Optimal evaluation method of water-wind-solar complementary capacity considering active regulation capacity of hydropower

Abstract

The present application belongs to the technical field of water, wind and light complementary ability evaluation, and relates to a water, wind and light complementary ability optimization evaluation method considering the active regulation ability of hydropower. Firstly, a simulation input data set of a water, wind and light multi-energy complementary system is constructed as a basic data sequence for complementary ability evaluation, including selection and generation of a typical reservoir inflow sequence and calculation of a typical wind and light output sequence synchronized with the typical reservoir inflow sequence, and at the same time, fixed physical parameters and dynamic time sequence parameters required by the model are initialized and defined; secondly, a water, wind and light complementary quantitative index considering the active regulation of hydropower is generated by considering the coefficient of variation; then, a water, wind and light complementary ability evaluation simulation optimization method is constructed, with the maximum water, wind and light complementary ability and the minimum water abandonment as objective functions, so as to reduce the water abandonment generated in the active regulation process of hydropower; finally, the hierarchical sequence method is used to realize efficient optimization solution of the multi-objective model.

Description

Technical Field

[0001] This invention belongs to the field of hydro-wind-solar hybrid technology, and relates to an optimization evaluation method for hydro-wind-solar hybrid capabilities that takes into account the active regulation capability of hydropower. Background Technology

[0002] With the large-scale construction of clean energy bases, the installed capacity of wind and solar power generation continues to grow. However, due to the randomness, intermittency, and volatility of meteorological factors such as wind speed, solar irradiance, ambient temperature, and rainfall, wind and solar power generation has inherent uncertainties, which pose a severe challenge to the instantaneous power balance of the power system and the safe and stable operation of the power grid.

[0003] Given the excellent regulation capabilities of hydropower, such as rapid start-up and shutdown and controllable output, and the natural seasonal and intraday complementarity between hydropower, wind power, and solar power, constructing a hydro-wind-solar coordinated operation system has become a key technological path for absorbing large-scale new energy sources and mitigating output fluctuations. Currently, in large river basins such as the Lancang River, the construction of integrated hydro-wind-solar energy bases by incorporating large-scale wind and solar turbines into cascade hydropower station groups has become an industry trend. Against this backdrop, accurately quantifying and evaluating the multi-energy complementarity capabilities of hydropower, wind power, and solar power becomes a prerequisite for determining the optimal bundled capacity and formulating complementary dispatch strategies.

[0004] However, existing technologies for evaluating the capacity of hydro-wind-solar hybridization have the following significant limitations and technical defects:

[0005] Limitations of evaluation dimensions: Existing technologies mostly use correlation coefficients or complementarity indices to characterize the relationships between variables. These methods are usually limited to the complementarity analysis of two-dimensional resources (such as wind-water or light-water), and are difficult to handle complex scenarios involving the coupling of multiple variables such as water, wind, and light.

[0006] Insufficient representation of fluctuation characteristics: Traditional indicators mainly focus on the correlation of the direction of variable change, ignoring the substantial impact of fluctuation amplitude on the system's regulation needs, resulting in an inability to accurately represent the peak-shaving pressure brought about by random fluctuations in meteorological factors.

[0007] The lack of consideration for the active regulation capacity of hydropower is the biggest shortcoming of existing technology. Current evaluation systems often treat hydropower as a passive, adaptive, static resource, failing to delve into the active regulation capacity of cascade hydropower stations in utilizing reservoir capacity for time-series power transfer. Ignoring the time-series regulation potential of hydropower leads to conservative or distorted assessments of complementary capabilities, failing to provide accurate quantitative data for practical engineering projects.

[0008] In summary, the existing technology lacks a complementary capability quantification method that can deeply examine the correlation between water, wind, and solar fluctuations and incorporate the active regulation capability of cascade hydropower and the time-series correlation characteristics of hydropower into the evaluation system. Summary of the Invention

[0009] To address the aforementioned deficiencies in existing technologies, the present invention aims to provide an optimization and evaluation method for the complementary capabilities of hydropower, wind power, and solar power, considering the active regulation capacity of hydropower. First, a simulation input dataset of a multi-energy complementary system is constructed as the basic data sequence for evaluating the complementary capabilities. This includes the selection and generation of typical inflow runoff sequences and the calculation of typical wind and solar power output sequences synchronized with the time sequence. Simultaneously, the fixed physical parameters and dynamic time-series parameters required by the model are initialized and defined. Second, a quantitative index of the complementary capabilities of hydropower, wind power, and solar power, considering the coefficient of variation, is generated. Then, a simulation optimization method for evaluating the complementary capabilities of hydropower, wind power, and solar power is constructed, with the objective functions of maximizing the complementary capabilities and minimizing water wastage, thereby reducing water wastage generated during the active regulation process of hydropower. Finally, a hierarchical sequence method is used to achieve efficient optimization and solution of the multi-objective model.

[0010] The technical solution of the present invention:

[0011] An optimization evaluation method for the hydro-wind-solar complementary capabilities, considering the active regulation capacity of hydropower, includes the following steps:

[0012] Step 1: Construct the simulation input dataset for the water-wind-solar multi-energy complementary system.

[0013] This step aims to generate the foundational data sequence for subsequent complementary capacity assessment, specifically including the selection and generation of typical inflow runoff sequences, and the calculation of typical wind and solar power output sequences synchronized with the time series. Simultaneously, it initializes and defines the fixed physical parameters and dynamic time-series parameters required by the model. The specific process is as follows:

[0014] Step 1.1: Generate a typical inflow runoff time series.

[0015] Based on historical hydrological data of the watershed, frequency analysis was used to statistically analyze long-term annual runoff data. According to preset design guarantee rates (such as the frequencies corresponding to high-water years, normal-water years, and low-water years), specific years were selected as typical level years. Daily or hourly inflow runoff for these typical level years was extracted to construct typical inflow runoff time series sequences for cascade hydropower stations.

[0016] Step 1.2: Generate typical wind and solar power output sequences.

[0017] To maintain consistency of meteorological conditions in the multi-energy complementarity assessment, meteorological observation data such as wind speed, solar irradiance, and temperature were selected from the same period (same typical level year) as the typical inflow runoff sequence.

[0018] Using empirical formulas and conversion models for wind and solar power output, meteorological data is transformed into power output data: the theoretical power output of the wind farm is calculated based on wind speed data, turbine hub height, and turbine power curves; the theoretical power output of the photovoltaic power station is calculated based on solar irradiance, ambient temperature, and photovoltaic module conversion efficiency. This generates a typical wind and solar power output time series that is strictly aligned with the typical inflow runoff time series.

[0019] Step 1.3: Initialize model input parameters.

[0020] The input dataset required to construct the complementary capability evaluation model is specifically divided into two categories: fixed physical parameters and dynamic time-series parameters.

[0021] Fixed parameters: A set of constants used to describe the physical properties and constraints of the system, including but not limited to: Basic parameters of hydropower stations and units: covering reservoir characteristic water levels (dead water level, normal storage water level), reservoir regulating capacity, installed capacity, number of units, rated head, rated flow of units, and comprehensive efficiency coefficient, etc.; Operating characteristic curves of hydropower stations: including reservoir water level-capacity curves and tailrace water level-outflow curves; Empirical formula parameters for wind and solar power: including wind turbine cut-in / cut-out wind speeds, rated wind speeds, photovoltaic panel reference temperatures, photoelectric conversion coefficients, and system loss factors, etc.; Installed capacity of wind and solar power: the rated installed capacity of planned or completed wind farms and photovoltaic power stations.

[0022] Dynamic parameters: a series of variables that change with time step t, including: daily average water consumption rate time series: a water consumption rate per unit of power generation calculated based on reservoir water level changes and unit operating characteristics; runoff time series: typical inflow runoff time series data generated in step (1.1) for the whole time period; wind and solar meteorological conditions time series: typical wind and solar power output time series generated from the original meteorological data such as wind speed, irradiance and temperature involved in step (1.2) for the whole time period.

[0023] Step 2: Generate quantitative indicators of the complementarity of water, wind and solar power that take into account active regulation of hydropower.

[0024] Given that traditional complementarity indices treat the output sequences of multidimensional power sources such as hydropower, wind power, and solar power as separate sequences, they are often inadequate or computationally intensive when solving the problem of evaluating the complementarity between high-dimensional power sources.

[0025] This method sums the power output sequences from multiple sources to obtain a single power output sequence, and then proposes an evaluation index for the complementarity of water, wind and solar power based on the coefficient of variation.

[0026] Using wind and solar power output and sequence under the same time series coefficient of variation This indicates the complementarity of wind power and photovoltaic power output under uncertain conditions, where t is the current time period and T is the number of time periods included in the dispatch cycle; The larger the size, the worse the wind-solar complementarity. The closer the value is to 0, the better the wind-solar complementarity. The time indicates that the wind and light are completely complementary, and the output and sequence are on a horizontal line.

[0027] Using the same time sequence for hydropower and solar power output coefficient of variation This indicates the complementarity of the overall operation of water, wind, and light. The determination of the strength of the complementarity between water, wind, and light is the same as that between wind and light.

[0028] The formulas are as follows:

[0029] Wind and solar power output and sequence coefficient of variation :

[0030] (1)

[0031] Water, wind, and light output and sequence coefficient of variation :

[0032] (2)

[0033] Complementarity index C:

[0034] (3)

[0035] In the formula, C represents the hydropower active regulation and complementarity capability of the water-wind-solar base throughout the entire optimization cycle, which is dimensionless.

[0036] in and The larger the value, the worse the complementarity between wind and solar energy, and between water and wind and solar energy. and The closer the value is to 0, the better the complementarity between wind and solar energy, and between water and wind and solar energy. This indicates that the scenery and landscape are completely complementary. The time interval indicates that water, wind, and solar power are completely complementary. Complete complementarity means that the power output and sequence are on the same horizontal line at the same time interval.

[0037] Step 3: Construct a simulation optimization method for evaluating the water-wind-solar complementary capabilities.

[0038] The principle behind this evaluation method is to maximize complementary capabilities.

[0039] Step 3.1, Objective Function

[0040] (4)

[0041] (5)

[0042] In the formula: The total water discharge from the reservoir in the Shuifengguang Base during the entire optimization cycle is expressed in 10,000 m³. 3 . The discharge flow rate of the reservoir during time period t is expressed in m³. 3 / s; This represents the number of seconds in a single time period, expressed in seconds (s).

[0043] Step 3.2, Constraints

[0044] Water balance constraints:

[0045] (6)

[0046] In the formula: , and These represent the inflow, power generation, outflow, and discharge of reservoir h during time period t, respectively, in m³. 3 / s; This represents the reservoir capacity of reservoir h during time period t, in ten thousand cubic meters. 3 .

[0047] Reservoir storage capacity constraint:

[0048] (7)

[0049] In the formula: and These represent the upper and lower limits of the reservoir's capacity during time period t, in ten thousand cubic meters. 3 .

[0050] Reservoir outflow and generating unit flow constraint:

[0051] (8)

[0052] (9)

[0053] In the formula: and The reservoirs were located during different time periods. The upper and lower limits of power generation flow, in m³. 3 / s; and The reservoirs were located during different time periods. The upper and lower limits of outbound flow rate, in m³. 3 / s.

[0054] Initial water level constraints of the reservoir:

[0055] (10)

[0056] In the formula: For the initial storage capacity, End-of-term storage capacity and These represent the initial and final reservoir capacity limits, in tens of thousands of cubic meters. 3 .

[0057] Hydropower output calculation constraints:

[0058] (11)

[0059] In the formula: The hydropower generation capacity during time period t is expressed in MW. The water consumption rate of the hydropower station in time period t, in m³. 3 / kWh.

[0060] Hydropower station output constraints:

[0061] (12)

[0062] In the formula: and These represent the upper and lower limits of the power output of the hydropower station during time period t, in MW.

[0063] Step 4: Solve the multi-objective problem using the sequence method.

[0064] This step employs a hierarchical sequence method to solve the multi-objective model of water-wind-solar hybrid development constructed in steps 2 and 3. The core idea of ​​this method is to transform the multi-objective optimization problem into a series of single-objective optimization problems. By introducing a priority mechanism and tolerance control strategy, the optimal solution for secondary objectives is sought while ensuring the achievement of the core objectives. The specific process is as follows:

[0065] Step 4.1: Prioritize the objective functions

[0066] Priorities are assigned to each objective function based on the actual operational requirements and scheduling strategy of the hydro-wind-solar hybrid system.

[0067] In this invention, the complementary capability evaluation model is decomposed into two levels:

[0068] First priority: Maximizing the complementarity index is set as the first objective function. This objective reflects the core evaluation requirements of the system.

[0069] Second priority: Minimize the total amount of water wasted as the second objective function. This objective reflects the system's demand for optimizing water resource utilization efficiency while meeting power generation needs; it is a secondary objective. This is a decision-making scheme that considers maximizing complementarity. The decision-making scheme is based on minimizing the total amount of water wasted. yes A subset of this reflects the impact of hydropower's active regulation capabilities on complementarity.

[0070] Step 4.2, First-level optimization: Solving the highest priority objective.

[0071] Under the premise of satisfying the basic system constraints (i.e., the feasible region determined in step 3.2), firstly, the first objective function is... Perform single-objective optimization to obtain the optimal function value for the first objective. .

[0072] Step 4.3, Constraint Curing and Tolerance Control

[0073] To avoid excessively sacrificing the performance of the first objective when optimizing the second objective, the optimization results of the first level need to be transformed into constraints for subsequent calculations. Considering the trade-offs often present in engineering practice regarding "non-dominated solutions," this invention introduces a finite degradation strategy. This allows the optimal value of the first objective function to moderately degrade within a preset tolerance range, thereby expanding the search space of the second objective function and preventing the second objective from becoming unsolvable or performing extremely poorly due to overly rigid constraints on the first objective.

[0074] Step 4.4, Second-level optimization

[0075] Under the additional constraint that the value of the first objective function is not worse than the degraded setpoint, in the new, restricted feasible region The second objective is to find the solution that minimizes the total water wastage, and finally obtain the comprehensive optimal solution of the water-wind-solar hybrid system.

[0076] Step 4.5, the specific model formula is as follows:

[0077] (13)

[0078] (14)

[0079] (15)

[0080] In the formula and These are the optimal function values ​​for the first and second objective functions, respectively. The allowable degradation amount for the first objective function; It is the set of real numbers; and , respectively, are the feasible regions of the decision variables for the first and second objective functions.

[0081] When performing calculations, Set as the first objective function. Set as the second objective function; the first objective function allows for a certain amount of degradation. Set to 3%; Feasible region of the first objective function The feasible region of the second objective function is determined according to equations (8)-(12). Determined according to equations (13)-(15).

[0082] The beneficial effects of this invention are:

[0083] This invention, through in-depth analysis of the stochastic fluctuation characteristics of meteorological factors affecting wind and solar resources, overcomes the limitations of traditional correlation coefficient evaluation and constructs a correlation model that incorporates fluctuation amplitude and temporal characteristics. Simultaneously, it transforms the reservoir regulation performance of cascade hydropower stations from a passive variable into an active regulation factor, deeply integrating the temporal correlation regulation capability of hydropower into the evaluation indicators. This achieves a precise quantitative characterization of the multi-energy complementarity capability of hydropower, wind, and solar power, providing a scientific theoretical basis for the capacity configuration and optimized scheduling of integrated hydropower, wind, and solar energy bases. Taking the downstream hydropower, wind, and solar base of the Lancang River as the research object, the complementary capability is evaluated for different time steps and scheduling cycles in the long, medium, and short term. A comparison with traditional methods based on maximizing the power generation capacity of hydropower, wind, and solar power reveals that this method improves the complementarity evaluation results by 148.39%, reflecting the active regulation capability of hydropower while ensuring water energy utilization. Attached Figure Description

[0084] Figure 1 This is a framework diagram of the assessment model for the complementary capabilities of water, wind, and solar power.

[0085] Figure 2 This is a diagram of a photovoltaic power generation scenario;

[0086] Figure 3 This is a diagram of wind power output.

[0087] Figure 4 It is a quantitative diagram of complementary indicators under the active regulation of hydropower in a medium-term complementary operation;

[0088] Figure 5 This is a diagram showing the operation of hydropower-wind-solar hybrid systems on typical days during the flood and dry seasons, where (a) to (d) correspond to the operation on four different days. Detailed Implementation

[0089] The invention will be further described below with reference to implementation examples.

[0090] The overall process of this invention is as follows: Figure 1 As shown.

[0091] Taking the Lancang River downstream integrated hydropower-wind-solar energy base with a hydropower installed capacity of 3 million kilowatts and a total new energy installed capacity of 9.8 million kilowatts as a case study, this paper constructs an evaluation model considering the complementary capabilities of hydropower, wind power, and solar power, taking into account the active regulation of hydropower. After the Lancang River downstream wind and solar power stations are connected to the grid, they are transmitted to Guangdong, Jiangsu, and other provinces via ultra-high voltage direct current transmission lines together with hydropower. Since this integrated hydropower-wind-solar clean energy base is still in the planning and construction stage, it is difficult to grasp the integrated evaluation of the giant hydropower station after the participation of new energy in transmission. Therefore, this embodiment uses the Lancang River basin integrated hydropower-wind-solar clean energy base as a simulation case. The constructed optimization model is solved using Python programming and Gurobi 10.0.1, with the optimization accuracy of the Gurobi solver set to gap ≤ 0.1%. The computing environment is an AMD Ryzen 7 5800H CPU 3.20 GHz, 16GB RAM, and Win11 operating system. The input data for the model includes runoff data, actual installed capacity data for wind power and photovoltaics, sourced from the Lancang River Hydropower Development Co., Ltd., and meteorological data for calculating renewable energy output sourced from ERA5 (https: / / cds.climate.copernicus.eu). The specific steps are as follows:

[0092] Step 1: Construct the simulation input dataset for the water-wind-solar multi-energy complementary system.

[0093] This step aims to generate the foundational data sequence for subsequent complementary capacity assessment, specifically including the selection and generation of typical inflow runoff sequences, and the calculation of typical wind and solar power output sequences synchronized with the time series. Simultaneously, it initializes and defines the fixed physical parameters and dynamic time-series parameters required by the model. The specific process is as follows:

[0094] Step 1.1: Generate a typical inflow runoff time series;

[0095] Based on historical hydrological data of the watershed, frequency analysis was used to statistically analyze long-term annual runoff data. According to preset design guarantee rates (such as the frequencies corresponding to high-water years, normal-water years, and low-water years), specific years were selected as typical level years. Daily or hourly inflow runoff for these typical level years was extracted to construct typical inflow runoff time series sequences for cascade hydropower stations.

[0096] Step 1.2: Generate typical wind and solar power output sequences;

[0097] To maintain consistency of meteorological conditions in the multi-energy complementarity assessment, meteorological observation data such as wind speed, solar irradiance, and temperature were selected from the same period (same typical level year) as the typical inflow runoff sequence.

[0098] Using empirical formulas and conversion models for wind and solar power output, meteorological data is transformed into power output data: the theoretical power output of the wind farm is calculated based on wind speed data, turbine hub height, and turbine power curves; the theoretical power output of the photovoltaic power station is calculated based on solar irradiance, ambient temperature, and photovoltaic module conversion efficiency. This generates a typical wind and solar power output time series that is strictly aligned with the typical inflow runoff time series.

[0099] Step 1.3: Initialize model input parameters;

[0100] The input dataset required to construct the complementary capability evaluation model is specifically divided into two categories: fixed physical parameters and dynamic time-series parameters.

[0101] Fixed parameters: A set of constants used to describe the physical properties and constraints of the system, including but not limited to: Basic parameters of hydropower stations and units: covering reservoir characteristic water levels (dead water level, normal storage water level), reservoir regulating capacity, installed capacity, number of units, rated head, rated flow of units, and comprehensive efficiency coefficient, etc.; Operating characteristic curves of hydropower stations: including reservoir water level-capacity curves and tailrace water level-outflow curves; Empirical formula parameters for wind and solar power: including wind turbine cut-in / cut-out wind speeds, rated wind speeds, photovoltaic panel reference temperatures, photoelectric conversion coefficients, and system loss factors, etc.; Installed capacity of wind and solar power: the rated installed capacity of planned or completed wind farms and photovoltaic power stations.

[0102] Dynamic parameters: a series of variables that change with time step t, including: daily average water consumption rate time series: a water consumption rate per unit of power generation calculated based on reservoir water level changes and unit operating characteristics; runoff time series: typical inflow runoff time series data generated in step (1.1) for the whole time period; wind and solar meteorological conditions time series: typical wind and solar power output time series generated from the original meteorological data such as wind speed, irradiance and temperature involved in step (1.2) for the whole time period.

[0103] Figure 2 Indicates photovoltaic power output scenarios, Figure 3 This indicates a wind power output scenario.

[0104] Step 2: Generate quantitative indicators of the complementarity of water, wind and solar power that take into account active regulation of hydropower.

[0105] Given that traditional complementarity indices treat the output sequences of multidimensional power sources such as hydropower, wind power, and solar power as separate sequences, they are often inadequate or computationally intensive when solving the problem of evaluating the complementarity between high-dimensional power sources.

[0106] This method sums the power output sequences from multiple sources to obtain a single power output sequence, and then proposes an evaluation index for the complementarity of water, wind and solar power based on the coefficient of variation.

[0107] Using wind and solar power output and sequence under the same time series coefficient of variation This indicates the complementarity of wind power and photovoltaic power output under uncertain conditions, where t is the current time period and T is the number of time periods included in the dispatch cycle; The larger the size, the worse the wind-solar complementarity. The closer the value is to 0, the better the wind-solar complementarity. The time indicates that the wind and light are completely complementary, and the output and sequence are on a horizontal line.

[0108] Using the same time sequence for hydropower and solar power output coefficient of variation This indicates the complementarity of the overall operation of water, wind, and light. The determination of the strength of the complementarity between water, wind, and light is the same as that between wind and light.

[0109] The formulas are as follows:

[0110] Wind and solar power output and sequence coefficient of variation :

[0111] (1)

[0112] Water, wind, and light output and sequence coefficient of variation :

[0113] (2)

[0114] Complementarity index C:

[0115] (3)

[0116] In the formula, C represents the hydropower active regulation and complementarity capability of the water-wind-solar base throughout the entire optimization cycle, which is dimensionless.

[0117] in and The larger the value, the worse the complementarity between wind and solar energy, and between water and wind and solar energy. and The closer the value is to 0, the better the complementarity between wind and solar energy, and between water and wind and solar energy. This indicates that the scenery and light are completely complementary. The time interval indicates that water, wind, and solar power are completely complementary. Complete complementarity means that the power output and sequence are on the same horizontal line at the same time interval.

[0118] Step 3: Construct a simulation optimization method for evaluating the water-wind-solar complementary capabilities.

[0119] The principle behind this evaluation method is to maximize complementary capabilities. Figure 1 This is a framework diagram of the evaluation method.

[0120] Step 3.1, Objective Function:

[0121] (4)

[0122] (5)

[0123] In the formula: The total water discharge from the reservoir in the Shuifengguang Base during the entire optimization cycle is expressed in 10,000 m³. 3 . The discharge flow rate of the reservoir during time period t is expressed in m³. 3 / s; This represents the number of seconds in a single time period, expressed in seconds (s).

[0124] Step 3.2, Constraints:

[0125] Water balance constraints:

[0126] (6)

[0127] In the formula: , and These represent the inflow, power generation, outflow, and discharge of reservoir h during time period t, respectively, in m³. 3 / s; This represents the reservoir capacity of reservoir h during time period t, in ten thousand cubic meters. 3 .

[0128] Reservoir storage capacity constraint:

[0129] (7)

[0130] In the formula: and These represent the upper and lower limits of the reservoir's capacity during time period t, in ten thousand cubic meters. 3 .

[0131] Reservoir outflow and generating unit flow constraint:

[0132] (8)

[0133] (9)

[0134] In the formula: and The reservoirs were located during different time periods. The upper and lower limits of power generation flow, in m³. 3 / s; and The reservoirs were located during different time periods. The upper and lower limits of outbound flow rate, in m³.3 / s.

[0135] Initial water level constraints of the reservoir:

[0136] (10)

[0137] In the formula: For the initial storage capacity, End-of-term storage capacity and These represent the initial and final reservoir capacity limits, in tens of thousands of cubic meters. 3 .

[0138] Hydropower output calculation constraints:

[0139] (11)

[0140] In the formula: The hydropower generation capacity during time period t is expressed in MW. The water consumption rate of the hydropower station in time period t, in m³. 3 / kWh.

[0141] Hydropower station output constraints:

[0142] (12)

[0143] In the formula: and These represent the upper and lower limits of the power output of the hydropower station during time period t, in MW.

[0144] Model optimization is performed by calling Gurobi 10.0.1 using Python 3.10.

[0145] Step 4: Use the sequence method to solve the multi-objective problem.

[0146] This step employs a hierarchical sequence method to solve the multi-objective model of water-wind-solar hybrid development constructed in steps 2 and 3. The core idea of ​​this method is to transform the multi-objective optimization problem into a series of single-objective optimization problems. By introducing a priority mechanism and tolerance control strategy, the optimal solution for secondary objectives is sought while ensuring the achievement of the core objectives. The specific process is as follows:

[0147] Step 4.1: Prioritize the objective functions;

[0148] Priorities are assigned to each objective function based on the actual operational requirements and scheduling strategy of the hydro-wind-solar hybrid system.

[0149] In this invention, the complementary capability evaluation model is decomposed into two levels:

[0150] First priority: Maximizing the complementarity index is set as the first objective function. This objective reflects the core evaluation requirements of the system.

[0151] Second priority: Minimize the total amount of water wasted as the second objective function. This objective reflects the system's demand for optimizing water resource utilization efficiency while meeting power generation needs.

[0152] Step 4.2, First-level optimization: Solving the highest priority objective;

[0153] Under the premise of satisfying the basic system constraints (i.e., the feasible region determined in step (3.2)), the first objective function is first... Perform single-objective optimization to obtain the optimal function value for the first objective. .

[0154] Step 4.3: Constraint solidification and tolerance control;

[0155] To avoid excessively sacrificing the performance of the first objective when optimizing the second objective, the optimization results of the first level need to be transformed into constraints for subsequent calculations. Considering the trade-offs often present in engineering practice regarding "non-dominated solutions," this invention introduces a finite degradation strategy. This allows the optimal value of the first objective function to moderately degrade within a preset tolerance range, thereby expanding the search space of the second objective function and preventing the second objective from becoming unsolvable or performing extremely poorly due to overly rigid constraints on the first objective.

[0156] Step 4.4, Second-level optimization;

[0157] Under the additional constraint that the value of the first objective function is not worse than the degraded setpoint, in the new, restricted feasible region The second objective is to find the solution that minimizes the total water wastage, and finally obtain the comprehensive optimal solution of the water-wind-solar hybrid system.

[0158] Step 4.5, the specific model formula is as follows:

[0159] (13)

[0160] (14)

[0161] (15)

[0162] In the formula and These are the optimal function values ​​for the first and second objective functions, respectively. The allowable degradation amount for the first objective function; It is the set of real numbers; and , respectively, are the feasible regions of the decision variables for the first and second objective functions.

[0163] When performing calculations, Set as the first objective function. Set as the second objective function; the first objective function allows for a certain amount of degradation. Set to 3%; Feasible region of the first objective function The feasible region of the second objective function is determined according to equations (8)-(12). Determined according to equations (13)-(15).

[0164] The solution yields the evaluation results of the complementarity of long-term, medium-term, and short-term complementary operations of water, wind, and solar power.

[0165] Table 1 Comparison of Complementarity Indicators between Long-Term Complementary Operation of Hydropower, Wind Power, and Solar Power and Operation with Maximum Power Generation.

[0166]

[0167] Note: The improvement rate in the table is calculated as [(Complementary C - Maximum Power Generation C) / Maximum Power Generation]. 】

[0168] Table 1 shows the evaluation results of the hydro-wind-solar complementarity capability after considering the active regulation capability of hydropower. It can be seen that compared with the traditional method of evaluating complementarity capability based on maximizing the power generation capacity of hydropower, wind power, and solar power, the evaluation results of the hydro-wind-solar complementarity capability after considering the active regulation capability of hydropower are improved by 148.39%.

[0169] Figure 4 This is a quantification of complementary indicators under the active regulation of hydropower during medium-term complementary operation. According to the figure analysis, the complementary evaluation indicators that take into account the simulation of hydropower regulation capacity can take into account reservoir scheduling behavior and obtain more refined and realistic quantitative results of complementary indicators.

[0170] Figure 5 This study examines the operation of hydropower-wind-solar hybrid systems on typical days during the flood and dry seasons. The results indicate that both the wind and solar power output levels and the active regulation capacity of hydropower have a significant impact on the assessment of hydropower-wind-solar hybrid system capabilities.

Claims

1. A method for optimizing and evaluating the hydro-wind-solar complementary capabilities, considering the active regulation capacity of hydropower, characterized in that, The steps include the following: Step 1: Construct the simulation input dataset for a multi-energy complementary system of water, wind, and solar power; This includes the selection and generation of typical inflow runoff sequences, as well as the calculation of typical wind and solar power output sequences synchronized with the time series; at the same time, the fixed physical parameters and dynamic time series parameters required by the model are initialized and defined. Step 2: Generate quantitative indicators of the complementarity of hydropower, wind power, and solar power, taking into account active regulation of hydropower. Using wind and solar power output and sequence under the same time series coefficient of variation , This indicates the complementarity of wind power and photovoltaic power output under uncertain conditions, where t is the current time period and T is the number of time periods included in the dispatch cycle; The larger the size, the worse the wind-solar complementarity. The closer the value is to 0, the better the wind-solar complementarity. The time indicates that the wind and light are completely complementary, and the output and sequence are on a horizontal line; Using the same time sequence for hydropower and solar power output coefficient of variation , This indicates the complementarity of the overall operation of water, wind, and light; the determination of the strength of the complementarity between water, wind, and light is the same as that between wind and light. The formulas are as follows: Wind and solar power output and sequence coefficient of variation : (1) Water, wind, and light output and sequence coefficient of variation : (2) Complementarity index C: (3) In the formula, C represents the hydropower active regulation and complementarity capability of the water-wind-solar base throughout the entire optimization cycle, which is dimensionless; in and The larger the value, the worse the complementarity between wind and solar energy, and between water and wind and solar energy. and The closer the value is to 0, the better the complementarity between wind and solar energy, and between water and wind and solar energy. This indicates that the scenery and landscape are completely complementary. The time sequence indicates that water, wind and light are completely complementary; complete complementarity means that the sequence of output and sum under the same time sequence is a horizontal line. Step 3: Construct a simulation optimization method for evaluating the water-wind-solar complementary capabilities; Step 3.1, Objective function; (4) (5) In the formula: The total water discharge from the reservoir in the Shuifengguang Base during the entire optimization cycle is expressed in 10,000 m³. 3 ; The discharge flow rate of the reservoir during time period t is expressed in m³. 3 / s; The number of seconds in a single time period, expressed in seconds (s). Step 3.2, Constraints; These include water balance constraints, reservoir storage capacity constraints, reservoir outflow and generating flow constraints, initial reservoir water level constraints, hydropower output calculation constraints, and hydropower station output constraints. Step 4: Solve the multi-objective model of water-wind-solar complementarity constructed in Steps 2 and 3 using the hierarchical sequence method; By introducing a priority mechanism and a tolerance control strategy, the complementary capability evaluation model is decomposed into two levels: the first priority sets the maximization of the complementarity index as the first objective function, and the second priority sets the minimum total water wastage as the second objective function. First, the first objective function is optimized in a single objective, and under the additional constraint that the value of the first objective function is not worse than the value set after degradation, the second objective function is solved to obtain the comprehensive optimal solution of the water-wind-solar complementary system.

2. The method for optimizing and evaluating the water-wind-solar complementary capabilities considering the active regulation capacity of hydropower, as described in claim 1, is characterized in that... Specifically as follows: Step 1.1: Generate a typical inflow runoff sequence; Based on historical hydrological data of the watershed, frequency analysis was used to statistically analyze long-term annual runoff data; according to the preset design guarantee rate, a specific year was selected as a typical level year; the daily or hourly inflow runoff of the typical level year was extracted to construct a typical inflow runoff time series of cascade hydropower stations. Step 1.2: Generate typical wind and solar power output sequences; To maintain consistency of meteorological conditions in the multi-energy complementarity assessment, wind speed, solar irradiance, and temperature observation data from the same period as the typical inflow runoff sequence were selected. Using empirical formulas and conversion models for wind and solar power output, meteorological data is converted into power output data: based on wind speed data, wind turbine hub height, and wind turbine power curves, the theoretical power output of wind farms is calculated; based on solar irradiance, ambient temperature, and photovoltaic module conversion efficiency, the theoretical power output of photovoltaic power plants is calculated; thus, a typical wind and solar power output time series aligned with a typical inflow runoff time series is generated. Step 1.3: Initialize model input parameters; The input dataset required to construct the complementary capability evaluation model is specifically divided into two categories: fixed physical parameters and dynamic time-series parameters. Fixed physical parameters: Basic parameters of hydropower stations and generating units: covering reservoir characteristic water level, reservoir regulating capacity, installed capacity, number of generating units, rated head, rated flow rate of generating units, and comprehensive efficiency coefficient; Operating characteristic curves of hydropower stations: reservoir water level-capacity curve and tailrace water level-outflow curve; Empirical formula parameters for wind and solar power: wind turbine cut-in / cut-out wind speed, rated wind speed, photovoltaic panel reference temperature, photoelectric conversion coefficient, and system loss factor; Wind and solar installed capacity: The rated installed capacity of planned or completed wind farms and photovoltaic power stations; Dynamic time series parameters: a sequence of variables that change with time step t, including: daily average water consumption rate time series: a sequence of water consumption rate per unit of power generation calculated based on reservoir water level changes and unit operating characteristics; Runoff time series: Typical inflow runoff time series data generated in step 1.1 for the entire time period; Wind and solar meteorological conditions time series: Typical wind and solar power output time series generated from the raw meteorological data of wind speed, irradiance and temperature obtained in step 1.2 for the entire time period.

3. The method for optimizing and evaluating the water-wind-solar complementary capabilities considering the active regulation capacity of hydropower, as described in claim 1, is characterized in that... The specific constraints in step 3.2 are as follows: Water balance constraints: (6) In the formula: , and These represent the inflow, power generation, and discharge of reservoir h during time period t, respectively, in m³. 3 / s; This represents the reservoir capacity of reservoir h during time period t, in ten thousand cubic meters. 3 ; Reservoir storage capacity constraint: (7) In the formula: and These represent the upper and lower limits of the reservoir's capacity during time period t, in ten thousand cubic meters. 3 ; Reservoir outflow and generating unit flow constraint: (8) (9) In the formula: and The reservoirs were located during different time periods. The upper and lower limits of power generation flow, in m³. 3 / s; and The reservoirs were located during different time periods. The upper and lower limits of outbound flow rate, in m³. 3 / s; Initial water level constraints of the reservoir: (10) In the formula: For the initial storage capacity, End-of-term storage capacity and These represent the initial and final reservoir capacity limits, in tens of thousands of cubic meters. 3 ; Hydropower output calculation constraints: (11) In the formula: The hydropower generation capacity during time period t is expressed in MW. The water consumption rate of the hydropower station in time period t, in m³. 3 / kWh; Hydropower station output constraints: (12) In the formula: and These represent the upper and lower limits of the power output of the hydropower station during time period t, in MW.

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