Scheduling optimization method and device for wind-photovoltaic mutual power generation system

Abstract

The invention discloses a wind-photovoltaic mutual power generation system scheduling optimization method and device, and belongs to the technical field of power system scheduling operation, and the method comprises the following steps: 1) obtaining equipment parameters and wind-photovoltaic operation data in a wind-photovoltaic power generation system; 2) based on the parameters and the data, combining with an optimization direction based on fitness function optimization, constructing a wind and light system equipment model, including a wind turbine output model and a photovoltaic output model; 3) after the wind-photovoltaic output model is established, simulating a scheduling scene in the wind-photovoltaic power generation system; 4) constructing a system optimization objective function of the power grid model; according to the optimization method, the power grid dispatching control system is optimized, instability caused by fluctuation of wind and photovoltaic power generation in the prior art can be solved, the robustness of the system is improved, efficiency optimization of the wind and photovoltaic power generation system is achieved, and a support is provided for building a renewable energy source power system with high energy efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching and operation, and in particular to a method and device for dispatching and optimizing a wind-photovoltaic mutual power generation system. Background Art

[0002] In the prior art, as people's demand for electric energy continues to rise, traditional power stations often use fossil energy, such as coal, to generate electricity, but this will affect our environment, resulting in an imbalance in the environment and energy structure, which needs to be urgently resolved. Therefore, fossil energy alone can no longer meet the growing demand for electricity. Users' electricity demand has evolved from daily electricity to the electricity demand of new energy vehicles. At the same time, as people's living standards improve, the requirements for electricity quality are also constantly increasing. Therefore, in the prior art, the coordinated operation and planning of multiple energy sources, including wind power generation systems, photovoltaic power generation systems, and hydropower generation systems, are also accompanied by the grid connection and operation of multiple power generation equipment, which is an important way to meet the growing needs of users. The coordinated operation of multiple energy sources to complement each other in power generation has become an important development direction for future power systems.

[0003] In the process of using multiple energy sources for power generation, it is necessary to consider the coordination of the system, factors such as the instability of the system output, and energy conversion issues; currently, in the process of wind power generation and photovoltaic power generation, the energy closed loop of hydrogen energy storage and mutual conversion of electricity and hydrogen can usually be realized through electrolyzers, hydrogen storage tanks and hydrogen fuel cells. However, the output power of the wind and solar system on the source side of the wind photovoltaic power generation system is very unstable. For example, there is solar energy during the day, but at night, solar power generation can hardly operate; in the process of wind power generation, the system will also have great instability with different wind speeds; in the existing system, these unstable factors are often rarely considered, resulting in low system operation efficiency, unstable output, and affecting the user demand side. Therefore, based on reasonable scheduling, reducing the fluctuation of the source side output and improving the stability of the system have become technical problems that need to be solved urgently. Summary of the invention

[0004] The purpose of the present invention is to solve the problem of unstable output in a wind-photovoltaic source-side wind-solar power generation system, and to propose a method and device for optimizing the scheduling of a wind-photovoltaic mutual power generation system.

[0005] On the one hand, in order to achieve the above-mentioned purpose, the present invention adopts the following method: a method for optimizing the dispatching of a wind-photovoltaic mutual power generation system, which comprises the following steps:

[0006] 1) Obtain equipment parameters and wind and photovoltaic operation data in wind and solar power generation systems;

[0007] 2) Based on the above parameters and data, combined with the optimization direction based on fitness function optimization, a wind-solar system equipment model is constructed, including a wind turbine output model and a photovoltaic output model;

[0008] 3) After establishing the wind-PV output model, simulate the dispatching scenarios in the wind-PV power generation system;

[0009] 4) constructing a system optimization objective function of the power grid model, wherein the system optimization function is a maximum fitness optimization function, and based on the optimization function, a particle swarm optimization algorithm is used to optimize the system to obtain optimization parameters;

[0010] 5) Apply the optimized parameters to the wind photovoltaic power generation system to achieve the efficiency optimization of the wind photovoltaic power generation system.

[0011] As a further description of the above technical solution:

[0012] Construct the output model of the photovoltaic array. The output model of the photovoltaic array is represented by the output power of the solar panel array, as shown below:

[0013]

[0014] Where Pm represents the rated output power of a single photovoltaic module; T represents the actual operating temperature of the photovoltaic module; T STC Indicates the operating temperature of the photovoltaic module under standard test conditions; Represents the temperature coefficient of the photovoltaic module; i m It represents the irradiance under standard test conditions; i represents the irradiance.

[0015] As a further description of the above technical solution:

[0016] In order to fully cope with the uncertainty of wind and solar power output, the relevant scenarios are simulated based on the Copula algorithm to achieve accurate model construction; the steps include:

[0017] 11) Use non-parametric kernel density estimation method to model the correlation of Copula function and determine the best Copula fitting function in each weather classification feature class;

[0018] 12) Using the kernel density estimation principle, the probability density functions of wind and solar output in each weather classification feature class are calculated as follows:

[0019]

[0020] Where: x is the hourly wind turbine output; y represents the hourly photovoltaic output; X t represents the hourly wind turbine output on the tth day of the Nth weather classification; Y trepresents the hourly PV output on the tth day of the Nth weather classification; h represents the bandwidth; K(·) represents the Gaussian kernel function.

[0021] As a further description of the above technical solution:

[0022] After constructing the probability density function, it also includes selecting the Copula function in the Archimedean family for modeling.

[0023] Where: The marginal distribution function of a single variable; C(·) is the copula connection function; F(x1, x2) is the joint distribution function of two variables.

[0024] As a further description of the above technical solution:

[0025] The couple connection function is as follows:

[0026]

[0027] In the above formula, λ is the correlation coefficient, ξ w and s are the future outputs of photovoltaic and wind energy systems, respectively.

[0028] As a further description of the above technical solution:

[0029] The fitness function is a maximum benefit fitness function, and the optimization steps are as follows:

[0030] 21) Generate uncertain scheduling scenarios for wind and solar power output based on copula function;

[0031] 22) Obtain equipment model, equipment constraints and revenue constraint indicators;

[0032] 23) Generate equipment operation strategy in the current scheduling scenario;

[0033] 24) Obtain the profit indicators of multiple scheduling scenarios and implement the optimal scheduling strategy based on the mixed integer linear programming algorithm;

[0034] In step 4, when it is determined that the optimal benefit has not been achieved, the device parameters are optimized based on the particle swarm algorithm.

[0035] As a further description of the above technical solution:

[0036] According to the characteristics of wind photovoltaic power generation system, the particle swarm optimization algorithm involved is as follows:

[0037] 31) Initialize particle swarm parameters (initial device capacity);

[0038] 32) Calculate particle fitness (equipment cost and operating benefits);

[0039] 33) Obtain the global optimal position of individual particles and the group;

[0040] 34) Update particle position and velocity;

[0041] 35) Determine whether the maximum number of iterations has been reached. If so, terminate the optimization process. If not, continue iterative optimization.

[0042] After the optimized parameters are obtained, the optimized parameters are applied to the wind photovoltaic power generation system to achieve efficiency optimization of the wind photovoltaic power generation system.

[0043] On the other hand, in order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a wind-photovoltaic mutual power generation system, which includes a photovoltaic power generation system, a wind power generation system and a control management system, and the control management system is implemented based on the optimization method described in any one of claims 1-7.

[0044] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0045] In the present invention, by optimizing the power grid dispatching and control system based on the optimization method of the present invention, the instability caused by the fluctuation of wind photovoltaic power generation in the prior art can be solved, the robustness of the system can be improved, the efficiency of the wind photovoltaic power generation system can be optimized, and support can be provided for the construction of a high-efficiency renewable energy power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Schematic diagram of the wind-photovoltaic hybrid power generation system.

[0047] Figure 2 Flow chart of the scheduling optimization method for wind-photovoltaic hybrid power generation system. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Example:

[0050] S01: Obtain equipment parameters and wind and photovoltaic operation data in the wind and solar power generation system;

[0051] S02: Based on the above parameters and the above data, combined with the optimization direction based on fitness function optimization, a wind-solar system equipment model is constructed, including a wind turbine output model and a photovoltaic output model;

[0052] S03: After establishing the wind-PV output model, simulate the dispatching scenario in the wind-PV power generation system;

[0053] S04: constructing a system optimization objective function of the power grid model, wherein the system optimization function is a maximum fitness optimization function, and based on the optimization function, a particle swarm optimization algorithm is used to optimize the system to obtain optimization parameters;

[0054] S05: Apply the optimized parameters to the wind photovoltaic power generation system to achieve efficiency optimization of the wind photovoltaic power generation system.

[0055] The wind and solar power output model established taking uncertainty into account includes:

[0056] Since the output power of wind power generation is greatly affected by wind speed fluctuations, firstly, in order to simulate the uncertainty of wind power output, a probability density function is established, and its probability density function is described as follows:

[0057]

[0058] Where k is the wind state parameter, which is used to measure the correlation between wind speed change and gust in the needle area; c is the spatial scale parameter, which is the overall situation of wind speed in the user's needle area; v is the average wind speed;

[0059] In this embodiment, input variables based on the change of wind speed and the overall situation of wind speed are constructed, accurate simulation of various situations of wind power generation is achieved, and a probability density function with wind speed as a variable is constructed; compared with the prior art, this density function adds the change of wind speed as an input, which can reflect the probability of occurrence at each power level, and then reveal the characteristics and laws of power fluctuations, and obtain probability distribution, mean, variance and other data of different power levels, which is conducive to better error analysis and optimization calculation.

[0060] Construct the output model Pw(v) of the wind turbine. The model of wind turbine power generation is related to wind speed and is a staged model, as follows:

[0061]

[0062] Where P w (v) is the wind power output that varies with time; P wf , P wr The wind turbine power at wind speeds below rated and rated are P respectively. wfDetermined by the following formula:

[0063]

[0064] Where ρ is the air density at the hub height, in kg / m3; Aw is the turbine swept area; C p is the power coefficient of the wind turbine;

[0065] In one embodiment, it is also necessary to convert the ground measured wind speed to obtain the blade wind speed of the turbine generator, where the ground wind speed v g The conversion relationship between the wind speed v of the turbine generator blades is as follows:

[0066]

[0067] In the formula, v g is the surface wind speed; h and h g are the height of wind turbine blade and the ground respectively; α is the Hellman friction coefficient;

[0068] Construct the output model of the photovoltaic array. The output model of the photovoltaic array is represented by the output power of the solar panel array, as shown below:

[0069]

[0070] Where Pm is the rated output power of a single photovoltaic module; T is the actual working temperature of the photovoltaic module; T STC —The operating temperature of the PV modules under standard test conditions; —PV module temperature coefficient; i m —Irradiance under standard test conditions; i—Irradiance.

[0071] The above photovoltaic array output model takes into account environmental factors such as temperature and solar radiation, providing a strong basis for power generation optimization.

[0072] In another embodiment, in order to fully cope with the uncertainty of wind and solar power output, the relevant scenarios are simulated based on the Copula algorithm to achieve accurate construction of the model; the following steps are included:

[0073] The nonparametric kernel density estimation method is used to model the correlation of the Copula function and determine the best Copula fitting function in each weather classification feature class:

[0074] S11: Use nonparametric kernel density estimation method to model the correlation of Copula function and determine the best Copula fitting function in each weather classification feature class;

[0075] S12: The probability density functions of wind and solar output in each weather classification feature class are calculated using the kernel density estimation principle as follows:

[0076]

[0077] Where: x is the hourly wind turbine output; y represents the hourly photovoltaic output; X t represents the hourly wind turbine output on the tth day of the Nth weather classification; Y t represents the hourly PV output on the tth day of the Nth weather classification; h represents the bandwidth; K(·) represents the Gaussian kernel function.

[0078] In this embodiment, a probability density function is constructed. Compared with the existing technical effects, the probability density function of wind and solar power output in each weather classification feature class is calculated using the kernel density estimation principle, which fully considers the correlation and uncertainty of wind and solar power output. By calculating the error data of different weather types, more accurate probability data can be obtained. Corresponding calculation methods are designed for different weather types to achieve accurate prediction of fluctuation patterns.

[0079] The Copula function in the Archimedean family is selected for modeling. In an optional embodiment, one or more of Gumbel-Copula, Clayton-Copula, Gaussian-Copula and t-Copula in the elliptic family can be selected for modeling.

[0080]

[0081] Where: The marginal distribution function of a single variable; C(·) is the copula connection function; F(x1, x2) is the joint distribution function of two variables.

[0082] In an optional embodiment, in order to better achieve modeling of related scenarios, considering the correlation of wind and photovoltaic power generation, the Copula connection function designed by the present invention is as follows:

[0083]

[0084] In the above formula, λ is the correlation coefficient, ξ w and s are the future outputs of photovoltaic and wind energy systems, respectively.

[0085] The connection function designed in this embodiment can better describe the correlation between wind power and photovoltaic power generation output compared with the prior art. The connection function inputs the future related parameter variables of photovoltaic and wind energy systems, which can better provide guarantee for scheduling optimization configuration.

[0086] In an optional embodiment, the optimal Copula function type may be comprehensively determined by selecting one or more of the Euclidean distance, the Kendall rank correlation coefficient, and the Spearman rank correlation coefficient.

[0087] In order to explore the impact of random fluctuations in wind and solar power when the wind and photovoltaic power generation system is continuously running for a long time, it is also necessary to randomly combine the M wind and solar power output data to form K groups of 7×24h wind and solar power output scheduling scenarios. The specific steps are as follows:

[0088] 1. Initialize the original array consisting of M wind and solar power data, and set the array length to m;

[0089] 2. Prepare the array by shuffling it and randomly generate a number n between [0, k] from the k unprocessed data;

[0090] 3. Exchange the k-th wind and solar power data with the n-th wind and solar power data in the original array position;

[0091] 4. Repeat steps 2 and 3 until all the data in the array are scrambled, and a new array generated by random combination of the shuffling algorithm is obtained;

[0092] 5. Divide the output data of seven adjacent wind and solar days into a group in turn to generate K groups of 7×24h wind and solar correlation output scheduling scenarios.

[0093] In another embodiment, in order to evaluate the benefits of the system, an optimization process based on a fitness function is designed according to the characteristics of the system itself. The fitness function is a maximum benefit fitness function, and the optimization steps are as follows:

[0094] S21: Generate uncertain scheduling scenarios for wind and solar power output based on copula function;

[0095] S22: Obtain equipment model, equipment constraint and revenue constraint indicators;

[0096] S23: Generate a device operation strategy in the current scheduling scenario;

[0097] S24: Obtain the profit indicators of multiple scheduling scenarios and implement the optimal scheduling strategy based on the mixed integer linear programming algorithm;

[0098] In step 4, when it is judged that the optimal benefit has not been achieved, the equipment parameters are optimized based on the particle swarm algorithm;

[0099] The particle swarm optimization algorithm is as follows:

[0100] S31: Initialize particle swarm parameters (initial device capacity);

[0101] S32: Calculate particle fitness (equipment cost and operating benefits);

[0102] S33: Obtain the global optimal position of individual particles and the group;

[0103] S34: Update particle position and velocity;

[0104] S35: Determine whether the maximum number of iterations has been reached. If so, terminate the optimization process. If not, continue iterative optimization.

[0105] After the optimized parameters are obtained, the optimized parameters are applied to the wind photovoltaic power generation system to achieve efficiency optimization of the wind photovoltaic power generation system.

[0106] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing the dispatching of a wind-photovoltaic mutual power generation system, characterized in that: The steps include: 1) Obtain equipment parameters and wind and photovoltaic operation data in wind and solar power generation systems; 2) Based on the above parameters and data, combined with the optimization direction based on fitness function optimization, a wind-solar system equipment model is constructed, including a wind turbine output model and a photovoltaic output model; 3) After establishing the wind-PV output model, simulate the dispatching scenarios in the wind-PV power generation system; 4) constructing a system optimization objective function of the power grid model, wherein the system optimization function is a maximum fitness optimization function, and based on the optimization function, a particle swarm optimization algorithm is used to optimize the system to obtain optimization parameters; 5) Apply the optimized parameters to the wind photovoltaic power generation system to achieve the efficiency optimization of the wind photovoltaic power generation system.

2. The method for optimizing the dispatching of wind-photovoltaic mutual power generation system according to claim 1, characterized in that: Construct the output model of the photovoltaic array. The output model of the photovoltaic array is represented by the output power of the solar panel array, as shown below: Where Pm represents the rated output power of a single photovoltaic module; T represents the actual operating temperature of the photovoltaic module; T STC Indicates the operating temperature of the photovoltaic module under standard test conditions; Represents the temperature coefficient of the photovoltaic module; i m It represents the irradiance under standard test conditions; i represents the irradiance.

3. The method for optimizing the dispatching of wind-photovoltaic mutual power generation system according to claim 1, characterized in that: In order to fully cope with the uncertainty of wind and solar power output, the relevant scenarios are simulated based on the Copula algorithm to achieve accurate model construction; the steps include: 11) Use non-parametric kernel density estimation method to model the correlation of Copula function and determine the best Copula fitting function in each weather classification feature class; 12) Using the kernel density estimation principle, the probability density functions of wind and solar output in each weather classification feature class are calculated as follows: Where: x is the hourly wind turbine output; y represents the hourly photovoltaic output; X t represents the hourly wind turbine output on the tth day of the Nth weather classification; Y t represents the hourly PV output on the tth day of the Nth weather classification; h represents the bandwidth; K(·) represents the Gaussian kernel function.

4. The method for optimizing the dispatching of wind-photovoltaic mutual power generation system according to claim 3 is characterized in that: After constructing the probability density function, it also includes selecting the Copula function in the Archimedean family for modeling. Where: The marginal distribution function of a single variable; C(·) is the copula connection function; F(x1, x2) is the joint distribution function of two variables.

5. The method for optimizing the dispatching of wind-photovoltaic mutual power generation system according to claim 4, characterized in that: The couple connection function is as follows: In the above formula, λ is the correlation coefficient, ξ w and s are the future outputs of photovoltaic and wind energy systems, respectively.

6. The method for optimizing the dispatching of wind-photovoltaic mutual power generation system according to claim 1, characterized in that: The fitness function is a maximum benefit fitness function, and the optimization steps are as follows: 21) Generate uncertain scheduling scenarios for wind and solar power output based on copula function; 22) Obtain equipment model, equipment constraints and revenue constraint indicators; 23) Generate equipment operation strategy in the current scheduling scenario; 24) Obtain the profit indicators of multiple scheduling scenarios and implement the optimal scheduling strategy based on the mixed integer linear programming algorithm; In step 4, when it is determined that the optimal benefit has not been achieved, the device parameters are optimized based on the particle swarm algorithm.

7. The method for optimizing the dispatching of wind-photovoltaic mutual power generation system according to claim 6, characterized in that: According to the characteristics of wind photovoltaic power generation system, the particle swarm optimization algorithm involved is as follows: 31) Initialize particle swarm parameters (initial device capacity); 32) Calculate particle fitness (equipment cost and operating benefits); 33) Obtain the global optimal position of individual particles and the group; 34) Update particle position and velocity; 35) Determine whether the maximum number of iterations has been reached. If so, terminate the optimization process. If not, continue iterative optimization. After the optimized parameters are obtained, the optimized parameters are applied to the wind photovoltaic power generation system to achieve efficiency optimization of the wind photovoltaic power generation system.

8. A wind-photovoltaic hybrid power generation system as claimed in any one of claims 1 to 7, characterized in that: It comprises a photovoltaic power generation system, a wind power generation system and a control management system, wherein the control management system is implemented based on the optimization method described in any one of claims 1-7.

Images