Day-ahead dispatching method and device for thermal power units that coordinate with the uncertainty of wind power using hydropower

Abstract

This invention relates to the field of power dispatch automation technology, and discloses a day-ahead dispatching method and apparatus for thermal power units that coordinates with the uncertainty of wind power. This invention considers the wind power output prediction error factor, uses the kernel density method to correct the spinning reserve constraint, and then establishes a multi-objective optimization model for hydropower unit output based on minimizing the maximum value of equivalent load changes in adjacent times, minimizing the variance of equivalent loads across all time periods, minimizing the maximum value of positive spinning reserve across all time periods, and minimizing the maximum value of negative spinning reserve across all time periods. The Pareto optimal solution set for hydropower unit output is obtained using a multi-swarm particle swarm optimization algorithm, from which the hydropower unit output scheme is derived. Based on this scheme, a day-ahead dispatching model for thermal power units considering the uncertainty of wind power output and hydropower coordination is constructed, and solved to obtain the day-ahead dispatching plan for thermal power units. This invention can fully utilize the advantages of hydropower and overcome the problems of strong fluctuations and large prediction errors in wind power generation.

Description

Technical Field

[0001] This invention relates to the field of power dispatch automation technology, and in particular to a day-ahead dispatching method and apparatus for thermal power units that coordinate hydropower with the uncertainty of wind power. Background Technology

[0002] Due to the influence of various natural factors such as wind speed, wind power generation is characterized by strong volatility and large prediction errors, posing a significant challenge to the preparation of day-ahead dispatch plans for thermal power units. If the uncertainties of wind power are not properly addressed, the prepared day-ahead dispatch plans for thermal power units may suffer from problems such as insufficient ramp-up capacity or insufficient reserves, leading to wind curtailment and load shedding.

[0003] For power grids rich in hydropower resources, the strong climbing ability of hydropower can be considered for coordinating and resolving the uncertainties of wind power generation. However, current day-ahead dispatching methods for thermal power units that consider the uncertainties of wind power and the coordination with hydropower are not yet mature. Summary of the Invention

[0004] This invention provides a method and apparatus for day-ahead scheduling of thermal power units to coordinate the uncertainty of wind power generation with hydropower, and solves the technical problem of how to make full use of the advantages of hydropower to coordinate and solve the uncertainty of wind power generation when carrying out day-ahead scheduling of thermal power units.

[0005] The first aspect of this invention provides a day-ahead dispatching method for thermal power units that addresses uncertainties in coordinating hydropower and wind power, comprising:

[0006] Considering the wind power output prediction error, the kernel density estimation method is used to correct the positive and negative spinning reserves for each scheduling period, resulting in the corrected positive and negative spinning reserves for each scheduling period.

[0007] Based on the corrected positive rotating reserve and the corrected negative rotating reserve, a multi-objective optimization model for the output of hydropower units is established with the optimization objectives of minimizing the maximum value of the equivalent load change between adjacent times, minimizing the variance of the equivalent load in all time periods, minimizing the maximum value of the positive rotating reserve in all time periods, and minimizing the maximum value of the negative rotating reserve in all time periods.

[0008] The multi-objective optimization model is solved using a multi-swarm particle swarm optimization algorithm to determine the Pareto optimal solution set for the hydropower unit output. The optimal solution is selected from the Pareto optimal solution set as the optimal hydropower output, thus obtaining the hydropower unit output scheme.

[0009] Based on the power output scheme of the hydropower unit, a model constraint is constructed, and with the minimization of the total operating cost of the power system as the objective function, a day-ahead dispatch model for thermal power units containing hydropower and wind power is constructed.

[0010] The daytime scheduling model of the thermal power units is optimized and solved to obtain the daytime scheduling plan of the thermal power units.

[0011] According to one achievable method of the first aspect of the present invention, the step of considering wind power output prediction errors and using kernel density estimation to correct the positive and negative spinning reserves for each scheduling period, to obtain corrected positive and negative spinning reserves for each scheduling period, includes:

[0012] For each scheduling period, obtain the predicted and actual renewable energy output data of the wind turbines for the corresponding scheduling period in historical days;

[0013] Based on the new energy power output forecast data and the new energy power output data, determine the historical forecast error data of the wind turbine units for the corresponding scheduling periods in each historical day;

[0014] Kernel density estimation and fitting are performed on the historical prediction error data to obtain the corresponding probability density function, and the first quantile and the second quantile of the probability density function are determined.

[0015] The positive rotation reserve and negative rotation reserve are corrected based on the first quantile and the second quantile to obtain the corrected positive rotation reserve and the corrected negative rotation reserve.

[0016] According to a method achievable according to a first aspect of the present invention, the step of correcting the positive rotation reserve and the negative rotation reserve based on the first quantile and the second quantile to obtain the corrected positive rotation reserve and the corrected negative rotation reserve includes:

[0017] Let the first quantile be The second quantile is in To determine the confidence level, the positive and negative spin-off reserves are corrected according to the following formula:

[0018]

[0019]

[0020] In the formula, R′ t,up This indicates the corrected forward rotation is ready for use, R′ t,down Indicates the corrected negative rotation is ready for use, R t,up R represents the positive reserve capacity requirement of the system during scheduling period t. t,down p represents the system's negative reserve capacity requirement during scheduling period t. RE,t The predicted renewable energy output of wind turbines during the dispatch period t.

[0021] According to one achievable method of the first aspect of the present invention, a multi-objective optimization model for the output of the hydropower unit is established based on the corrected positive rotating reserve and the corrected negative rotating reserve, with the optimization objectives of minimizing the maximum value of the equivalent load change between adjacent times, minimizing the variance of the equivalent load over all time periods, minimizing the maximum value of the positive rotating reserve over all time periods, and minimizing the maximum value of the negative rotating reserve over all time periods, including:

[0022] The maximum value of the equivalent load change between adjacent time periods is set as follows:

[0023]

[0024] In the formula, F1 represents the maximum value of the equivalent load change between adjacent time periods, and Pl t p represents the total load during the scheduling period t. hg,t For the output of the hydropower unit hg during the dispatch period t, P W,t To predict the wind power output during the dispatch period t, Pl t+1 For the total load during scheduling period t+1, p hg,t+1 To determine the output of the hydropower unit hg during the dispatch period t+1, P W,t+1 For the predicted wind power output during the dispatch period t+1, where T represents the last dispatch period of the day, G... h A collection of hydroelectric generating units;

[0025] The variance of the equivalent load for all time periods is set as follows:

[0026]

[0027] in:

[0028]

[0029] In the formula, F2 represents the variance of the equivalent load over all time periods, and Pl ave This represents the average equivalent load difference across all time periods.

[0030] Set the maximum forward rotation reserve value for all time periods to:

[0031]

[0032] In the formula, F3 represents the maximum positive rotation reserve value for all time periods, and R′ t,up p is reserved for the corrected forward rotation. hg,max This is the maximum output of the hydroelectric generator unit hg;

[0033] The maximum negative rotation reserve value for all time periods is set as follows:

[0034]

[0035] In the formula, F4 represents the maximum negative rotation reserve for all time periods, and R′ t,down p is reserved for the corrected negative rotation. hg,min This represents the minimum output of the hydroelectric generator unit hg;

[0036] The objective function of the multi-objective optimization model is set as min(F1, F2, F3, F4).

[0037] According to one achievable method of the first aspect of the present invention, the step of solving the multi-objective optimization model using a multi-swarm particle swarm optimization algorithm to determine the Pareto optimal solution set for the hydropower unit output includes:

[0038] Step S10: Set up a first particle group for minimizing F1, a second particle group for minimizing F2, a third particle group for minimizing F3, and a fourth particle group for minimizing F4; initialize each particle group and an external reserve set for recording the searched non-dominated solutions.

[0039] Step S20: Update the position and velocity of particles in each particle swarm, calculate the fitness value of newly generated particles, and update the local guide particles of the particles.

[0040] Step S30: Save the local optimal solution in the new particle swarm to the external reserve set, maintain and update the external reserve set, and randomly select a particle in the updated external reserve set as the new global guiding particle.

[0041] Step S40: Determine whether the algorithm meets the algorithm termination condition. If it does, proceed to step S50; otherwise, jump to step S20.

[0042] Step S50: Output all particles in the external reserve set as the Pareto optimal solution set corresponding to the objective function of the multi-objective optimization model.

[0043] According to one achievable method of the first aspect of the present invention, the step of selecting the optimal solution from the Pareto optimal solution set as the optimal hydropower output includes:

[0044] The optimal solution is selected from the Pareto optimal solution set using the entropy weight method as the optimal hydropower output.

[0045] According to one achievable method of the first aspect of the present invention, the step of constructing a day-ahead dispatch model for thermal power units containing hydropower and wind power based on the power output scheme of the hydropower unit and with the minimization of the total operating cost of the power system as the objective function includes:

[0046] Based on the aforementioned hydropower unit output scheme, the equivalent load and positive / negative rotational reserve are determined as follows:

[0047]

[0048]

[0049]

[0050] In the formula, Pl′ t For the load after scheduling period t, Pl t The total load during the scheduling period t, For the optimal hydropower output, P W,t For the predicted wind power output during the dispatch period t, where T represents the last dispatch period of the day, G... h R″ is a collection of hydroelectric generating units. t,up R′ is reserved for the forward rotation after the equivalent value. t,up p is reserved for the corrected forward rotation. hg,max R″ represents the maximum output of the hydroelectric generator unit hg. t,down p is reserved for the negative rotation after equivalence. hg,min This represents the minimum output of the hydroelectric generator unit hg;

[0051] The power balance constraints are constructed as follows:

[0052]

[0053] In the formula, u g,t p represents the start-up and shutdown status of thermal power unit g during dispatch period t. g,t Let G be the power generation capacity of thermal power unit g during the dispatch period t, and G be the set of thermal power units.

[0054] The upper and lower limits of the output of thermal power units are constrained as follows:

[0055] u g,t p g,min ≤p g,t ≤u g,t p g,max ,t=1,2,...,T

[0056] In the formula, p g,min p is the lower limit of the output of thermal power unit g. g,max This represents the upper limit of the output of thermal power unit g;

[0057] The power ramping constraints for thermal power units are as follows:

[0058]

[0059] In the formula, p g,t-1 Let g be the power generation capacity of thermal power unit g during the dispatch period t-1. This represents the maximum upward climbing power of thermal power unit g. The maximum downward climbing power of thermal power unit g;

[0060] The constraints for setting up standby capacity are as follows:

[0061]

[0062]

[0063] The objective function for constructing the day-ahead scheduling model of the thermal power units is:

[0064]

[0065] In the formula, f represents the objective function, a g Let b be the secondary power generation cost coefficient of thermal power unit g. g Let c be the primary power generation cost coefficient for thermal power unit g. g U is a constant power generation cost coefficient for thermal power unit g. g,t-1 S represents the start-up and shutdown status of thermal power unit g during the dispatch period t-1. g The start-up and shutdown costs for thermal power unit g.

[0066] A second aspect of the present invention provides a day-ahead dispatching device for thermal power units that coordinates the uncertainty of wind power with hydropower, comprising:

[0067] The positive and negative spinning reserve correction module is used to take into account the wind power output prediction error and use the kernel density estimation method to correct the positive and negative spinning reserves for each scheduling period, so as to obtain the corrected positive and negative spinning reserves for each scheduling period.

[0068] The first model construction module is used to establish a multi-objective optimization model for the output of hydropower units based on the corrected positive rotating reserve and the corrected negative rotating reserve, with the optimization objectives being to minimize the maximum value of the equivalent load change between adjacent times, the minimum variance of the equivalent load in all time periods, the minimum maximum value of the positive rotating reserve in all time periods, and the minimum maximum value of the negative rotating reserve in all time periods.

[0069] The optimal hydropower output determination module is used to solve the multi-objective optimization model using a multi-swarm particle swarm algorithm, determine the Pareto optimal solution set for the hydropower unit output, select the optimal solution from the Pareto optimal solution set as the optimal hydropower output, and obtain the hydropower unit output scheme.

[0070] The second model construction module constructs model constraints based on the power output scheme of the hydropower unit, and constructs a day-ahead dispatch model for thermal power units containing hydropower and wind power with the objective function of minimizing the total operating cost of the power system.

[0071] The optimization solution module is used to optimize and solve the day-ahead scheduling model of the thermal power units to obtain the day-ahead scheduling plan of the thermal power units.

[0072] According to one embodiment of the second aspect of the present invention, the positive and negative rotation backup correction module includes:

[0073] The data acquisition unit is used to acquire, for each scheduling period, the predicted and actual output data of new energy for wind turbines in the corresponding scheduling periods of historical days.

[0074] The prediction error determination unit is used to determine the historical prediction error data of the wind turbine for the corresponding scheduling period in each historical day based on the new energy power output prediction data and the new energy actual power output data.

[0075] The quantile determination unit is used to perform kernel density estimation fitting on the historical prediction error data to obtain the corresponding probability density function, and to determine the first quantile and the second quantile of the probability density function.

[0076] The positive and negative rotation reserve correction unit is used to correct the positive rotation reserve and the negative rotation reserve according to the first quantile and the second quantile, so as to obtain the corrected positive rotation reserve and the corrected negative rotation reserve.

[0077] According to one achievable method of the second aspect of the present invention, the positive and negative rotation backup correction unit is specifically used for:

[0078] Let the first quantile be The second quantile is in To determine the confidence level, the positive and negative spin-off reserves are corrected according to the following formula:

[0079]

[0080]

[0081] In the formula, R′ t,up This indicates the corrected forward rotation is ready for use, R′ t,down Indicates the corrected negative rotation is ready for use, R t,up R represents the positive reserve capacity requirement of the system during scheduling period t. t,down p represents the system's negative reserve capacity requirement during scheduling period t. RE,t The predicted renewable energy output of wind turbines during the dispatch period t.

[0082] According to one achievable embodiment of the second aspect of the present invention, the first model building module comprises:

[0083] The first setting unit is used to set the maximum value of the equivalent load change over adjacent time periods as follows:

[0084]

[0085] In the formula, F1 represents the maximum value of the equivalent load change between adjacent time periods, and Pl t p represents the total load during the scheduling period t. hg,t For the output of the hydropower unit hg during the dispatch period t, P W,t To predict the wind power output during the dispatch period t, Pl t+1 For the total load during scheduling period t+1, p hg,t+1 To determine the output of the hydropower unit hg during the dispatch period t+1, P W,t+1 For the predicted wind power output during the dispatch period t+1, where T represents the last dispatch period of the day, G... h A collection of hydroelectric generating units;

[0086] The second setting unit is used to set the variance of the equivalent load for all time periods as follows:

[0087]

[0088] in:

[0089]

[0090] In the formula, F2 represents the variance of the equivalent load over all time periods, and Pl ave This represents the average equivalent load difference across all time periods.

[0091] The third setting unit is used to set the maximum positive rotation reserve value for all time periods as follows:

[0092]

[0093] In the formula, F3 represents the maximum positive rotation reserve value for all time periods, and R′ t,up p is reserved for the corrected forward rotation. hg,max This is the maximum output of the hydroelectric generator unit hg;

[0094] The fourth setting unit is used to set the maximum negative rotation reserve value for all time periods as follows:

[0095]

[0096] In the formula, F4 represents the maximum negative rotation reserve for all time periods, and R′ t,down p is reserved for the corrected negative rotation. hg,min This represents the minimum output of the hydroelectric generator unit hg;

[0097] The fifth setting unit is used to set the objective function of the multi-objective optimization model to min(F1, F2, F3, F4).

[0098] According to one achievable method of the second aspect of the present invention, the optimal hydropower output determination module includes:

[0099] An initialization unit is used to set up a first particle group for minimizing F1, a second particle group for minimizing F2, a third particle group for minimizing F3, and a fourth particle group for minimizing F4, and to initialize each particle group and an external reserve set for recording the searched non-dominated solutions.

[0100] The local update unit is used to update the position and velocity of particles in each particle swarm, calculate the fitness value of new particles, and update the local guide particles of the particles.

[0101] The global update unit is used to save the local optimal solution in the new particle swarm to the external reserve set, maintain and update the external reserve set, and randomly select a particle in the updated external reserve set as the new global guiding particle.

[0102] The algorithm termination judgment unit is used to determine whether the algorithm meets the algorithm termination condition. If it does, the optimal solution output unit is executed; otherwise, the local update unit is jumped to the local update unit.

[0103] The optimal solution output unit is used to output all particles in the external reserve set as the Pareto optimal solution set corresponding to the objective function of the multi-objective optimization model.

[0104] According to one achievable method of the second aspect of the present invention, the optimal hydropower output determination module further includes:

[0105] The optimal hydropower output solution unit is used to select the optimal solution from the Pareto optimal solution set using the entropy weight method as the optimal hydropower output.

[0106] According to one achievable method of the second aspect of the present invention, the second model building module includes:

[0107] The equivalent determination unit is used to determine the equivalent load and positive / negative rotational reserve based on the power output scheme of the hydropower unit:

[0108]

[0109]

[0110]

[0111] In the formula, Pl′ tFor the load after scheduling period t, Pl t The total load during the scheduling period t, For the optimal hydropower output, P W,t For the predicted wind power output during the dispatch period t, where T represents the last dispatch period of the day, G... h R″ is a collection of hydroelectric generating units. t,up R′ is reserved for the forward rotation after the equivalent value. t,up p is reserved for the corrected forward rotation. hg,max R″ represents the maximum output of the hydroelectric generator unit hg. t,down p is reserved for the negative rotation after equivalence. hg,min This represents the minimum output of the hydroelectric generator unit hg;

[0112] The first building unit, used to construct the power balance constraints, is:

[0113]

[0114] In the formula, u g,t p represents the start-up and shutdown status of thermal power unit g during dispatch period t. g,t Let G be the power generation capacity of thermal power unit g during the dispatch period t, and G be the set of thermal power units.

[0115] The second building unit is used to construct the upper and lower limit constraints of the thermal power unit's output:

[0116] u g,t p g,min ≤p g,t ≤u g,t p g,max ,t=1,2,…,T

[0117] In the formula, p g,min p is the lower limit of the output of thermal power unit g. g,max This represents the upper limit of the output of thermal power unit g;

[0118] The third building unit is used to construct the ramp-up power constraint for thermal power units as follows:

[0119]

[0120] In the formula, p g,t-1 Let g be the power generation capacity of thermal power unit g during the dispatch period t-1. This represents the maximum upward climbing power of thermal power unit g. The maximum downward climbing power of thermal power unit g;

[0121] The fourth building block, used to construct the spare capacity constraint, is:

[0122]

[0123]

[0124] The fifth construction unit, used to construct the day-ahead scheduling model of the thermal power units, has the following objective function:

[0125]

[0126] In the formula, f represents the objective function, a g Let b be the secondary power generation cost coefficient of thermal power unit g. g Let c be the primary power generation cost coefficient for thermal power unit g. g U is a constant power generation cost coefficient for thermal power unit g. g,t-1 S represents the start-up and shutdown status of thermal power unit g during the dispatch period t-1. g The start-up and shutdown costs for thermal power unit g.

[0127] A third aspect of the present invention provides a day-ahead dispatching device for thermal power units that coordinates the uncertainty of wind power with hydropower, comprising:

[0128] A memory for storing instructions; wherein the instructions are used to implement the day-ahead scheduling method for thermal power units that coordinates hydropower with wind power uncertainty as described in any of the above-mentioned methods;

[0129] A processor for executing instructions in the memory.

[0130] The fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the day-ahead scheduling method for thermal power units that coordinates hydropower with wind power uncertainty as described in any of the above embodiments.

[0131] As can be seen from the above technical solutions, the present invention has the following advantages:

[0132] This invention considers wind power output prediction error factors and uses the kernel density method to correct the spinning reserve constraint. Then, based on minimizing the maximum value of equivalent load change between adjacent times, minimizing the variance of equivalent load across all time periods, minimizing the maximum value of positive spinning reserve across all time periods, and minimizing the maximum value of negative spinning reserve across all time periods, a multi-objective optimization model for hydropower unit output is established. The Pareto optimal solution set for hydropower unit output is obtained using a multi-swarm particle swarm optimization algorithm, and the hydropower unit output scheme is derived from it. Based on this scheme, a day-ahead scheduling model for thermal power units considering wind power output uncertainty and hydropower coordination is constructed and solved to obtain the day-ahead scheduling plan for thermal power units. This invention can fully utilize the advantages of hydropower and overcome the problems of strong fluctuations and large prediction errors in wind power generation, thereby effectively avoiding problems such as wind curtailment and load shedding caused by insufficient ramp-up capacity or insufficient reserve in the day-ahead scheduling plan for thermal power units. Attached Figure Description

[0133] 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.

[0134] Figure 1 A flowchart of a day-ahead scheduling method for thermal power units that coordinates hydropower with wind power uncertainty, provided as an optional embodiment of the present invention;

[0135] Figure 2 The flowchart illustrates, in an optional embodiment of the present invention, the solution of the multi-objective optimization model using a multi-swarm particle swarm optimization algorithm.

[0136] Figure 3 The diagram below shows the structural connection of a day-ahead dispatching device for thermal power units that coordinates the uncertainty of wind power with hydropower, as provided in an optional embodiment of the present invention.

[0137] Figure label:

[0138] 1-Positive and negative rotation backup correction module; 2-First model construction module; 3-Optimal hydropower output determination module; 4-Second model construction module; 5-Optimization solution module. Detailed Implementation

[0139] This invention provides a method and apparatus for day-ahead scheduling of thermal power units to coordinate the uncertainty of wind power generation using hydropower, which is used to solve the technical problem of how to make full use of the advantages of hydropower to coordinate and solve the uncertainty of wind power generation when carrying out day-ahead scheduling of thermal power units.

[0140] 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.

[0141] This invention provides a day-ahead dispatching method for thermal power units that coordinates hydropower with the uncertainty of wind power. The method of this application can be implemented based on a terminal with data processing capabilities, such as a computer, server, or cloud platform.

[0142] Please see Figure 1 , Figure 1The flowchart illustrates a day-ahead scheduling method for thermal power units that coordinates hydropower with wind power uncertainties, provided by an embodiment of the present invention.

[0143] The present invention provides a day-ahead dispatching method for thermal power units that coordinates hydropower with wind power uncertainty, comprising steps S1-S5.

[0144] Step S1: Considering the wind power output prediction error, the positive and negative spinning reserves under each scheduling period are corrected using the kernel density estimation method to obtain the corrected positive and negative spinning reserves under each scheduling period.

[0145] Understandably, due to the small margins of positive and negative spinning reserves, wind power output is uncertain, so it is necessary to adjust the positive and negative spinning reserves for each dispatch period.

[0146] As one feasible approach, kernel density estimation is used to correct positive and negative spin reserves for each scheduling period, including:

[0147] For each scheduling period, obtain the predicted and actual renewable energy output data of the wind turbines for the corresponding scheduling period in historical days;

[0148] Based on the new energy power output forecast data and the new energy power output data, determine the historical forecast error data of the wind turbine units for the corresponding scheduling periods in each historical day;

[0149] Kernel density estimation and fitting are performed on the historical prediction error data to obtain the corresponding probability density function, and the first quantile and the second quantile of the probability density function are determined.

[0150] The positive rotation reserve and negative rotation reserve are corrected based on the first quantile and the second quantile to obtain the corrected positive rotation reserve and the corrected negative rotation reserve.

[0151] For wind power output, the prediction error for each scheduling period can be obtained through historical data. Specifically, let the historical predicted output values ​​of new energy sources for each day during scheduling period t be... The corresponding actual output is The corresponding historical prediction error data can then be obtained as follows:

[0152]

[0153] In the formula, Let represent the historical prediction error data of the i-th sample during the scheduling period t, where i = 1, 2, ..., N, and N is the number of samples.

[0154] When performing kernel density estimation fitting on the historical prediction error data, specifically, according to the kernel density estimation principle, the kernel estimation formula for the load prediction error in the t-th scheduling period of the next 24 hours can be obtained as follows:

[0155]

[0156] In the formula, Let h be the kernel estimate of the load forecast error for the t-th scheduling period in the next 24 hours, where h is the bandwidth, K(·) is the kernel function, and t = 1, 2, ..., T, where T represents the last scheduling period of the day.

[0157] T can be set according to the actual day-ahead scheduling of thermal power units. As a specific implementation method, a 24-hour day can be considered, with one point considered every 15 minutes of each hour, for a total of 96 points per day, then T = 96.

[0158] There are various options for choosing a kernel function. In this specific implementation, a Gaussian kernel function is selected. According to the widely used MISE (mean integrated mean squared error) method, the optimal bandwidth when K(·) is a Gaussian kernel function is:

[0159] h = 1.06 N -1 / 5 σ

[0160] In the formula, σ is the set of error samples. The standard deviation.

[0161] By combining the formulas for kernel estimation, Gaussian kernel function, and optimal bandwidth, the probability density function of the load forecast error for the t-th time period of the second day can be obtained.

[0162] Find the probability density function Afterwards, Substituting into the following formula, the first quantile can be obtained. Second quantile

[0163]

[0164]

[0165] In the formula, The confidence level.

[0166] As one feasible approach, the positive and negative rotational reserves are corrected according to the following formula:

[0167]

[0168]

[0169] In the formula, R′ t,up This indicates the corrected forward rotation is ready for use, R′ t,down Indicates the corrected negative rotation is ready for use, R t,up R represents the positive reserve capacity requirement of the system during scheduling period t. t,down p represents the system's negative reserve capacity requirement during scheduling period t. RE,t The predicted renewable energy output of wind turbines during the dispatch period t.

[0170] Step S2: Based on the corrected positive rotating reserve and the corrected negative rotating reserve, a multi-objective optimization model for the output of the hydropower unit is established with the optimization objectives of minimizing the maximum value of the equivalent load change between adjacent times, minimizing the variance of the equivalent load in all time periods, minimizing the maximum value of the positive rotating reserve in all time periods, and minimizing the maximum value of the negative rotating reserve in all time periods.

[0171] To reduce fluctuations between adjacent time periods and lower the ramp-up difficulty for thermal power units, the first objective is defined as the maximum equivalent load change between adjacent time periods, and this maximum equivalent load change between adjacent time periods should be minimized. In one feasible approach, the maximum equivalent load change between adjacent time periods is set as follows:

[0172]

[0173] In the formula, F1 represents the maximum value of the equivalent load change between adjacent time periods, and Pl t p represents the total load during the scheduling period t. hg,t For the output of the hydropower unit hg during the dispatch period t, P W,t To predict the wind power output during the dispatch period t, Pl t+1 For the total load during scheduling period t+1, p hg,t+1 To determine the output of the hydropower unit hg during the dispatch period t+1, P W,t+1 For the predicted wind power output during the dispatch period t+1, where T represents the last dispatch period of the day, G... h It is a collection of hydroelectric generating units.

[0174] To reduce fluctuations between adjacent time periods and lower the ramp-up difficulty for thermal power units, a second objective is defined as the variance of the equivalent load across all time periods, and this variance should be minimized as much as possible. In one feasible approach, the variance of the equivalent load across all time periods is set as follows:

[0175]

[0176] in:

[0177]

[0178] In the formula, F2 represents the variance of the equivalent load over all time periods, and Pl ave This represents the average equivalent load difference across all time periods.

[0179] To reduce the difficulty of maintaining positive rotation reserve for thermal power units, a third objective is defined as the maximum positive rotation reserve over all time periods, and this objective should be minimized as much as possible. In one feasible approach, the maximum positive rotation reserve over all time periods is set as follows:

[0180]

[0181] In the formula, F3 represents the maximum positive rotation reserve value for all time periods, and R′ t,up p is reserved for the corrected forward rotation. hg,max This is the maximum output of the hydroelectric generator unit hg.

[0182] To reduce the difficulty of maintaining negative spinning reserve in thermal power units, a fourth target, F4, is defined as the maximum negative spinning reserve over all time periods, and this target value is minimized as much as possible. In one feasible approach, the maximum negative spinning reserve over all time periods is set as follows:

[0183]

[0184] In the formula, F4 represents the maximum negative rotation reserve for all time periods, and R′ t,down p is reserved for the corrected negative rotation. hg,min This is the minimum output of the hydroelectric generator unit hg.

[0185] Based on the above settings, in this embodiment, the objective function of the multi-objective optimization model is set to min(F1, F2, F3, F4).

[0186] Step S3: Solve the multi-objective optimization model using a multi-swarm particle swarm optimization algorithm to determine the Pareto optimal solution set for the hydropower unit output. Select the optimal solution from the Pareto optimal solution set as the optimal hydropower output to obtain the hydropower unit output scheme.

[0187] In one feasible way, such as Figure 2 As shown, the method of using a multi-swarm particle swarm optimization algorithm to solve the multi-objective optimization model and determine the Pareto optimal solution set for the hydropower unit output includes:

[0188] Step S10: Set up a first particle group for minimizing F1, a second particle group for minimizing F2, a third particle group for minimizing F3, and a fourth particle group for minimizing F4; initialize each particle group and an external reserve set for recording the searched non-dominated solutions.

[0189] Step S20: Update the position and velocity of particles in each particle swarm, calculate the fitness value of newly generated particles, and update the local guide particles of the particles.

[0190] Step S30: Save the local optimal solution in the new particle swarm to the external reserve set, maintain and update the external reserve set, and randomly select a particle in the updated external reserve set as the new global guiding particle.

[0191] Step S40: Determine whether the algorithm meets the algorithm termination condition. If it does, proceed to step S50; otherwise, jump to step S20.

[0192] Step S50: Output all particles in the external reserve set as the Pareto optimal solution set corresponding to the objective function of the multi-objective optimization model.

[0193] Let the first particle swarm, the second particle swarm, the third particle swarm, and the fourth particle swarm be S1 = {q} i,1 , i=1,2,...,N′}, S2={q i,2 , i=1,2,...,N′}, S3={q i,3 , i=1,2,...,N′}, S4={q i,4 Let i = 1, 2, ..., N′. Here, N′ is the number of particles. The velocity of each particle in each particle swarm is shown in the following equation:

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202] In the formula, each row of each matrix represents the output of each hydropower unit in T time periods.

[0203] And it satisfies:

[0204] p hg,min ≤q i,hg,t,1 ,q i,hg,t,2 ,qi,hg,t,3 ,q i,hg,t,4 ≤p hg,max

[0205] -p hg,max ≤v i,hg,t,1 ,v i,hg,t,2 ,v i,hg,t,3 ,v i,hg,t,4 ≤p hg,max

[0206] In the formula, p hg,max p is the maximum output of the hydroelectric generator unit hg. hg,min This is the minimum output of the hydroelectric generator unit hg.

[0207] The process of initializing each particle swarm and the external reserve set used to record the non-dominated solutions found includes:

[0208] Randomly generate particle positions in a given variable space, initialize particle velocities, calculate the fitness value of each particle, and save the non-dominated solutions in the particle swarm to an external reserve set. Use the following crowding method to select the particle with the highest crowding in the reserve set as the global guide particle.

[0209] Assume there are currently N external reserves in total. gl For each particle, the particles in the external reserve set are individually sorted in ascending order of their objective function values, and then the crowding degree of each particle is calculated. For example, for the i-th particle in the external reserve set, which is at position l after being sorted by the j-th objective function, its crowding degree is calculated using the following formula:

[0210]

[0211] In the formula, d i,j F represents the crowding degree of the i-th particle in the external reserve set after sorting by the j-th objective function. j,max F is the maximum value of the objective function. j,min F is the minimum value of the objective function. j,1 F is the objective function value that is in the first position after sorting the j-th objective function. j,2 F is the objective function value that is in the second position after sorting the j-th objective function. j,l-1 F represents the objective function value corresponding to the (l-1)th position after sorting the j-th objective function. j,l+1 Let $\mathbf{j}$ be the objective function value corresponding to the (l+1)th position after sorting the $j$-th objective function. To be in the Nth position after sorting the j-th objective function gl The objective function value corresponding to the position -1 To be in the Nth position after sorting the j-th objective functiongl The objective function value corresponding to the position.

[0212] Then the combined crowding degree d of the i-th particle in the external reserve set under the four objective functions is... i for:

[0213]

[0214] The particles in the external reserve set with the highest crowding are selected as the global guide particles for the evolution of each sub-particle group.

[0215] Specifically, in step S20, if the newly formed particle is not dominated by its local optimal solution, then it becomes the local guiding particle of the original particle.

[0216] Specifically, in step S30, when maintaining and updating the external reserve set, non-dominated solutions are retained, and the rest are removed.

[0217] The iterative evolution formula is shown below (using particle swarm S1 as an example; other particle swarms are the same as particle swarm S1):

[0218]

[0219]

[0220] In the formula, Let be the velocity of the i-th particle in particle swarm S1 during the (k+1)-th iteration. For the particle swarm S1 i The velocity of a particle in the kth iteration Let S be the locally best solution obtained by the i-th particle in particle swarm S1 up to the k-th iteration. The global guiding particle selected from the external reserve set in the k-th iteration. Let i be the i-th particle in the particle swarm S1 at the k-th iteration. Let ω be the i-th particle of the particle swarm S1 in the (k+1)-th iteration, where ω, c1, and c2 are fixed constants, and r1 and r2 are random numbers in the range [0,1].

[0221] After updating each particle, update the local optimal solution obtained by each particle after k+1 iterations. Add the local optimal solution obtained by each particle to the external reserve set (excluding duplicates), and maintain and update the external reserve set.

[0222] In one feasible approach, the optimal solution is selected from the Pareto optimal solution set using the entropy weight method as the optimal hydropower output.

[0223] Specifically, assuming there are M Parato optimal solutions in the Pareto optimal solution set, the final hydropower unit output scheme is obtained using the entropy weight method, denoted as... The optimal solution selected from the Pareto optimal solution set is the optimal hydropower output, including:

[0224] The four objective functions of each Parato optimal solution are normalized; with F1 as the first objective function, the normalization of the first objective function is as follows:

[0225]

[0226] In the formula, f1 represents the normalized value of the first objective function, F1 max This represents the threshold of the first objective function;

[0227] Using F2 as the second objective function, the normalization process for the second objective function is as follows:

[0228]

[0229] In the formula, f2 represents the normalized value of the second objective function. This represents the threshold of the second objective function;

[0230] Using F3 as the third objective function, the normalization of the third objective function is as follows:

[0231]

[0232] In the formula, f3 represents the normalized value of the third objective function. This represents the threshold of the third objective function;

[0233] Using F4 as the fourth objective function, the normalization process for the fourth objective function is as follows:

[0234]

[0235] In the formula, f4 represents the normalized value of the second objective function. This represents the threshold of the second objective function;

[0236] The normalized values ​​of the four objective functions of the M Parato optimal solutions are standardized as follows:

[0237]

[0238] Calculate the entropy values ​​of the four objective function indices for the M Parato optimal solutions. The smaller the entropy value, the greater the role of that index in the overall evaluation.

[0239]

[0240] In the formula, e1 is the entropy value of the first objective function index, e2 is the entropy value of the second objective function index, e3 is the entropy value of the third objective function index, and e4 is the entropy value of the fourth objective function index.

[0241] If we define 0 × ln0 = 0, then λ1 to λ4 are shown in the following equations:

[0242]

[0243] The comprehensive evaluation index of the M Parato optimal solutions is calculated according to the following formula:

[0244] I i =λ1f 1,i +λ2f 2,i +λ3f 3,i +λ4f 4,i

[0245] In the formula, I i This represents the comprehensive evaluation index of the i-th Parato optimal solution.

[0246] The larger the value of the comprehensive evaluation index, the more desirable the solution is. The solution with the largest value of the comprehensive evaluation index is taken as the optimal hydropower output.

[0247] In this embodiment, the optimal hydropower output is determined based on the entropy weight method, ensuring the objectivity of the optimal solution. It should be noted that in other implementations, other existing methods, such as the subjective weight method, can also be used to determine the optimal hydropower output from the Parato optimal solution set.

[0248] Step S4: Based on the power output scheme of the hydropower unit, construct model constraints and with the goal of minimizing the total operating cost of the power system, construct a day-ahead dispatch model for thermal power units containing hydropower and wind power.

[0249] In one feasible approach, the model constraint is constructed based on the hydropower unit output scheme, and a day-ahead dispatch model for thermal power units containing hydropower and wind power is constructed with the objective function of minimizing the total operating cost of the power system. This includes:

[0250] Based on the aforementioned hydropower unit output scheme, the equivalent load and positive / negative rotational reserve are determined as follows:

[0251]

[0252]

[0253]

[0254] In the formula, Pl′ tFor the load after scheduling period t, Pl t The total load during the scheduling period t, For the optimal hydropower output, P W,t For the predicted wind power output during the dispatch period t, where T represents the last dispatch period of the day, G... h R″ is a collection of hydroelectric generating units. t,up R′ is reserved for the forward rotation after the equivalent value. t,up p is reserved for the corrected forward rotation. hg,max R″ represents the maximum output of the hydroelectric generator unit hg. t,down p is reserved for the negative rotation after equivalence. hg,min This represents the minimum output of the hydroelectric generator unit hg;

[0255] The power balance constraints are constructed as follows:

[0256]

[0257] In the formula, u g,t p represents the start-up and shutdown status of thermal power unit g during dispatch period t. g,t Let G be the power generation capacity of thermal power unit g during the dispatch period t, and G be the set of thermal power units.

[0258] The upper and lower limits of the output of thermal power units are constrained as follows:

[0259] u g,t p g,min ≤p g,t ≤u g,t p g,max ,t=1,2,...,T

[0260] In the formula, p g,min p is the lower limit of the output of thermal power unit g. g,max This represents the upper limit of the output of thermal power unit g;

[0261] The power ramping constraints for thermal power units are as follows:

[0262]

[0263] In the formula, p g,t-1 Let g be the power generation capacity of thermal power unit g during the dispatch period t-1. This represents the maximum upward climbing power of thermal power unit g. The maximum downward climbing power of thermal power unit g;

[0264] The constraints for setting up standby capacity are as follows:

[0265]

[0266]

[0267] The objective function for constructing the day-ahead scheduling model of the thermal power units is:

[0268]

[0269] In the formula, a g Let b be the secondary power generation cost coefficient of thermal power unit g. g Let c be the primary power generation cost coefficient for thermal power unit g. g U is a constant power generation cost coefficient for thermal power unit g. g,t-1 S represents the start-up and shutdown status of thermal power unit g during the dispatch period t-1. g The start-up and shutdown costs for thermal power unit g.

[0270] In this embodiment, the equivalent load and positive and negative rotating reserves are determined based on the power output scheme of the hydropower unit, and a reserve capacity constraint is set on this basis. This allows each thermal power unit to have a greater margin in power output scheduling under the modified positive and negative rotating reserve constraint, and reduces the uncertainty of wind power output.

[0271] Step S5: Optimize and solve the day-ahead scheduling model of the thermal power units to obtain the day-ahead scheduling plan of the thermal power units.

[0272] The day-ahead scheduling model for thermal power units is an optimization problem with a quadratic objective function and linear constraints. It can be solved using mature mathematical methods and commercial software, such as quadratic programming and CPLEX software.

[0273] The present invention also provides a day-ahead dispatching device for thermal power units that coordinates with hydropower to address wind power uncertainties. This device can be used to execute the day-ahead dispatching method for thermal power units that coordinates with hydropower to address wind power uncertainties as described in any of the above embodiments of the present invention.

[0274] Please see Figure 3 , Figure 3 The diagram shows a structural connection block diagram of a day-ahead dispatching device for thermal power units that coordinates the uncertainty of wind power with hydropower, according to an embodiment of the present invention.

[0275] This invention provides a day-ahead dispatching device for thermal power units that coordinates hydropower with wind power uncertainties, comprising:

[0276] Positive and negative spinning reserve correction module 1 is used to take into account the wind power output prediction error and use the kernel density estimation method to correct the positive and negative spinning reserves under each scheduling period, so as to obtain the corrected positive and negative spinning reserves under each scheduling period.

[0277] The first model construction module 2 is used to establish a multi-objective optimization model for the output of hydropower units based on the corrected positive rotating reserve and the corrected negative rotating reserve, with the optimization objectives being to minimize the maximum value of the equivalent load change between adjacent times, minimize the variance of the equivalent load in all time periods, minimize the maximum value of the positive rotating reserve in all time periods, and minimize the maximum value of the negative rotating reserve in all time periods.

[0278] The optimal hydropower output determination module 3 is used to solve the multi-objective optimization model using a multi-swarm particle swarm algorithm, determine the Pareto optimal solution set for the hydropower unit output, select the optimal solution from the Pareto optimal solution set as the optimal hydropower output, and obtain the hydropower unit output scheme.

[0279] The second model construction module 4 constructs model constraints based on the power output scheme of the hydropower unit, and constructs a day-ahead dispatch model for thermal power units containing hydropower and wind power with the objective function of minimizing the total operating cost of the power system.

[0280] The optimization solution module 5 is used to optimize and solve the day-ahead scheduling model of the thermal power units to obtain the day-ahead scheduling plan of the thermal power units.

[0281] In one feasible manner, the positive and negative rotation backup correction module 1 includes:

[0282] The data acquisition unit is used to acquire, for each scheduling period, the predicted and actual output data of new energy for wind turbines in the corresponding scheduling periods of historical days.

[0283] The prediction error determination unit is used to determine the historical prediction error data of the wind turbine for the corresponding scheduling period in each historical day based on the new energy power output prediction data and the new energy actual power output data.

[0284] The quantile determination unit is used to perform kernel density estimation fitting on the historical prediction error data to obtain the corresponding probability density function, and to determine the first quantile and the second quantile of the probability density function.

[0285] The positive and negative rotation reserve correction unit is used to correct the positive rotation reserve and the negative rotation reserve according to the first quantile and the second quantile, so as to obtain the corrected positive rotation reserve and the corrected negative rotation reserve.

[0286] In one feasible manner, the positive and negative rotation backup correction unit is specifically used for:

[0287] Let the first quantile be The second quantile is in To determine the confidence level, the positive and negative spin-off reserves are corrected according to the following formula:

[0288]

[0289]

[0290] In the formula, R′ t,up This indicates the corrected forward rotation is ready for use, R′ t,down Indicates the corrected negative rotation is ready for use, R t,up R represents the positive reserve capacity requirement of the system during scheduling period t. t,down p represents the system's negative reserve capacity requirement during scheduling period t. RE,t The predicted renewable energy output of wind turbines during the dispatch period t.

[0291] In one feasible implementation, the first model building module 2 includes:

[0292] The first setting unit is used to set the maximum value of the equivalent load change over adjacent time periods as follows:

[0293]

[0294] In the formula, F1 represents the maximum value of the equivalent load change between adjacent time periods, and Pl t p represents the total load during the scheduling period t. hg,t For the output of the hydropower unit hg during the dispatch period t, P W,t To predict the wind power output during the dispatch period t, Pl t+1 For the total load during scheduling period t+1, p hg,t+1 To determine the output of the hydropower unit hg during the dispatch period t+1, P W,t+1 For the predicted wind power output during the dispatch period t+1, where T represents the last dispatch period of the day, G... h A collection of hydroelectric generating units;

[0295] The second setting unit is used to set the variance of the equivalent load for all time periods as follows:

[0296]

[0297] in:

[0298]

[0299] In the formula, F2 represents the variance of the equivalent load over all time periods, and Pl ave This represents the average equivalent load difference across all time periods.

[0300] The third setting unit is used to set the maximum positive rotation reserve value for all time periods as follows:

[0301]

[0302] In the formula, F3 represents the maximum positive rotation reserve value for all time periods, and R′ t,up p is reserved for the corrected forward rotation. hg,max This is the maximum output of the hydroelectric generator unit hg;

[0303] The fourth setting unit is used to set the maximum negative rotation reserve value for all time periods as follows:

[0304]

[0305] In the formula, F4 represents the maximum negative rotation reserve for all time periods, and R′ t,down p is reserved for the corrected negative rotation. hg,min This represents the minimum output of the hydroelectric generator unit hg;

[0306] The fifth setting unit is used to set the objective function of the multi-objective optimization model to min(F1, F2, F3, F4).

[0307] In one feasible manner, the optimal hydropower output determination module 3 includes:

[0308] An initialization unit is used to set up a first particle group for minimizing F1, a second particle group for minimizing F2, a third particle group for minimizing F3, and a fourth particle group for minimizing F4, and to initialize each particle group and an external reserve set for recording the searched non-dominated solutions.

[0309] The local update unit is used to update the position and velocity of particles in each particle swarm, calculate the fitness value of new particles, and update the local guide particles of the particles.

[0310] The global update unit is used to save the local optimal solution in the new particle swarm to the external reserve set, maintain and update the external reserve set, and randomly select a particle in the updated external reserve set as the new global guiding particle.

[0311] The algorithm termination judgment unit is used to determine whether the algorithm meets the algorithm termination condition. If it does, the optimal solution output unit is executed; otherwise, the local update unit is jumped to the local update unit.

[0312] The optimal solution output unit is used to output all particles in the external reserve set as the Pareto optimal solution set corresponding to the objective function of the multi-objective optimization model.

[0313] In one feasible implementation, the optimal hydropower output determination module 3 further includes:

[0314] The optimal hydropower output solution unit is used to select the optimal solution from the Pareto optimal solution set using the entropy weight method as the optimal hydropower output.

[0315] In one feasible implementation, the second model building module 4 includes:

[0316] The equivalent determination unit is used to determine the equivalent load and positive / negative rotational reserve based on the power output scheme of the hydropower unit:

[0317]

[0318]

[0319]

[0320] In the formula, Pl′ t For the load after scheduling period t, Pl t The total load during the scheduling period t, For the optimal hydropower output, P W,t For the predicted wind power output during the dispatch period t, where T represents the last dispatch period of the day, G... h R″ is a collection of hydroelectric generating units. t,up R′ is reserved for the forward rotation after the equivalent value. t,up p is reserved for the corrected forward rotation. hg,max R″ represents the maximum output of the hydroelectric generator unit hg. t,down p is reserved for the negative rotation after equivalence. hg,min This represents the minimum output of the hydroelectric generator unit hg;

[0321] The first building unit, used to construct the power balance constraints, is:

[0322]

[0323] In the formula, u g,t p represents the start-up and shutdown status of thermal power unit g during dispatch period t. g,t Let G be the power generation capacity of thermal power unit g during the dispatch period t, and G be the set of thermal power units.

[0324] The second building unit is used to construct the upper and lower limit constraints of the thermal power unit's output:

[0325] u g,t p g,min ≤p g,t ≤u g,t p g,max ,t=1,2,...,T

[0326] In the formula, p g,min p is the lower limit of the output of thermal power unit g.g,max This represents the upper limit of the output of thermal power unit g;

[0327] The third building unit is used to construct the ramp-up power constraint for thermal power units as follows:

[0328]

[0329] In the formula, p g,t-1 Let g be the power generation capacity of thermal power unit g during the dispatch period t-1. This represents the maximum upward climbing power of thermal power unit g. The maximum downward climbing power of thermal power unit g;

[0330] The fourth building block, used to construct the spare capacity constraint, is:

[0331]

[0332]

[0333] The fifth construction unit, used to construct the day-ahead scheduling model of the thermal power units, has the following objective function:

[0334]

[0335] In the formula, a g Let b be the secondary power generation cost coefficient of thermal power unit g. g Let c be the primary power generation cost coefficient for thermal power unit g. g U is a constant power generation cost coefficient for thermal power unit g. g,t-1 S represents the start-up and shutdown status of thermal power unit g during the dispatch period t-1. g The start-up and shutdown costs for thermal power unit g.

[0336] The present invention also provides a day-ahead dispatching device for thermal power units that coordinates the uncertainty of wind power with hydropower, comprising:

[0337] A memory for storing instructions; wherein the instructions are used to implement the day-ahead scheduling method for thermal power units with uncertainties in coordinating hydropower and wind power as described in any of the above embodiments;

[0338] A processor for executing instructions in the memory.

[0339] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the day-ahead scheduling method for thermal power units that coordinates hydropower with wind power uncertainties as described in any of the above embodiments.

[0340] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and the specific beneficial effects of the devices, modules, and units described above can be referred to the corresponding beneficial effects in the foregoing method embodiments, and will not be repeated here.

[0341] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0342] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0343] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0344] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0345] 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 day-ahead dispatching method for thermal power units that coordinates the uncertainties of wind power with hydropower, characterized in that, include: Considering the wind power output prediction error, the kernel density estimation method is used to correct the positive and negative spinning reserves for each scheduling period, resulting in the corrected positive and negative spinning reserves for each scheduling period. Based on the corrected positive rotating reserve and the corrected negative rotating reserve, a multi-objective optimization model for the output of hydropower units is established with the optimization objectives of minimizing the maximum value of the equivalent load change between adjacent times, minimizing the variance of the equivalent load in all time periods, minimizing the maximum value of the positive rotating reserve in all time periods, and minimizing the maximum value of the negative rotating reserve in all time periods. The multi-objective optimization model is solved using a multi-swarm particle swarm optimization algorithm to determine the Pareto optimal solution set for the hydropower unit output. The optimal solution is selected from the Pareto optimal solution set as the optimal hydropower output, thus obtaining the hydropower unit output scheme. Based on the power output scheme of the hydropower unit, a model constraint is constructed, and with the minimization of the total operating cost of the power system as the objective function, a day-ahead dispatch model for thermal power units containing hydropower and wind power is constructed. The daytime scheduling model of the thermal power units is optimized and solved to obtain the daytime scheduling plan of the thermal power units; The method considers wind power output prediction errors and uses kernel density estimation to correct the positive and negative spinning reserves for each scheduling period, resulting in corrected positive and negative spinning reserves for each scheduling period, including: For each scheduling period, obtain the predicted and actual renewable energy output data of the wind turbines for the corresponding scheduling period in historical days; Based on the new energy power output forecast data and the new energy power output data, determine the historical forecast error data of the wind turbine units for the corresponding scheduling periods in each historical day; Kernel density estimation and fitting are performed on the historical prediction error data to obtain the corresponding probability density function, and the first quantile and the second quantile of the probability density function are determined. The positive rotation reserve and negative rotation reserve are corrected based on the first quantile and the second quantile to obtain the corrected positive rotation reserve and the corrected negative rotation reserve. The step of correcting the positive rotation reserve and negative rotation reserve based on the first quantile and the second quantile to obtain the corrected positive rotation reserve and the corrected negative rotation reserve includes: Let the first quantile be The second quantile is ,in To determine the confidence level, the positive and negative spin-off reserves are corrected according to the following formula: In the formula, This indicates that the corrected forward rotation is ready for use. This indicates that the corrected negative rotation is ready for use. For the system during the scheduling period Positive reserve capacity requirements, For the system during the scheduling period Negative reserve capacity requirements For wind turbine units during the dispatch period The forecast output of renewable energy is as follows; Based on the corrected positive and negative rotating reserves, a multi-objective optimization model for hydropower unit output is established with the optimization objectives of minimizing the maximum value of equivalent load change between adjacent time periods, minimizing the variance of equivalent load across all time periods, minimizing the maximum value of positive rotating reserves across all time periods, and minimizing the maximum value of negative rotating reserves across all time periods. This model includes: The maximum value of the equivalent load change between adjacent time periods is set as follows: In the formula, This represents the maximum value of the equivalent load change over adjacent time periods. For scheduling period Total load, In order to schedule hydroelectric units of efforts, In order to schedule Wind power forecast output, For scheduling period Total load, In order to schedule hydroelectric units of efforts, In order to schedule Wind power forecast output, This indicates the last scheduling period of the day. A collection of hydroelectric generating units; The variance of the equivalent load for all time periods is set as follows: in: In the formula, This represents the variance of the equivalent load over all time periods. This represents the average equivalent load difference across all time periods. Set the maximum forward rotation reserve value for all time periods to: In the formula, This represents the maximum positive rotation reserve value across all time periods. Prepared for the corrected forward rotation. For hydroelectric generator units Maximum output; The maximum negative rotation reserve value for all time periods is set as follows: In the formula, This represents the maximum negative rotational reserve value across all time periods. This is for use as a corrected negative rotation. For hydroelectric generator units Minimum output; The objective function of the multi-objective optimization model is set as follows: .

2. The day-ahead dispatching method for thermal power units with uncertainties in coordinating hydropower and wind power according to claim 1, characterized in that, The process of solving the multi-objective optimization model using a multi-swarm particle swarm optimization algorithm to determine the Pareto optimal solution set for the hydropower unit output includes: Step S10: Set up a first particle group with the aim of minimizing the maximum value of the equivalent load change between adjacent time periods, a second particle group with the aim of minimizing the variance of the equivalent load in all time periods, a third particle group with the aim of minimizing the maximum value of the positive rotating reserve in all time periods, and a fourth particle group with the aim of minimizing the maximum value of the negative rotating reserve in all time periods; initialize each particle group and an external reserve set for recording the non-dominated solutions found. Step S20: Update the position and velocity of particles in each particle swarm, calculate the fitness value of newly generated particles, and update the local guide particles of the particles. Step S30: Save the local optimal solution in the new particle swarm to the external reserve set, maintain and update the external reserve set, and randomly select a particle in the updated external reserve set as the new global guiding particle. Step S40: Determine whether the algorithm meets the algorithm termination condition. If it does, proceed to step S50; otherwise, jump to step S20. Step S50: Output all particles in the external reserve set as the Pareto optimal solution set corresponding to the objective function of the multi-objective optimization model.

3. The day-ahead dispatching method for thermal power units with uncertainties in coordinating hydropower and wind power according to claim 2, characterized in that, The step of selecting the optimal solution from the Pareto optimal solution set as the optimal hydropower output includes: The optimal solution is selected from the Pareto optimal solution set using the entropy weight method as the optimal hydropower output.

4. The day-ahead dispatching method for thermal power units with uncertainties in coordinating hydropower and wind power according to claim 1, characterized in that, The model constraint is constructed based on the power output scheme of the hydropower unit, and the objective function is to minimize the total operating cost of the power system. A day-ahead dispatch model for thermal power units containing hydropower and wind power is then constructed, including: Based on the aforementioned hydropower unit output scheme, the equivalent load and positive / negative rotational reserve are determined as follows: In the formula, The load after the scheduling period t is equalized. For scheduling period Total load, For the optimal hydropower output, In order to schedule Wind power forecast output, This indicates the last scheduling period of the day. A collection of hydroelectric generating units. For use as the equivalent forward rotation, Prepared for the corrected forward rotation. For hydroelectric generator units Maximum output Reserved for the negative rotation after the equivalent value. For hydroelectric generator units Minimum output; The power balance constraints are constructed as follows: In the formula, For thermal power units During the scheduling period Start-stop status, For thermal power units During the scheduling period The power generation capacity, A collection of thermal power units; The upper and lower limits of the output of thermal power units are constrained as follows: In the formula, For thermal power units The lower limit of output, For thermal power units The upper limit of output; The power ramping constraints for thermal power units are as follows: In the formula, For thermal power units During the scheduling period The power generation capacity, For thermal power units Maximum uphill power, For thermal power units Maximum downhill climbing power; The constraints for setting up standby capacity are as follows: The objective function for constructing the day-ahead scheduling model of the thermal power units is: In the formula, Describe the objective function. For thermal power units The secondary power generation cost coefficient, For thermal power units The primary power generation cost coefficient, For thermal power units The constant power generation cost coefficient, For thermal power units During the scheduling period Start-stop status, For thermal power units Start-up and shutdown costs.

5. A day-ahead dispatching device for thermal power units that coordinates the uncertainty of wind power with hydropower, characterized in that, include: The positive and negative spinning reserve correction module is used to take into account the wind power output prediction error and use the kernel density estimation method to correct the positive and negative spinning reserves for each scheduling period, so as to obtain the corrected positive and negative spinning reserves for each scheduling period. The first model construction module is used to establish a multi-objective optimization model for the output of hydropower units based on the corrected positive rotating reserve and the corrected negative rotating reserve, with the optimization objectives being to minimize the maximum value of the equivalent load change between adjacent times, the minimum variance of the equivalent load in all time periods, the minimum maximum value of the positive rotating reserve in all time periods, and the minimum maximum value of the negative rotating reserve in all time periods. The optimal hydropower output determination module is used to solve the multi-objective optimization model using a multi-swarm particle swarm algorithm, determine the Pareto optimal solution set for the hydropower unit output, select the optimal solution from the Pareto optimal solution set as the optimal hydropower output, and obtain the hydropower unit output scheme. The second model construction module constructs model constraints based on the power output scheme of the hydropower unit, and constructs a day-ahead dispatch model for thermal power units containing hydropower and wind power with the objective function of minimizing the total operating cost of the power system. The optimization solution module is used to optimize and solve the day-ahead scheduling model of the thermal power units to obtain the day-ahead scheduling plan of the thermal power units; The positive and negative rotation backup correction module includes: The data acquisition unit is used to acquire, for each scheduling period, the predicted and actual output data of new energy for wind turbines in the corresponding scheduling periods of historical days. The prediction error determination unit is used to determine the historical prediction error data of the wind turbine for the corresponding scheduling period in each historical day based on the new energy power output prediction data and the new energy actual power output data. The quantile determination unit is used to perform kernel density estimation fitting on the historical prediction error data to obtain the corresponding probability density function, and to determine the first quantile and the second quantile of the probability density function. The positive and negative rotation reserve correction unit is used to correct the positive rotation reserve and the negative rotation reserve according to the first quantile and the second quantile, so as to obtain the corrected positive rotation reserve and the corrected negative rotation reserve. The positive and negative rotation backup correction unit is specifically used for: Let the first quantile be The second quantile is ,in To determine the confidence level, the positive and negative spin-off reserves are corrected according to the following formula: In the formula, This indicates that the corrected forward rotation is ready for use. This indicates that the corrected negative rotation is ready for use. For the system during the scheduling period Positive reserve capacity requirements, For the system during the scheduling period Negative reserve capacity requirements For wind turbine units during the dispatch period The forecast output of renewable energy is as follows; The first model building module includes: The first setting unit is used to set the maximum value of the equivalent load change over adjacent time periods as follows: In the formula, This represents the maximum value of the equivalent load change over adjacent time periods. For scheduling period Total load, In order to schedule hydroelectric units of efforts, In order to schedule Wind power forecast output, For scheduling period Total load, In order to schedule hydroelectric units of efforts, In order to schedule Wind power forecast output, This indicates the last scheduling period of the day. A collection of hydroelectric generating units; The second setting unit is used to set the variance of the equivalent load for all time periods as follows: in: In the formula, This represents the variance of the equivalent load over all time periods. This represents the average equivalent load difference across all time periods. The third setting unit is used to set the maximum positive rotation reserve value for all time periods as follows: In the formula, This represents the maximum positive rotation reserve value across all time periods. Prepared for the corrected forward rotation. For hydroelectric generator units Maximum output; The fourth setting unit is used to set the maximum negative rotation reserve value for all time periods as follows: In the formula, This represents the maximum negative rotational reserve value across all time periods. This is for use as a corrected negative rotation. For hydroelectric generator units Minimum output; The fifth setting unit is used to set the objective function of the multi-objective optimization model as follows: .

6. A day-ahead dispatching device for thermal power units that coordinates the uncertainty of wind power with hydropower, characterized in that, include: A memory for storing instructions; wherein the instructions are used to implement the day-ahead scheduling method for thermal power units with uncertainties in coordinating hydropower and wind power as described in any one of claims 1-4; A processor for executing instructions in the memory.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the day-ahead scheduling method for thermal power units that addresses the uncertainty of wind power in coordination with hydropower, as described in any one of claims 1-4.

Images