Power system frequency control method of wind and light storage power system

Abstract

The invention discloses a power system frequency control method for a wind and light storage power system, relates to the technical field of power system frequency control, and solves the technical problems that only how to regulate and guarantee the stability of power grid frequency is considered and the influence of regulation measures on the service life and operation cost of the power system is ignored in the prior art. The method comprises the following steps: calculating an electric energy difference between electric energy generated by an electric power system in a current acquisition period and predicted required electric energy by predicting an abnormal condition of an operation frequency; when the electric energy difference is smaller than 0, the difference value between the maximum output electric energy of the electric power system in the collection period and the electric energy generated by the electric power system in the current collection period is calculated, and the difference value is the maximum electric energy difference; formulating an optimal frequency regulation mode according to the maximum electric energy difference, the electric energy difference and the wear cost of each unit of increased electric energy of the electric power system; accurate control and stable operation of the frequency of the power system are realized, and the overall performance and sustainability of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the field of power system frequency control, and specifically is a power system frequency control method for a wind, solar and power storage system. Background Art

[0002] With the widespread application of renewable energy, wind power and solar energy, as two main green energy sources, have gradually occupied an important position in the power system. However, their intermittent, volatile and non-dispatching characteristics have posed new challenges to the stability of the power grid.

[0003] The invention patent application with publication number CN116526512A discloses a frequency control method and energy storage system for an electric power system; the electric power system is connected to the energy storage system; the method comprises: when the difference between the actual frequency of the electric power system and the rated operating frequency exceeds a preset threshold, obtaining the actual frequency change rate of the electric power system; according to the actual frequency change rate and the target frequency change rate, obtaining the first exchange power of the energy storage system; and controlling the energy storage system to exchange power with the electric power system according to the first exchange power.

[0004] The above-mentioned prior art accurately calculates and controls the required power adjustment amount by real-time analysis of the frequency change rate, effectively ensuring the stability of the power grid frequency and avoiding the risk of over-control, but ignores the impact of the control measures themselves on the life and operating costs of the power system; therefore, the present invention proposes a power system frequency control method for a wind, solar and energy storage power system. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a power system frequency control method for a wind, solar and storage power system, which is used to solve the technical problem that the prior art only considers how to regulate and ensure the stability of the power grid frequency, but ignores the impact of the regulation measures themselves on the life and operating costs of the power system.

[0006] To achieve the above object, a first aspect of the present invention provides a method for controlling the frequency of a wind-solar-storage power system, comprising the following steps:

[0007] Step 1: Establish a digital twin model of the power system, collect the operating frequency of the power system every other collection cycle, calculate the difference between the operating frequency and the target frequency, and obtain the frequency fluctuation amplitude; the power system includes the wind turbine power system, the photovoltaic power system, and the energy storage power system;

[0008] Step 2: Determine whether the frequency fluctuation amplitude is greater than the amplitude threshold; if yes, the frequency fluctuation is abnormal, and calculate the power difference between the power generated by the power system in the current collection cycle and the expected power demand; if no, the frequency fluctuation is normal. If the frequency fluctuation is normal for M consecutive collection cycles, calculate the power difference between the power generated by the power system in M ​​collection cycles and the expected power demand in M ​​collection cycles; where M>1 and is a positive integer;

[0009] Step 3: Determine whether the electric energy difference is less than 0; if so, calculate the difference between the maximum output electric energy of the power system during the collection cycle and the electric energy generated by the power system during the current collection cycle, and mark it as the maximum electric energy difference; formulate the optimal frequency adjustment method based on the maximum electric energy difference, the electric energy difference, and the wear cost of each additional unit of electric energy in the power system; if not, the electric energy difference generated by the energy storage power system is stored.

[0010] Preferably, the establishing of a digital twin model of the power system includes:

[0011] Extract the structural model of the power system from historical data and build a three-dimensional digital twin model of the power system;

[0012] The equipment operation data, meteorological data and power data of the power system are extracted from historical data, and the equipment operation data and meteorological data are used as the standard input data of the three-dimensional digital twin model, and the power data is used as the standard output data of the three-dimensional digital twin model to train the digital twin model of the power system. Among them, the meteorological data includes wind speed and light intensity; the power data includes the operating frequency of the power system and the generated electric energy.

[0013] The present invention lays a solid foundation for realizing the automated and intelligent operation and management of the power grid by deeply integrating historical equipment operation data, meteorological conditions such as wind speed and light intensity, and power data. The model can accurately simulate the real-time operation status and future trends of the power system, which helps to provide early warning of frequency conditions, conduct frequency adjustment in time, ensure the stable operation of the power grid, and ensure the continuity and safety of power supply.

[0014] Preferably, analyzing the estimated required electric energy in the current collection cycle includes:

[0015] Obtain the user's historical electricity consumption data from the power management center, classify it according to the season, and calculate the electricity consumption per person per unit time in each season, that is, the user's unit time electricity consumption, marked as DNi; where i represents the season type, which includes spring, summer, autumn and winter;

[0016] According to the season to which the current collection cycle belongs, the estimated required electric energy XQL of the current collection cycle is calculated by the formula XQL=DNi×RS×TC; wherein RS is the number of users in the power system supply area; and TC is the duration of the collection cycle.

[0017] In order to accurately predict and manage the needs of the power system, the present invention first needs to obtain detailed historical user electricity consumption data from the power management center, calculate the electricity consumption per unit time of each user for each season, and predict the electricity demand of the current collection cycle. This not only helps the power system to prepare power dispatch and reserve in advance, but also effectively responds to seasonal power consumption peaks and ensures the stability and reliability of power supply.

[0018] Preferably, the maximum output electric energy of the power system during the acquisition period includes:

[0019] The wind force within the collection period is processed by averaging to obtain the average wind force within the collection period; based on the average wind force, the optimal peripheral speed ratio is extracted and the rotation speed of the wind turbine power system is analyzed; the average wind force and rotation speed are input into the digital twin model of the power system, and the maximum output power of the wind turbine power system is obtained as output;

[0020] According to the meteorological data in the collection period, the MPPT algorithm is used to dynamically adjust the working point of the photovoltaic power system to output the maximum output power under different meteorological data, and the average value of several maximum output power obtained is processed to obtain the maximum output power of the photovoltaic power system in the collection period;

[0021] The maximum output power of the energy storage power system is the current stored power.

[0022] In the energy management system, the present invention is crucial to accurately predict and optimize the maximum output power of wind, photovoltaic and energy storage power systems, and can grasp the power generation potential of each system in real time, so as to respond quickly when the grid frequency fluctuates, ensuring that the power system can still maintain stable operation of the grid under limited output power.

[0023] Preferably, the optimal frequency regulation method is formulated according to the maximum power difference, the power difference, and the wear cost of each additional unit of power in the power system, including:

[0024] Select a frequency adjustment method where |F-FS|≤Fd from several frequency adjustment methods;

[0025] Wherein, Fd is the frequency deviation threshold, F is the frequency value after frequency adjustment, and FS is the target frequency;

[0026] Selecting an optimal frequency adjustment method from adjustment methods that meet the frequency deviation threshold;

[0027] Establish the objective function of minimizing the frequency regulation cost:

[0028] Min(Q)=FDN×FB+GDN×GB+CDN×CB;

[0029] Among them, Min(Q) is the objective function with the lowest frequency regulation cost, FDN is the amount of electric energy regulated by the wind turbine power system, GDN is the amount of electric energy regulated by the photovoltaic power system, and CDN is the amount of electric energy charged and discharged by the energy storage system.

[0030] The constraint condition is FDN+GDN+CDN≤DZC, that is, the required increase in power energy of the power system cannot exceed 0≤FDN, GDN, CDN≤MDj;

[0031] Among them, DZC is the energy difference, MDj is the maximum energy difference, and j is the power system type.

[0032] The present invention comprehensively considers the costs incurred by the wind turbine power system, photovoltaic power system, and energy storage system in the regulation process, constructs an objective function with the lowest frequency regulation cost to achieve low-cost regulation of the power system frequency, and formulates the optimal frequency regulation method to balance supply and demand and minimize costs in the face of fluctuations in the power grid frequency; while ensuring the stable operation of the power system, low-cost regulation of the power system frequency is achieved.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. The present invention predicts the operating frequency of the power system through the digital twin model of the power system. When the operating frequency is abnormal, the power difference between the supply and demand power of the current power system and the maximum output power of the power system are analyzed to obtain the maximum power difference of the current power system; the objective function with the lowest frequency regulation cost is constructed through the maximum power difference, the power difference, and the wear cost of each additional unit of power in the power system, and the optimal frequency regulation method is selected; the cost minimization is achieved as the regulation goal, which not only reduces the risk of power grid failure and power outage caused by frequency fluctuations, but also improves energy utilization efficiency and reduces overall operating costs;

[0035] 2. The present invention establishes a digital twin model of the power system. When the frequency fluctuation is abnormal, the electric energy difference between the electric energy generated by the power system in the current collection cycle and the expected demand electric energy is calculated; when the electric energy difference is less than 0, the difference between the maximum output electric energy of the power system in the collection cycle and the electric energy generated by the power system in the current collection cycle is calculated as the maximum electric energy difference; according to the maximum electric energy difference, the electric energy difference, and the wear cost of each additional unit of electric energy in the power system, the optimal frequency adjustment method is formulated; the accurate prediction of the behavior of the power system is achieved, the operating frequency of the power system is adjusted in time, and the continuous and reliable operation of the power grid is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 It is a schematic diagram of the process of the present invention;

[0038] Figure 2 It is a schematic diagram of the flow chart of the method for analyzing estimated power demand of the present invention. DETAILED DESCRIPTION

[0039] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. 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.

[0040] See also Figure 1 The first aspect of the present invention provides a method for controlling the frequency of a wind-solar-storage power system, comprising the following steps:

[0041] Step 1: Establish a digital twin model of the power system, collect the operating frequency of the power system every other collection cycle, calculate the difference between the operating frequency and the target frequency, and obtain the frequency fluctuation amplitude;

[0042] Among them, the power system includes wind turbine power system, photovoltaic power system and energy storage power system; the collection cycle can be set to 30s;

[0043] Specifically, the physical model of the power system is extracted from historical data to construct a three-dimensional digital twin model of the power system;

[0044] Extract equipment operation data, meteorological data and power data of the power system from historical data, and use equipment operation data and meteorological data as standard input data of the three-dimensional digital twin model, and power data as standard output data of the three-dimensional digital twin model, and train to obtain the digital twin model of the power system;

[0045] Among them, equipment operation data includes current, voltage, vibration frequency, etc.; meteorological data includes temperature, humidity, wind speed, light intensity, etc.; power data includes the operating frequency of the power system and the generated electrical energy.

[0046] Step 2: Determine whether the frequency fluctuation amplitude is greater than the amplitude threshold; if yes, the frequency fluctuation is abnormal, and calculate the power difference between the power generated by the power system in the current collection cycle and the expected power demand; if no, the frequency fluctuation is normal. If the frequency fluctuation is normal for M consecutive collection cycles, calculate the power difference between the power generated by the power system in M ​​collection cycles and the expected power demand in M ​​collection cycles; where M>1 and is a positive integer;

[0047] See also Figure 2 , where the estimated power demand analysis for the collection period is as follows:

[0048] Obtain the user's historical electricity consumption data from the power management center, classify it according to the season, and calculate the electricity consumption per person per unit time in each season, that is, the user's unit time electricity consumption, marked as DNi; where i represents the season type, which includes spring, summer, autumn and winter;

[0049] It should be noted that users have different electricity consumption habits and demand characteristics in different seasons. For example, in summer, due to the increased use of air conditioners, electricity consumption may be significantly higher than in other seasons.

[0050] The power consumption per unit time may be the power consumption per minute, per hour or per week, etc., and may be set according to actual calculation needs.

[0051] According to the season to which the current collection cycle belongs, the estimated required electric energy XQL of the current collection cycle is calculated by the formula XQL=DNi×RS×TC; wherein RS is the number of users in the power system supply area; and TC is the duration of the collection cycle.

[0052] The present invention analyzes the estimated electric energy required for the current collection period through historical electricity consumption data, that is, the estimated demanded electric energy; the estimated demanded electric energy is mainly closely related to the season, the user's electric energy consumption per unit time, and the number of users in the power system supply area. If the current collection period is in a season where the user's electric energy consumption per unit time is large and there are many users, the estimated electric energy required for the current collection period is large, and the estimated demanded electric energy value is large.

[0053] It should be noted that, because the duration of the acquisition cycle of the present invention is the same, it is a fixed value factor and the impact on the estimated required electric energy is negligible.

[0054] Step 3: Determine whether the power difference is less than 0; if yes, calculate the difference between the maximum output power of the power system in the acquisition cycle and the power generated by the power system in the current acquisition cycle, and mark it as the maximum power difference;

[0055] Specifically, the wind force within the collection period is averaged to obtain the average wind force within the collection period; based on the average wind force, the optimal peripheral speed ratio is extracted, and the rotation speed of the wind turbine power system is analyzed; the average wind force and rotation speed are input into the digital twin model of the power system, and the maximum output power of the wind turbine power system is obtained;

[0056] Among them, the optimal peripheral speed ratio uses CFD software to numerically simulate the flow field of the fan at different wind speeds and rotation speeds. The simulation results can predict the wind energy conversion efficiency, and the wind speed and rotation speed corresponding to the highest conversion efficiency are ratio processed to obtain the optimal peripheral speed ratio.

[0057] According to the meteorological data in the collection period, the MPPT algorithm is used to dynamically adjust the working point of the photovoltaic power system to output the maximum output power under different meteorological data, and the average value of several maximum output power obtained is processed to obtain the maximum output power of the photovoltaic power system in the collection period;

[0058] It should be noted that the working point of the photovoltaic array is dynamically adjusted according to the collected meteorological data to track the maximum power point. Since the meteorological data is changing, the maximum power point will also change with the change of meteorological data during the collection period. In the process, several instantaneous or maximum output electrical energies within a certain period of time will be generated.

[0059] The maximum output power of the energy storage power system is the current stored power.

[0060] Furthermore, the optimal frequency regulation method is formulated based on the maximum power difference, power difference, and the wear cost of each additional unit of power in the power system. The specific analysis is as follows:

[0061] Select a frequency adjustment method where |F-FS|≤Fd from several frequency adjustment methods;

[0062] Among them, Fd is the frequency deviation threshold, F is the operating frequency after frequency adjustment, and FS is the target frequency, that is, the frequency value of stable operation of the power grid, which is generally 50Hz or 60Hz, which varies from region to region;

[0063] Selecting an optimal frequency adjustment method from adjustment methods that meet the frequency deviation threshold;

[0064] Establish the objective function of minimizing the frequency regulation cost:

[0065] Min(Q)=FDN×FB+GDN×GB+CDN×CB;

[0066] Among them, Min(Q) is the objective function with the lowest frequency regulation cost, FDN is the amount of electric energy regulated by the wind turbine power system, GDN is the amount of electric energy regulated by the photovoltaic power system, and CDN is the amount of electric energy charged and discharged by the energy storage system.

[0067] The constraint condition is FDN+GDN+CDN≤DZC, that is, the required increase in power energy of the power system cannot exceed 0≤FDN, GDN, CDN≤MDj;

[0068] Among them, DZC is the energy difference, MDj is the maximum energy difference, and j is the power system type.

[0069] It should be noted that the amount of electric energy that the power system needs to regulate only needs to be less than or equal to the electric energy difference. Therefore, the sum of the regulated electric energies of the wind turbine / photovoltaic / energy storage must not exceed the electric energy difference, otherwise excessive regulation will lead to frequency instability; and the amount of electric energy that can be adjusted by the wind turbine / photovoltaic / energy storage power system is less than the maximum adjustable electric energy difference itself.

[0070] The present invention selects the optimal frequency regulation method by evaluating the cost of frequency regulation; the frequency regulation cost is mainly related to the amount of electric energy regulated by the wind turbine / photovoltaic / energy storage system, and the wear cost per unit of electric energy corresponding to each power system. Assuming that the wear cost per unit of electric energy corresponding to the wind turbine power system is the least compared with the other two power systems, the wind turbine power system is mainly used to generate electric energy; assuming that two / three of the power systems are relatively small, the electric energy is evenly distributed to two / three of them to achieve the lowest regulation cost.

[0071] It should be noted that the wear cost in the present invention is the mechanical wear in the power system and mainly refers to the additional wear cost caused by the increase of electric energy output during the operation of generators, transformers and other mechanical equipment.

[0072] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0073] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for controlling the frequency of a wind-solar-storage power system, characterized in that: The following steps are involved: Step 1: Establish a digital twin model of the power system, collect the operating frequency of the power system every other collection cycle, calculate the difference between the operating frequency and the target frequency, and obtain the frequency fluctuation amplitude; the power system includes the wind turbine power system, the photovoltaic power system, and the energy storage power system; Step 2: Determine whether the frequency fluctuation amplitude is greater than the amplitude threshold; if yes, the frequency fluctuation is abnormal, and calculate the power difference between the power generated by the power system in the current collection cycle and the expected power demand; if no, the frequency fluctuation is normal. If the frequency fluctuation is normal for M consecutive collection cycles, calculate the power difference between the power generated by the power system in M ​​collection cycles and the expected power demand in M ​​collection cycles; where M>1 and is a positive integer; Step 3: Determine whether the electric energy difference is less than 0; if so, calculate the difference between the maximum output electric energy of the power system during the collection cycle and the electric energy generated by the power system during the current collection cycle, and mark it as the maximum electric energy difference; formulate the optimal frequency adjustment method based on the maximum electric energy difference, the electric energy difference, and the wear cost of each additional unit of electric energy in the power system; if not, the electric energy difference generated by the energy storage power system is stored.

2. The method for controlling the frequency of a wind-solar-storage power system according to claim 1, characterized in that: The establishment of a digital twin model of the power system includes: Extract the structural model of the power system from historical data and build a three-dimensional digital twin model of the power system; The equipment operation data, meteorological data and power data of the power system are extracted from historical data, and the equipment operation data and meteorological data are used as the standard input data of the three-dimensional digital twin model, and the power data is used as the standard output data of the three-dimensional digital twin model to train the digital twin model of the power system. Among them, the meteorological data includes wind speed and light intensity; the power data includes the operating frequency of the power system and the generated electric energy.

3. The method for controlling the frequency of a wind-solar-storage power system according to claim 1, characterized in that: Analyze the expected power demand during the current collection period, including: Obtain the user's historical electricity consumption data from the power management center, classify it according to the season, calculate the user's unit time electricity consumption in each season, and mark it as DNi; where i represents the season type, which includes spring, summer, autumn and winter; According to the season to which the current collection cycle belongs, the estimated required electric energy XQL of the current collection cycle is calculated by the formula XQL=DNi×RS×TC; wherein RS is the number of users in the power system supply area; and TC is the duration of the collection cycle.

4. The method for controlling the frequency of a wind-solar-storage power system according to claim 1, characterized in that: The maximum output electric energy of the power system during the acquisition period includes: The wind force within the collection period is processed by averaging to obtain the average wind force within the collection period; based on the average wind force, the optimal peripheral speed ratio is extracted and the rotation speed of the wind turbine power system is analyzed; the average wind force and rotation speed are input into the digital twin model of the power system, and the maximum output power of the wind turbine power system is obtained as output; According to the meteorological data in the collection period, the MPPT algorithm is used to dynamically adjust the working point of the photovoltaic power system to output the maximum output power under different meteorological data, and the average value of several maximum output power obtained is processed to obtain the maximum output power of the photovoltaic power system in the collection period; The maximum output power of the energy storage power system is the current stored power.

5. The method for controlling the frequency of a wind-solar-storage power system according to claim 1, characterized in that: The optimal frequency regulation method is formulated according to the maximum power difference, the power difference, and the wear cost of each additional unit of power in the power system, including: Select a frequency adjustment method where |F-FS|≤Fd from several frequency adjustment methods; Wherein, Fd is the frequency deviation threshold, F is the frequency value after frequency adjustment, and FS is the target frequency; Selecting an optimal frequency adjustment method from adjustment methods that meet the frequency deviation threshold; Establish the objective function of minimizing the frequency regulation cost: Min(Q)=FDN×FB+GDN×GB+CDN×CB; Among them, Min(Q) is the objective function with the lowest frequency regulation cost, FDN is the amount of electric energy regulated by the wind turbine power system, GDN is the amount of electric energy regulated by the photovoltaic power system, and CDN is the amount of electric energy charged and discharged by the energy storage system. The constraint condition is FDN+GDN+CDN≤DZC, that is, the required increase in power energy of the power system cannot exceed 0≤FDN, GDN, CDN≤MDj; Among them, DZC is the energy difference, MDj is the maximum energy difference, and j is the power system type.

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