results of the actual utilization of new energy sources such as wind power and photovoltaic power. The core indicator is the curtailment rate (and the actual amount of electricity consumed and the theoretical generating capacity), which is used to measure the level of new energy consumption in the power system and guide the optimization of new energy consumption strategies. Renewable energy consumption results include wind power curtailment rate, photovoltaic curtailment rate, and the proportion of electricity consumed by new energy sources.

[0086] Furthermore, step 105 may include the following sub-steps:

[0087] S51. Solve the hydropower generation optimization model and output the daily hydropower generation data.

[0088] S52. Using the daily hydropower generation data as the boundary, conduct hourly time-series simulation calculations of the power system throughout the year, and output the annual power balance optimization results and new energy consumption results.

[0089] It should be noted that, based on the established hydropower generation optimization model (which includes the objective function of "hydropower output matching net load trend" and constraints such as expected output limits, fixed power generation in three stages, and unit maintenance), optimization calculations are carried out using mainstream mathematical solvers such as Gurobi and Cplex. Before solving, the boundary variables in the model (such as the expected output of the hydropower station in each time period and the monthly power generation adjustment range) need to be accurately matched with the actual parameters. At the same time, the solution convergence accuracy (usually 1e-6) is set to ensure the stability of the results. During the solution process, the solver will automatically iterate and verify the compatibility of each constraint (such as avoiding the contradiction of "forced output exceeding the limit during maintenance period"). If there is a conflict, the model parameters will be adjusted backtracking (such as fine-tuning the monthly power generation adjustment range). Finally, the hydropower annual 8760-hour time-series output curve is output, and the daily hydropower generation data is summarized by natural day. Subsequently, using the daily power generation data of this hydropower station as the core boundary, and combining the technical and economic parameters of other power sources in the power system, such as coal-fired power (including start-up and shutdown costs and ramp-up rates), gas-fired power (including peak-shaving response speed), and nuclear power (including baseload output characteristics), as well as constraints such as power transmission and reception plans, cross-sectional transmission limits, and system reserve requirements, a time-series operation simulation of 8760 hours per year was conducted. During the simulation, power output and load demand were matched hourly (e.g., hydropower was prioritized for peak shaving during peak load periods, and wind and solar power were prioritized for consumption during off-peak periods to reduce curtailment). The process verifies the balance relationship of "total power output = total system load + external transmission demand + reserve capacity", and finally generates two types of results: one is the annual power balance optimization results, which includes the output allocation details of different power sources in each period, the time distribution and specific values ​​of power gap / surplus, and the system reserve satisfaction rate; the other is the renewable energy consumption results, which include the actual consumption of wind power and photovoltaic power, theoretical generating capacity, and curtailment rate (curtailment rate = curtailment amount / theoretical generating capacity × 100%). Both types of results provide data support for the annual planning of the power system.

[0090] For comparison of technical effects, existing technologies can be used as a reference. The regulation performance of hydropower station reservoirs is as follows:

[0091] (1) Run-of-river type is an unregulated hydropower station that cannot regulate natural run-of-river.

[0092] (2) Daily regulation is to store the natural water volume that exceeds the power generation needs during low load periods within a day and use it in a concentrated manner during peak load periods to increase the working capacity of the hydropower station, so that the hydropower station can bear the peak load of the power system, which is very beneficial to the high-quality and economical operation of the power system.

[0093] (3) Weekly regulation is to allocate the amount of natural water that is more than the power generation needs on low-load days within a week to high-load days.

[0094] (4) Monthly regulation is to allocate the amount of natural water that is more than the power generation needs during low-load weeks / ten-days of the month to high-load weeks / ten-days.

[0095] (5) Seasonal regulation is to allocate the excess natural water volume that is low-load ten days / months within a quarter to high-load ten days / months for use.

[0096] (6) Annual regulation can store excess water during the flood season in the reservoir to supplement power generation during the dry season.

[0097] Multi-year regulating hydropower stations can allocate excess water from wet years or several consecutive wet years to dry years or several consecutive dry years.

[0098] When conducting annual power balance analysis, hydropower volume serves as the calculation boundary, directly impacting system balance and renewable energy absorption results. The existing workflow involves two steps: First, optimizing hydropower scheduling calculations to refine the annual hydropower generation plan and obtain monthly hydropower volume. Second, conducting annual power balance calculations using a 8760-hour time-series simulation technique. The hydropower model employs monthly projected output, average output, and forced output. The projected and forced outputs represent the maximum and minimum monthly hydropower output, respectively, while the average monthly output represents the monthly electricity consumption. That is, average monthly output × number of hours in the month = monthly hydropower volume. The monthly hydropower volume optimized in the first step serves as the boundary for the balance analysis. Based on this model, annual power balance calculations are performed to analyze system balance and renewable energy absorption. The existing hydropower models in power balance analysis consider monthly power constraints but do not take into account the regulation performance of reservoirs of hydropower stations with annual regulation or above. In other words, they do not consider the monthly power mutual assistance effect of reservoirs of hydropower stations with annual regulation or above on hydropower, which affects the accuracy of system balance and new energy consumption results.

[0099] To address the aforementioned problems, this invention proposes a method for optimizing hydropower generation based on the three-stage inflow characteristics of dry-flood-dry season. This invention considers the regulation performance of reservoirs in hydropower stations with annual regulation capacity and above in its power balance analysis, and considers the monthly mutual assistance of hydropower generation in the hydropower model. Based on the inflow characteristics of the basin, the 12 months of the year are divided into three stages: dry-flood-dry season, generally with the dry season from January to May, the flood season from June to October, and the dry season from November to December. For reservoirs in hydropower stations with annual regulation capacity and above, it is assumed that the total power generation in each stage is fixed, and the monthly power generation within each stage can be coordinated.

[0100] Specifically, please refer to Figure 2 Based on the system's 8760-hour net load curve (load + external transmission demand - wind power availability - solar power availability), and considering the three-stage output of hydropower during the dry season (January-May), flood season (June-October), and low-lying season (November-December), hydropower generation is arranged according to the principle of peak shaving, and then a power balance calculation is carried out. The specific calculation steps are as follows:

[0101] Step (1): Obtain the system's annual 8760 time-series load curve, wind power 8760 time-series power generation curve, and photovoltaic 8760 time-series power generation curve, and calculate the system's net load curve.

[0102] Step (2): Based on the three-stage output of hydropower, calculate the electricity generation of the reservoirs of hydropower stations with annual regulation and above during the three stages of dry season (January-May), flood season (June-October), and dry season (November-December).

[0103] Step (3): Construct an optimization model for hydropower generation.

[0104] Step (4): Optimize the solution and calculate the annual 8760 time-series power output curve of hydropower to obtain the daily power generation of hydropower.

[0105] Step (5): Based on the daily power generation boundary of hydropower, conduct a time-series operation simulation calculation of the system for 8760 hours throughout the year to analyze the annual power balance and the consumption of new energy.

[0106] In summary, this invention considers the reservoir regulation performance of hydropower stations with annual regulation and above in the power balance analysis, and takes into account the monthly power mutual assistance effect of hydropower in the hydropower model. It achieves coordination of hydropower during the three periods of dry season (January-May), flood season (June-October), and dry season (November-December), making the power balance and new energy consumption results of power systems containing large-scale hydropower more accurate.

[0107] In this embodiment of the invention, a method for optimizing hydropower generation based on the three-stage inflow characteristics of dry-flood-dry season is provided. This method acquires basin inflow characteristic data, annual hourly time-series power generation curves of new energy sources, and annual hourly time-series load curves of the power system. Based on these curves, the net load curve of the power system is calculated. The basin inflow characteristic data is used to divide the flood and dry seasons, outputting the first dry season, flood season, and second dry season. The total fixed power generation of annual regulating and above-level hydropower stations in the power system is calculated for each of the three dry seasons. Based on the power system's net load curve and the total fixed power generation of annual regulating and above-level hydropower stations, a hydropower generation optimization model is constructed. Based on this model, hydropower generation is optimized to generate annual power balance optimization results and new energy consumption data. Results: Based on the above scheme, this invention divides the watershed inflow characteristic data into three stages: "first dry season - flood season - second dry season," calculates the fixed total power output of annual regulating and above-level hydropower stations in the power system for each stage, and calculates the net load curve by combining the annual hourly time-series load curve of new energy and the power system. It constructs a hydropower generation optimization model with "fixed total power output in three stages + monthly power balance" as its core, replacing the rigid monthly power output limit of traditional hydropower models. This allows hydropower output to be flexibly adjusted according to changes in watershed inflow and to match system load fluctuations based on the net load curve. It effectively fills the technical gap in existing methods that ignore the monthly power allocation capabilities of annual regulating and above-level hydropower stations. Finally, through model optimization, it generates annual power balance optimization results and new energy consumption results, significantly improving the accuracy of power system balance calculations, thereby improving the accuracy of system balance and new energy consumption results, and making the consumption results of new energy sources such as wind power and photovoltaics more consistent with actual operating scenarios.

[0108] Please see Figure 3 , Figure 3 This is a structural block diagram of a hydropower generation optimization system based on the three-stage water inflow characteristics of dry season, flood season, and dry season, provided in Embodiment 2 of the present invention.

[0109] This invention provides a hydropower generation optimization system based on the three-stage inflow characteristics of dry-flood-dry seasons, comprising:

[0110] The acquisition module 301 is used to acquire watershed inflow characteristic data, annual hourly time-series power generation curves of new energy sources, and annual hourly time-series load curves of the power system.

[0111] The first calculation module 302 is used to calculate the net load curve of the power system based on the annual hourly time-series power generation curve of new energy and the annual hourly time-series load curve of the power system.

[0112] The second calculation module 303 is used to divide the flood and dry seasons based on the watershed inflow characteristic data, output the first dry season stage, the flood season stage, and the second dry season stage, and calculate the total fixed value of the annual regulation and above hydropower stations in the power system in the first dry season stage, the flood season stage, and the second dry season stage respectively.

[0113] Module 304 is used to construct a hydropower generation optimization model based on the net load curve of the power system and the fixed total power generation of annual regulating and above hydropower stations in the first dry season, flood season and second dry season respectively.

[0114] The generation module 305 is used to optimize hydropower generation based on the hydropower generation optimization model, and generate annual power balance optimization results and new energy consumption results.

[0115] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0116] This invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program; when the computer program is executed by the processor, the processor performs the steps of the hydropower generation optimization method based on the three-stage inflow characteristics of dry-flood-dry season as described in any of the above embodiments.

[0117] This invention also provides a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the steps of the hydropower generation optimization method based on the three-stage inflow characteristics of dry-flood-dry season as described in any of the above embodiments.

[0118] This invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the hydropower generation optimization method based on the three-stage inflow characteristics of dry-flood-dry season as described in any of the above embodiments.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0120] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing hydropower generation based on the three-stage inflow characteristics of dry-flood-dry season, characterized in that, include: Acquire data on watershed inflow characteristics, annual hourly time-series power generation curves of new energy sources, and annual hourly time-series load curves of the power system; Calculate the net load curve of the power system based on the annual hourly time-series power generation curve of the new energy source and the annual hourly time-series load curve of the power system. Based on the watershed inflow characteristics data, the flood and dry seasons are divided, and the first dry season, flood season, and second dry season are output. The total fixed value of annual regulation and above hydropower stations in the power system is calculated for the first dry season, the flood season, and the second dry season, respectively. Based on the net load curve of the power system and the fixed total electricity generation of the annual regulating and above hydropower stations in the first dry season, the flood season, and the second dry season, a hydropower generation optimization model is constructed. Based on the aforementioned hydropower generation optimization model, hydropower generation is optimized to generate annual power balance optimization results and new energy consumption results.

2. The method for optimizing hydropower generation based on the three-stage inflow characteristics of dry-flood-dry seasons as described in claim 1, characterized in that, The process of optimizing hydropower generation based on the aforementioned hydropower generation optimization model, generating annual power balance optimization results and renewable energy consumption results, includes: Solve the hydropower generation optimization model and output the daily hydropower generation data; Using the daily hydropower generation data as the boundary, we conduct hourly time-series simulation calculations of the power system throughout the year, and output the annual power balance optimization results and new energy consumption results.

3. The method for optimizing hydropower generation based on the three-stage inflow characteristics of dry-flood-dry seasons as described in claim 1, characterized in that, The hydropower generation optimization model includes an objective function and hydropower optimization constraints.

4. The method for optimizing hydropower generation based on the three-stage inflow characteristics of dry-flood-dry seasons as described in claim 3, characterized in that, The objective function is specifically: ； in, Let be the net load value of the power system in time period t, and let represent the net load curve of the power system. The power generation value of the nth annual regulating or above hydropower station during time period t includes the fixed total power generation value of the annual regulating or above hydropower station during the first dry season, the flood season, and the second dry season.

5. The method for optimizing hydropower generation based on the three-stage inflow characteristics of dry-flood-dry seasons as described in claim 3, characterized in that, The hydropower optimization constraints include the expected output limit constraints of the hydropower station, the forced output limit constraints of the hydropower station, the three-stage power generation constraints of the reservoir of hydropower stations with annual regulation and above, the monthly power generation adjustment limit constraints, the power generation limit constraints of the hydropower units, and the sum of the power generation of the hydropower units limit constraints.

6. The method for optimizing hydropower generation based on the three-stage inflow characteristics of dry-flood-dry seasons as described in claim 1, characterized in that, The formula for calculating the net load curve of the power system is as follows: ； in, Let be the net load value of the power system in time period t, and let represent the net load curve of the power system. Let be the original load value of the power system in time period t, and let represent the hourly time-series load curve of the power system throughout the year; This represents the wind power generation value during time period t in the annual hourly time-series power generation curve of new energy sources. This represents the photovoltaic power generation value during time period t in the annual hourly time-series power generation curve of new energy.

7. A hydropower generation optimization system based on the three-stage water inflow characteristics of dry-flood-dry season, characterized in that, include: The acquisition module is used to acquire watershed inflow characteristic data, annual hourly time-series power generation curves of new energy sources, and annual hourly time-series load curves of the power system. The first calculation module is used to calculate the net load curve of the power system based on the annual hourly time-series power generation curve of the new energy source and the annual hourly time-series load curve of the power system. The second calculation module is used to divide the flood and dry seasons based on the watershed inflow characteristic data, output the first dry season stage, the flood season stage, and the second dry season stage, and calculate the fixed total electricity generation of the annual regulating and above hydropower stations in the power system during the first dry season stage, the flood season stage, and the second dry season stage, respectively. The construction module is used to construct a hydropower generation optimization model based on the net load curve of the power system and the fixed total power generation of the annual regulating and above hydropower stations in the first dry season, the flood season, and the second dry season, respectively. The generation module is used to optimize hydropower generation based on the hydropower generation optimization model, and generate annual power balance optimization results and new energy consumption results.

8. A computer device, characterized in that, The device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the hydropower generation optimization method based on the three-stage inflow characteristics of dry-flood-dry season as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the hydropower generation optimization method based on the three-stage water inflow characteristics of dry-flood-dry periods as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the hydropower generation optimization method based on the three-stage inflow characteristics of dry-flood-dry season as described in any one of claims 1-6.

Images