WO2022217788A1 - 一种新能源电站一次调频网络化控制方法 - Google Patents

一种新能源电站一次调频网络化控制方法 Download PDF

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WO2022217788A1
WO2022217788A1 PCT/CN2021/109974 CN2021109974W WO2022217788A1 WO 2022217788 A1 WO2022217788 A1 WO 2022217788A1 CN 2021109974 W CN2021109974 W CN 2021109974W WO 2022217788 A1 WO2022217788 A1 WO 2022217788A1
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power
generation unit
time
power generation
inverter
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PCT/CN2021/109974
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English (en)
French (fr)
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岳东
窦春霞
张智俊
丁孝华
罗剑波
李延满
黄堃
韩韬
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南京邮电大学
国网电力科学研究院有限公司
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Priority to US17/921,111 priority Critical patent/US11862982B2/en
Publication of WO2022217788A1 publication Critical patent/WO2022217788A1/zh

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H02J3/241—The oscillation concerning frequency
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2639—Energy management, use maximum of cheap power, keep peak load low
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

Definitions

  • the invention belongs to the field of smart grid control, and in particular relates to a primary frequency modulation network control method for a new energy power station.
  • the purpose of the present invention is to provide a networked control method for primary frequency modulation of a new energy power station, so as to solve the problems of poor frequency modulation accuracy, slow frequency modulation speed, and aging of communication equipment in some new energy power stations in the existing primary frequency modulation method for new energy power stations. , resulting in the problem of low success rate of primary frequency regulation of new energy power plants.
  • the present invention provides a networked control method for primary frequency regulation of a new energy power station, the method comprising:
  • the optimal control sequence of the inverter of each power generation unit in each time period determines the optimal control sequence includes a plurality of inverters The control amount of the active power of the device;
  • the optimal control sequence of each time period is marked with a time scale, and sent to the execution device of each power generation unit according to the time period, and the execution device of each power generation unit receives the optimal control sequence, and determines and stores it according to the time scale. or not to store the optimal control sequence;
  • the execution device of each power generation unit determines the control amount to be executed according to the received and stored optimal control sequence and the time scale thereof, so as to realize the prediction compensation for the delay of the communication network.
  • the historical operation data includes one or more of historical active power data for at least 24 hours, light intensity data for at least 24 hours, and wind speed data for at least 24 hours.
  • inverter active power model is established through the following steps:
  • ⁇ P ref is the difference between the actual reference power value of the inverter and the current power value
  • T d is the delay from when the inverter receives the control command to the start of execution
  • ⁇ i od is the difference between the current component of the d-axis of the inverter and the current component at the previous moment
  • u od is the component of the output port voltage of the inverter on the d-axis
  • ⁇ P int is the integral of the difference between ⁇ P ref and ⁇ P dg
  • s is the Laplace operator
  • x(k) [ ⁇ P dg (k) ⁇ P int (k) ⁇ i od (k) ⁇ P ref (k)] T
  • ⁇ P dg (k) is the output power of the inverter at the kth moment and the output at the previous moment
  • the difference of power, ⁇ i od (k) is the difference between the current component of the inverter d-axis at the k-th moment and the current component at the previous moment
  • ⁇ P ref (k) is the actual reference power value of the inverter and the k-th moment
  • the difference between the power values, ⁇ P int (k) is the integral of the difference between ⁇ P ref (k) and ⁇ P dg (k), is the difference between the reference power value of the inverter and the power value at the kth moment
  • T p is the sampling time.
  • the optimal control sequence of the inverter of each power generation unit in each time period including:
  • the objective function of predictive control is used to determine the optimal control sequence of the inverter of each power generation unit in each time period, the The objective function of predictive control is expressed as:
  • N p is the length of the prediction domain
  • N is the number of power generation units in the new energy power station
  • ⁇ i (k) is the weight coefficient of the ith power generation unit
  • ⁇ i (k) -b i ⁇ P i (k)
  • ⁇ P i (k) is the power change of the ith power generation unit at the kth time relative to the k-1th time
  • C i (k) is the confidence of the i-th power generation unit at the k-th time, and the initial value is 1, and are the upper and lower limits of the estimated value of the primary frequency regulation
  • ⁇ P is the total power required by the new energy power station to participate in the primary frequency regulation
  • ⁇ P total is the total estimated value of the new energy power station participating in the primary frequency regulation
  • P i (k) is the kth time.
  • the generated power of i generating units, ⁇ f is the system frequency deviation
  • K is the primary frequency modulation coefficient of the new energy power station
  • the optimal control sequence is:
  • u * (k) is the optimal control sequence at the kth moment
  • N c is the length of the control domain
  • It is the inverter control amount at the k+ith time point predicted at the kth time point.
  • each power generation unit receives the optimal control sequence, and determines to store or not to store the optimal control sequence according to the time scale, including:
  • the received optimal control sequence is not stored
  • the execution device of each power generation unit determines the control amount to be executed, including:
  • the first control variable of the optimal control sequence stored therein is executed.
  • the execution device of each power generation unit determines the control amount to be executed, including:
  • the optimal control sequence package stored by the execution device of the ith power generation unit is:
  • k l is the time scale of the optimal control sequence package stored by the execution device of the ith power generation unit at the current k time, is the i-th inverter control variable at the k l +i time predicted at the k l time;
  • the execution device of each power generation unit determines the control amount to be executed, and further includes:
  • the optimal control sequence packet received by the execution device of the ith power generation unit for:
  • k r is the time scale of the optimal control sequence packet received by the execution device of the ith power generation unit at the current k time, It is the ith inverter control variable at the k r + i time predicted at the k r time
  • the optimal control sequence of the inverter of each power generation unit in each time period further comprising:
  • the optimal control sequence of each time period of the inverter of each power generation unit is determined based on the result of the primary frequency modulation power distribution.
  • C i (k+1) is the confidence function at the k+1th moment
  • the power reference value of the i-th power generation unit at the k-th time is the power deviation function of the i-th power generation unit at the k-th time, which is defined as follows:
  • ⁇ is a set threshold value to prevent the decrease of confidence due to normal power fluctuation
  • P i (k) is the generated power of the i-th power generation unit at the k-th time.
  • This description provides a networked control method for primary frequency regulation of a new energy power station, which can estimate the frequency regulation potential of a new energy power station based on a short-term real-time prediction algorithm, and solves the problem of primary frequency regulation caused by the uncertainty and volatility of new energy power generation.
  • the problem of poor FM accuracy In addition, in the real-time power distribution of the new energy power station participating in the primary frequency regulation, the corresponding optimization objective function is designed considering the start-up cost of the power generation unit of the new energy power station, so as to solve the problem of slow response speed of primary frequency regulation, poor frequency regulation accuracy and network communication delay in practical engineering. At the same time, it also ensures the economy of the new energy power station participating in the primary frequency regulation.
  • the dynamic weight coefficient based on the confidence function to correct the primary frequency modulation output of each new energy power station power generation unit, the problem of limited adjustable power and failure of some new energy power station power generation units is effectively solved.
  • FIG. 1 is a flowchart of a networked control method for primary frequency regulation of a new energy power station according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of a control loop in the PQ mode of the power generation unit inverter of the new energy power plant of the present invention
  • FIG. 3 is the equivalent transfer function of the control loop of the power generation unit inverter of the new energy power plant in the PQ mode of the present invention.
  • the existing primary frequency modulation methods of new energy power stations have poor frequency modulation accuracy, slow frequency modulation speed, and some new energy power stations also have problems such as aging communication equipment, resulting in a low success rate of primary frequency modulation of new energy power stations.
  • the present invention proposes a networked control method for primary frequency regulation of a new energy power station.
  • primary frequency regulation is activated to provide support for grid frequency stability.
  • This method is based on the source-grid-load-storage networked cloud decision-making control system platform.
  • the prediction algorithm is used to evaluate the primary frequency regulation potential of the new energy power station, and considering the power generation cost of the new energy power station unit, an online rolling optimization based on model predictive control is designed.
  • the method is used to distribute the primary frequency modulation power of the power generation units in the new energy power station.
  • FIG. 1 shows a flowchart of a networked control method for primary frequency regulation of a new energy power station provided by an embodiment of the present invention. As shown in Figure 1, the method at least includes the following steps:
  • Step 11 Determine the estimated value of the primary frequency regulation of each power generation unit of the new energy power station according to the historical operation data of the new energy power station.
  • the estimated value of the primary frequency regulation of each power generation unit may be determined in different specific ways. For example, in one embodiment, based on the historical operation data of the new energy power station, the least squares support vector machine may be used to realize Estimate the primary frequency regulation potential of each power generation unit. This specification does not limit the specific method used to determine the estimated value of primary frequency modulation.
  • the historical operating data may include one or more of at least 24 hours of historical active power data, at least 24 hours of light intensity data, and at least 24 hours of wind speed data.
  • Step 12 According to the estimated value of the primary frequency regulation, and based on the inverter active power model established in advance, determine the optimal control sequence of each time period of the inverter of each power generation unit in an online rolling optimization manner.
  • the optimal control sequence includes a plurality of control quantities of inverter active power.
  • each unit of the new energy power station transmits power to the bus through the inverter.
  • its power is required to be adjustable, so it must operate in the PQ mode.
  • an equivalent mathematical model in the inverter PQ mode can be established to obtain the operating characteristics of each unit of the new energy power station.
  • the control loop in the inverter PQ mode is composed of the power outer loop and the current inner loop of the dq coordinate axis, ignoring the disturbance of the q axis, the active power and reactive power output by the inverter can be calculated by the formula (1) Calculate:
  • u od is the component of the inverter output port voltage on the d-axis
  • i od and i oq are the components of the inverter output port current on the d-axis and q-axis, respectively
  • P dg and Q dg are Active and reactive power output by the inverter.
  • the delay from the inverter receiving the control command to the start of execution can be equivalent to a first-order inertia link, which is represented by introducing a time constant T d .
  • the time constant is introduced and to represent the dynamic response characteristics of the current inner loop. Therefore, in an example, the equivalent mathematical model of the output active power of the inverter of the new energy power station in PQ mode can be expressed as:
  • ⁇ P ref is the difference between the actual reference power value of the inverter and the current power value
  • ⁇ i od is the current component of the inverter d-axis and the current at the previous moment difference of components
  • u od is the component of the inverter output port voltage on the d-axis
  • ⁇ P dg is the difference between the output power of the inverter at the current moment and the output power at the previous moment
  • ⁇ P int is the integral of the difference between ⁇ P ref and ⁇ P dg
  • s is the Laplace operator.
  • x(k) [ ⁇ P dg (k) ⁇ P int (k) ⁇ i od (k) ⁇ P ref (k)] T , where ⁇ P dg (k) is the inverter output power at the kth time
  • ⁇ i od (k) is the difference between the current component at the k-th moment of the d-axis of the inverter and the current component at the previous moment
  • ⁇ P ref (k) is the actual reference power of the inverter
  • ⁇ P int (k) is the integral of the difference between ⁇ P ref (k) and ⁇ P dg (k); is the difference between the reference power value of the inverter and the power value at the kth moment
  • T p is the sampling time.
  • a primary frequency modulation power distribution method based on model predictive control is proposed. Taking the system frequency lower than the rated frequency to enable primary frequency modulation as an example, the objective function of model predictive control can be expressed as:
  • N p is the length of the prediction domain
  • N is the number of power generation units in the new energy power station
  • ⁇ i (k) is the weight coefficient of the ith power generation unit
  • ⁇ i (k) -b i ⁇ P i ( k)
  • ⁇ P i (k) is the power change of the i-th power generation unit at the k-th time relative to the k-1-th time.
  • C i (k) is the confidence of the i-th power generation unit at the k-th time, and the initial value is 1, and are the upper and lower limits of the estimated value of the primary frequency modulation calculated in step 11, respectively
  • ⁇ P is the total power required by the new energy power station to participate in the primary frequency modulation
  • ⁇ P total is the total estimated value of the new energy power station participating in the primary frequency modulation
  • P i (k ) is the generated power of the i-th power generation unit at the k-th time
  • ⁇ f is the system frequency deviation
  • K is the primary frequency modulation coefficient of the new energy power station
  • Equation (11) is the mathematical model of the active power output by the inverter, and its meaning is the same as that of Equation ( 4) Same.
  • the optimal control sequence can be obtained by solving (5) as:
  • u * (k) is the optimal control sequence at the kth moment
  • N c is the length of the control domain
  • It is the inverter control amount at the k+ith time point predicted at the kth time point.
  • the communication network often suffers from delay, which affects the control performance. Therefore, a networked dynamic compensation mechanism with the ability to cope with the delay of the communication network can be established based on the idea of predictive compensation.
  • step 13 the optimal control sequence of each time period is marked with a time stamp, and sent to the execution device of each power generation unit according to the time period, and the execution device of each power generation unit receives the optimal control sequence, and according to the time period
  • the time stamp determines whether or not to store the optimal control sequence.
  • the maximum delay of the communication network is not greater than the control domain length N c in equation (12).
  • the optimal control sequence calculated in step 12 is packaged, and the optimal control sequence package is time-stamped by the time synchronization device, and then sent to the execution device of the corresponding power generation unit for storage, and the corresponding power generation is performed in subsequent steps.
  • the unit's actuators are time-scaled for comparison.
  • the received optimal control sequence if the time scale of the optimal control sequence received by the executing device of each power generation unit is less than or equal to the time scale of the stored optimal control sequence, the received optimal control sequence is not stored; otherwise, the received optimal control sequence is stored. optimal control sequence.
  • Step 14 The execution device of each power generation unit determines the control amount to be executed according to the received and stored optimal control sequence and the time scale thereof.
  • the time scale of the received optimal control sequence is consistent with the time scale of the stored optimal control sequence, it is determined that the communication network is normal and there is no delay, and at this time, the first step of the stored optimal control sequence is executed. a control amount.
  • the optimal control sequence package in step 13 is not received at the current time k, and it is assumed that the optimal control sequence package stored by the execution device of the ith power generation unit at this time is:
  • k l is the time scale of the optimal control sequence package stored by the execution device of the ith power generation unit at the current k time.
  • the execution control amount Since it is assumed that the maximum delay of the communication network is not greater than the control domain length N c in formula (12), when there is a delay in the communication network, the control amount exist.
  • the time scale of the optimal control sequence packet received at the current time k is smaller than the time scale of the stored optimal control sequence packet, and it is assumed that k l is the execution device of the ith power generation unit at the current time k.
  • the time stamp of the stored optimal control sequence packet, at which time the control quantity is executed Since it is assumed that the maximum delay of the communication network is not greater than the control domain length N c in formula (12), when there is a delay in the communication network, the control amount exist.
  • the time scale of the optimal control sequence packet received at the current k time is greater than the time scale of the stored optimal control sequence packet, and k r is assumed to be received by the execution device of the ith power generation unit at the current k time
  • the time scale of the optimal control sequence package arrived, at which time the control quantity is executed Since it is assumed that the maximum delay of the communication network is not greater than the control domain length N c in formula (12), when there is a delay in the communication network, the control amount exist.
  • step 12 according to the estimated value of the primary frequency modulation, based on the inverter active power model established in advance, determine the power distribution result of the primary frequency modulation of each power generation unit, according to the preset
  • the confidence function adjusts the primary frequency modulation power distribution result, and determines the optimal control sequence for each time period of the inverter of each power generation unit according to the adjusted primary frequency modulation power distribution result.
  • the expression of the confidence function may be:
  • C i (k+1) is the confidence function at the k+1th moment
  • the power reference value of the i-th power generation unit at the k-th time is the power deviation function of the i-th power generation unit at the k-th time, which is defined as follows:
  • ⁇ is a set threshold to prevent the confidence from dropping due to normal power fluctuations.
  • the frequency regulation potential of a new energy power station can be estimated based on a short-term real-time prediction algorithm, and the uncertainty and volatility of power generation due to new energy can be solved.
  • the problem caused by the poor accuracy of the primary frequency modulation.
  • the corresponding optimization objective function is designed considering the start-up cost of the power generation unit of the new energy power station, so as to solve the problem of slow response speed of primary frequency regulation, poor frequency regulation accuracy and network communication delay in practical engineering. At the same time, it also ensures the economy of the new energy power station participating in the primary frequency regulation.
  • the dynamic weight coefficient based on the confidence function is used to correct the primary frequency regulation output of each new energy power station power generation unit, which effectively solves the problem of limited adjustable power and failure of some new energy power station power generation units.

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Abstract

本发明公开一种基于源网荷储网络化云决策控制系统平台的新能源电站一次调频网络化控制方法，包括：根据新能源电站的历史运行数据，确定新能源电站各发电单元的一次调频预估值；根据一次调频预估值，基于逆变器有功功率模型，确定各发电单元的逆变器各个时段的最优控制序列，最优控制序列包括多个逆变器有功功率的控制量；将各个时段的最优控制序列标注时标，并根据时段发送到各发电单元的执行装置，各发电单元执行装置接收最优控制序列，并根据时标确定存储或不存储所述最优控制序列；各发电单元执行装置根据接收和已存储的最优控制序列及其时标，确定执行的控制量。本发明解决了因新能源发电功率不确定性和波动性引起的一次调频准确性差的问题。

Description

一种新能源电站一次调频网络化控制方法 技术领域
本发明属于智能电网控制领域，具体涉及一种新能源电站一次调频网络化控制方法。
背景技术
近年来，新能源发电等新能源在世界范围内得到了快速发展。从2015年至今，中国光伏发电并网装机容量持续位居世界第一，并仍呈现较快增长趋势。然而，随着大量电力电子器件的采用，电力系统逐渐呈现低惯量特征，对系统频率动态安全的影响引发关注，电网运行压力日益增大。而当前新能源电站参与一次调频的方式主要还是以单次下发调频指令的方式为主，其调频精度差，调频速度慢，且部分新能源电站还存在通信设备老化等问题，导致一次调频的成功率大打折扣。
发明内容
本发明的目的在于提供一种新能源电站一次调频网络化控制方法，以解决现有的新能源电站一次调频方法存在调频精度差，调频速度慢，且部分新能源电站还存在通信设备老化等问题，导致新能源电站一次调频成功率较低的问题。
为解决上述技术问题，本发明提供一种新能源电站一次调频网络化控制方法，所述方法包括：
根据新能源电站的历史运行数据，确定新能源电站的各发电单元的一次调频预估值；
根据所述一次调频预估值，基于预先建立的逆变器有功功率模型，确定所述各发电单元的逆变器的各个时段的最优控制序列，所述最优控制序列包括多个逆变器有功功率的控制量；
将所述各个时段的最优控制序列标注时标，并根据所述时段发送到各发电单元的执行装置，所述各发电单元的执行装置接收最优控制序列，并根据所述时标确定存储或不存储所述最优控制序列；
各发电单元的执行装置根据接收和已存储的最优控制序列以及其所述时标， 确定执行的所述控制量，从而实现对通信网络延时的预测补偿。
进一步地，所述历史运行数据包括至少24小时的历史有功功率数据、至少24小时的光照强度数据、至少24小时的风速数据中的一种或多种。
进一步地，所述逆变器有功功率模型通过以下步骤建立：
建立逆变器PQ模式下输出有功功率的等效数学模型：
Figure PCTCN2021109974-appb-000001
其中，
Figure PCTCN2021109974-appb-000002
为逆变器的参考功率值与当前功率值之差，ΔP ref为逆变器实际的参考功率值与当前功率值之差，T d为逆变器接收到控制指令到开始执行的延时，Δi od为逆变器d轴当前的电流分量与上一时刻电流分量的差值，
Figure PCTCN2021109974-appb-000003
为有功功率电流内环的时间常数，
Figure PCTCN2021109974-appb-000004
和
Figure PCTCN2021109974-appb-000005
分别为功率外环PI控制器的比例系数和积分系数，ΔP dg为当前时刻逆变器输出功率与上一时刻输出功率的差值，u od为逆变器输出端口电压在d轴的分量，ΔP int为ΔP ref和ΔP dg差值的积分量，s为拉普拉斯算子；
基于所述等效数学模型，建立逆变器输出有功功率的状态空间模型：
Figure PCTCN2021109974-appb-000006
其中，
Figure PCTCN2021109974-appb-000007
Figure PCTCN2021109974-appb-000008
对
Figure PCTCN2021109974-appb-000009
进行离散化，得到离散时间下逆变器输出有功功率的数学模型：
x(k+1)＝Ax(k)+Bu(k)
其中，x(k)＝[ΔP dg(k) ΔP int(k) Δi od(k) ΔP ref(k)] T，ΔP dg(k)为第k时刻逆变器输出功率与上一时刻输出功率的差值，Δi od(k)为逆变器d轴第k时刻的电流分量与上一时刻电流分量的差值，ΔP ref(k)为逆变器实际的参考功率值与第k时刻功率值之差，ΔP int(k)为ΔP ref(k)和ΔP dg(k)差值的积分量，
Figure PCTCN2021109974-appb-000010
Figure PCTCN2021109974-appb-000011
为逆变器的参考功率值与第k时刻功率值之差，
Figure PCTCN2021109974-appb-000012
Figure PCTCN2021109974-appb-000013
T p为采样时间。
进一步地，根据所述一次调频预估值，基于预先建立的逆变器有功功率模型，确定所述各发电单元的逆变器的各个时段的最优控制序列，包括：
当系统频率低于额定频率启用一次调频时，基于所述逆变器有功功率模型，利用预测控制的目标函数，确定所述各发电单元的逆变器的各个时段的最优控制序列，所述预测控制的目标函数表示为：
Figure PCTCN2021109974-appb-000014
其中，N p为预测域长度，N为新能源电站的发电单元数量，λ i(k)为第i个发电单元的权重系数，Φ i(k)＝-b iΔP i(k),b i＝{0,1}为第i个发电单元的成本函数，ΔP i(k)为第k时刻第i个发电单元的相对于第k-1时刻的功率变化量；
所述目标函数的约束条件为：
Figure PCTCN2021109974-appb-000015
Figure PCTCN2021109974-appb-000016
Figure PCTCN2021109974-appb-000017
ΔP＝KΔf
Figure PCTCN2021109974-appb-000018
x(k+1)＝Ax(k)+Bu(k)
其中，C i(k)为第i个发电单元在第k时刻的置信度，初始值为1，
Figure PCTCN2021109974-appb-000019
和
Figure PCTCN2021109974-appb-000020
分别为一次调频预估值的上下限，ΔP为新能源电站参与一次调频所需的总功率，ΔP total为新能源电站参与一次调频的总预估值，P i(k)为第k时刻第i个发电单元的发电功率，Δf为系统频率偏差量，K为新能源电站的一次调频系数；x(k+1)＝Ax(k)+Bu(k)为离散时间下的逆变器有功功率模型；
所述最优控制序列为：
Figure PCTCN2021109974-appb-000021
其中，u *(k)为第k时刻的最优控制序列，N c为控制域长度，
Figure PCTCN2021109974-appb-000022
为第k时刻预测的第k+i时刻逆变器控制量。
进一步地，所述各发电单元的执行装置接收最优控制序列，并根据所述时标确定存储或不存储所述最优控制序列，包括：
若各发电单元的执行装置接收的最优控制序列的时标小于等于已存储的最优控制序列的时标，则不存储接收的最优控制序列；
否则，存储接收的最优控制序列。
进一步地，所述各发电单元的执行装置根据接收和已存储的最优控制序列以及其所述时标，确定执行的所述控制量，包括：
若当前时刻，各发电单元的执行装置接收的最优控制序列和其存储的最优控制序列的时标一致，则执行其存储的最优控制序列的第一个控制量。
进一步地，所述各发电单元的执行装置根据接收和已存储的最优控制序列以及其所述时标，确定执行的所述控制量，包括：
若当前k时刻，第i个发电单元的执行装置存储的最优控制序列包为：
Figure PCTCN2021109974-appb-000023
其中，k l为当前k时刻第i个发电单元的执行装置存储的最优控制序列包的时标，
Figure PCTCN2021109974-appb-000024
为第k l时刻预测的第k l+i时刻第i个逆变器控制量；
若当前k时刻，第i个发电单元的执行装置没有接收到最优控制序列包，执行控制量
Figure PCTCN2021109974-appb-000025
若当前k时刻，第i个发电单元的执行装置接收到的最优控制序列包的时标小于k l，执行控制量
Figure PCTCN2021109974-appb-000026
进一步地，所述各发电单元的执行装置根据接收和已存储的最优控制序列以及其所述时标，确定执行的所述控制量，还包括：
若当前k时刻，第i个发电单元的执行装置接收的最优控制序列包
Figure PCTCN2021109974-appb-000027
为：
Figure PCTCN2021109974-appb-000028
其中，k r为当前k时刻第i个发电单元的执行装置接收到的最优控制序列包的时标，
Figure PCTCN2021109974-appb-000029
为第k r时刻预测的第k r+i时刻第i个逆变器控制量
若当前k时刻，第i个发电单元的执行装置接收到的最优控制序列包的时标k r大于k l，执行控制量
Figure PCTCN2021109974-appb-000030
进一步地，根据所述一次调频预估值，基于预先设立的逆变器有功功率模型，确定所述各发电单元的逆变器的各个时段的最优控制序列，还包括：
根据所述一次调频预估值，基于预先设立的逆变器有功功率模型，确定所述各发电单元的一次调频功率分配结果，根据预设置信度函数调整所述一次调频功率分配结果，根据调整后的一次调频功率分配结果，确定所述各发电单元的逆变器的各个时段的最优控制序列。
进一步地，所述置信度函数的数学表达式为：
Figure PCTCN2021109974-appb-000031
其中，C i(k+1)为第k+1时刻的置信度函数，
Figure PCTCN2021109974-appb-000032
第i个发电单元在第k 时刻的功率参考值，
Figure PCTCN2021109974-appb-000033
为第i个发电单元在第k时刻的功率偏差函数，其定义如下：
Figure PCTCN2021109974-appb-000034
其中，δ为设定的阈值，防止因正常的功率波动而造成置信度的下降，P i(k)为第k时刻第i个发电单元的发电功率。相比于现有技术，本发明所达到的有益技术效果：
本说明提供的一种新能源电站一次调频网络化控制方法，可以基于短期实时预测算法对新能源电站的调频潜力进行预估，解决了因新能源发电功率的不确定性和波动性引起的一次调频准确性差的问题。并且，在新能源电站参与一次调频的实时功率分配中，考虑了新能源电站发电单元的启动成本设计相应的优化目标函数，在解决实际工程中一次调频响应速度慢、调频精度差、网络通信延迟等难题的同时，也保证了新能源电站参与一次调频的经济性。此外，通过采用基于置信度函数的动态权重系数对每个新能源电站发电单元的一次调频出力进行修正，有效地解决了部分新能源电站发电单元可调功率受限以及发生故障的问题。
附图说明
图1是本发明实施例的一种新能源电站一次调频网络化控制方法流程图；
图2是本发明的新能源电厂发电单元逆变器PQ模式下的控制环路示意图；
图3是本发明的新能源电厂发电单元逆变器PQ模式下的控制环路的等效传递函数。
具体实施方式
下面结合具体实施例对本发明作进一步描述。以下实施例仅用于更加清楚地说明本发明的技术方案，而不能以此来限制本发明的保护范围。
如前所述，现有的新能源电站一次调频方法存在调频精度差，调频速度慢，且部分新能源电站还存在通信设备老化等问题，导致新能源电站一次调频的成功率较低的问题。
为解决上述技术问题，本发明提出一种新能源电站一次调频网络化控制方法，当系统频率超出一次调频死区时，启动一次调频，为电网频率稳定提供支撑。该方法基于源网荷储网络化云决策控制系统平台，采用预测算法对新能源电站的 一次调频潜力进行评估，并考虑新能源电站单元的发电成本，设计了基于模型预测控制的在线式滚动优化方法，以对新能源电站内发电单元的一次调频功率进行分配。
图1示出本发明实施例提供的一种新能源电站一次调频网络化控制方法的流程图。如图1所示，该方法至少包括如下步骤：
步骤11，根据新能源电站的历史运行数据，确定新能源电站的各发电单元的一次调频预估值。
在不同的实施例中，可以采用不同的具体方式确定各发电单元的一次调频预估值，例如，在一个实施例中，可以基于新能源电站的历史运行数据，采用最小二乘支持向量机实现对各发电单元的一次调频潜力预估。本说明书对于采用何种具体方式确定一次调频预估值不做限定。
在一个实施例中，历史运行数据可以包括至少24小时的历史有功功率数据、至少24小时的光照强度数据、至少24小时的风速数据中的一种或多种。
步骤12，根据所述一次调频预估值，基于预先建立的逆变器有功功率模型，以在线滚动优化的方式确定所述各发电单元的逆变器的各个时段的最优控制序列，所述最优控制序列包括多个逆变器有功功率的控制量。
具体的，在一个实施例中，新能源电站各单元均通过逆变器向母线输送功率，当参与一次调频时，要求其功率可调，因此要运行在PQ模式下。在一个例子中，可以建立逆变器PQ模式下的等效数学模型，得到新能源电站各单元的运行特性。如图2所示，逆变器PQ模式下的控制环路由dq坐标轴的功率外环以及电流内环构成，忽略其q轴的扰动，逆变器输出的有功功率和无功功率可以通过公式(1)进行计算：
Figure PCTCN2021109974-appb-000035
式(1)中，u od为逆变器输出端口电压在d轴的分量，i od和i oq分别为逆变器输出端口电流在d轴和q轴的分量，P dg和Q dg分别为逆变器输出的有功功率和无功功率。
如图3所示，逆变器接收到控制指令到开始执行的延时可以等效为一阶惯性环节，通过引入时间常数T d来表示。同时，引入时间常数
Figure PCTCN2021109974-appb-000036
和
Figure PCTCN2021109974-appb-000037
来表示电流内环的动态响应特性。因此，在一个例子中，新能源电站逆变器PQ模式下输出有功功率的等效数学模型可以表示为：
Figure PCTCN2021109974-appb-000038
式(2)中，
Figure PCTCN2021109974-appb-000039
为逆变器的参考功率值与当前功率值之差，ΔP ref为逆变器实际的参考功率值与当前功率值之差，Δi od为逆变器d轴当前的电流分量与上一时刻电流分量的差值，
Figure PCTCN2021109974-appb-000040
为有功功率电流内环的时间常数，
Figure PCTCN2021109974-appb-000041
和
Figure PCTCN2021109974-appb-000042
分别为功率外环PI控制器的比例系数和积分系数，u od为逆变器输出端口电压在d轴的分量，ΔP dg为当前时刻逆变器输出功率与上一时刻输出功率的差值，ΔP int为ΔP ref和ΔP dg差值的积分量，s为拉普拉斯算子。
同理可得，逆变器PQ模式下输出无功功率的等效数学模型。由于新能源电站参与一次调频仅需改变有功功率，因此，本发明仅围绕其有功功率的控制进行建模。通过公式(2)可建立状态空间模型如下：
Figure PCTCN2021109974-appb-000043
其中，
Figure PCTCN2021109974-appb-000044
Figure PCTCN2021109974-appb-000045
Figure PCTCN2021109974-appb-000046
Figure PCTCN2021109974-appb-000047
通过将公式(3)进行离散化，可以得到离散时间下逆变器输出有功功率的数学模型：
x(k+1)＝Ax(k)+Bu(k)        (4)
式(4)中，x(k)＝[ΔP dg(k) ΔP int(k) Δi od(k) ΔP ref(k)] T，其中ΔP dg(k)为第k时刻逆变器输出功率与上一时刻输出功率的差值，Δi od(k)为逆变器d轴第k时刻的电流分量与上一时刻电流分量的差值，ΔP ref(k)为逆变器实际的参考功率值与第k时刻功率值之差，ΔP int(k)为ΔP ref(k)和ΔP dg(k)差值的积分量；
Figure PCTCN2021109974-appb-000048
Figure PCTCN2021109974-appb-000049
为逆变器的参考功率值与第k时刻功率值之差，
Figure PCTCN2021109974-appb-000050
T p为采样时间。
在一个实施例中，基于建立的逆变器PQ模式下输出有功功率的数学模型，提出基于模型预测控制的一次调频功率分配方法。以系统频率低于额定频率启用一次调频为例，模型预测控制的目标函数可以表示为：
Figure PCTCN2021109974-appb-000051
式(5)中，N p为预测域长度，N为新能源电站的发电单元数量，λ i(k)为 第i个发电单元的权重系数，Φ i(k)＝-b iΔP i(k),b i＝{0,1}为第i个发电单元的成本函数，ΔP i(k)为第k时刻第i个发电单元的相对于第k-1时刻的功率变化量。
公式(5)的目标函数受到以下条件的约束：
Figure PCTCN2021109974-appb-000052
Figure PCTCN2021109974-appb-000053
Figure PCTCN2021109974-appb-000054
ΔP＝KΔf          (9)
Figure PCTCN2021109974-appb-000055
x(k+1)＝Ax(k)+Bu(k)         (11)
式(6)中，C i(k)为第i个发电单元在第k个时刻的置信度，初始值为1，
Figure PCTCN2021109974-appb-000056
和
Figure PCTCN2021109974-appb-000057
分别为由步骤11计算得到的一次调频预估值的上下限，ΔP为新能源电站参与一次调频所需的总功率，ΔP total为新能源电站参与一次调频的总预估值，P i(k)为第k时刻第i个发电单元的发电功率，Δf为系统频率偏差量，K为新能源电站的一次调频系数；式(11)为逆变器输出有功功率的数学模型，含义与式(4)相同。在一个实施例中，求解(5)可以得到最优控制序列为：
Figure PCTCN2021109974-appb-000058
式中，u *(k)为第k时刻的最优控制序列，N c为控制域长度，
Figure PCTCN2021109974-appb-000059
为第k时刻预测的第k+i时刻逆变器控制量。
实践中，通信网络常常出现延时进而影响控制性能，因此可以基于预测补偿思想设立具有应对通信网络延时能力的网络化动态补偿机制。
于是，在步骤13，将所述各个时段的最优控制序列标注时标，并根据所述时段发送到各发电单元的执行装置，所述各发电单元的执行装置接收最优控制序 列，并根据所述时标确定存储或不存储所述最优控制序列。
具体的，在一个实施例中，可以假设通信网络的延时最大量不大于式(12)中的控制域长度N c。将步骤12中计算得到的最优控制序列打包，并通过时间同步装置对所述最优控制序列包加上时标，然后下发至相应发电单元的执行装置进行存储，在后续步骤由相应发电单元的执行装置进行时标对比。
在一个实施例中，若各发电单元的执行装置接收的最优控制序列的时标小于等于已存储的最优控制序列的时标，则不存储接收的最优控制序列；否则，存储接收的最优控制序列。
步骤14，各发电单元的执行装置根据接收和已存储的最优控制序列以及其所述时标，确定执行的所述控制量。
在一个实施例中，若接收到的最优控制序列时标和已存储的最优控制序列时标一致，则判断通信网络正常，无延时，此时执行存储的最优控制序列的第一个控制量。
在另一个实施例中，若接收到的最优控制序列时标和已存储的最优控制序列时标不一致或没有接收到最优控制序列包，则判断通信网络存在延时。因此，在一个例子中，若当前k时刻没有接收到步骤13中的最优控制序列包，并假设此时第i个发电单元的执行装置存储的最优控制序列包为：
Figure PCTCN2021109974-appb-000060
其中，k l为当前k时刻第i个发电单元的执行装置存储的最优控制序列包的时标。此时，执行控制量
Figure PCTCN2021109974-appb-000061
由于假设通信网络的延时最大量不大于公式(12)中的控制域长度N c，因此，当通信网络存在延时情况时，控制量
Figure PCTCN2021109974-appb-000062
存在。
在又一个实施例中，若当前k时刻接收到的最优控制序列包的时标小于存储的最优控制序列包的时标，并假设k l为当前k时刻第i个发电单元的执行装置存储的最优控制序列包的时标，此时执行控制量
Figure PCTCN2021109974-appb-000063
由于假设通信网络的延时最大量不大于公式(12)中的控制域长度N c，因此，当通信网络存在延 时情况时，控制量
Figure PCTCN2021109974-appb-000064
存在。
在又一个例子中，若当前k时刻接收到的最优控制序列包的时标大于存储的最优控制序列包的时标，并假设k r为当前k时刻第i个发电单元的执行装置接收到的最优控制序列包的时标，此时执行控制量
Figure PCTCN2021109974-appb-000065
由于假设通信网络的延时最大量不大于公式(12)中的控制域长度N c，因此，当通信网络存在延时情况时，控制量
Figure PCTCN2021109974-appb-000066
存在。
通过上述步骤，可以实现新能源电站的一次调频功率的在线分配和预测补偿，然而当电站内部分单元出现故障或发电功率受限的情况时会影响一次调频的效果。
因此，根据一种实施方式，在步骤12中还可以根据所述一次调频预估值，基于预先设立的逆变器有功功率模型，确定所述各发电单元的一次调频功率分配结果，根据预设置信度函数调整所述一次调频功率分配结果，根据调整后的一次调频功率分配结果，确定所述各发电单元的逆变器的各个时段的最优控制序列。
具体的，在一个实施例中，所述置信度函数的表达可以为：
Figure PCTCN2021109974-appb-000067
其中，C i(k+1)为第k+1时刻的置信度函数，
Figure PCTCN2021109974-appb-000068
第i个发电单元在第k时刻的功率参考值，
Figure PCTCN2021109974-appb-000069
为第i个发电单元在第k时刻的功率偏差函数，其定义如下：
Figure PCTCN2021109974-appb-000070
其中，δ为设定的阈值，防止因正常的功率波动而造成置信度的下降。
通过如公式(14)所示的置信度函数，若在第k时刻第i个发电单元的发电功率P i(k)达到了设定值
Figure PCTCN2021109974-appb-000071
则第k+1时刻第i个发电单元的置信度C i(k+1)为1；若第k个时刻第i个发电单元的发电功率P i(k)未达到设定值
Figure PCTCN2021109974-appb-000072
则减小其置信度，相应的，其承担的一次调频功率也将减小；若第i个发电单元的发电 功率达到上限，或因故障导致光伏单元无动作，则C i(k+1)为0，由公式(5)可知，其在第k+1时刻的权重值λ i(k+1)也为0，不再提供一次调频的功率支撑。
通过本说明书实施例提供的一种新能源电站一次调频网络化控制方法，可以基于短期实时预测算法对新能源电站的调频潜力进行预估，解决了因新能源发电功率的不确定性和波动性引起的一次调频准确性差的问题。并且，在新能源电站参与一次调频的实时功率分配中，考虑了新能源电站发电单元的启动成本设计相应的优化目标函数，在解决实际工程中一次调频响应速度慢、调频精度差、网络通信延迟等难题的同时，也保证了新能源电站参与一次调频的经济性。最后采用基于置信度函数的动态权重系数对每个新能源电站发电单元的一次调频出力进行修正，有效地解决了部分新能源电站发电单元可调功率受限以及发生故障的问题。
以上已以较佳实施例公布了本发明，然其并非用以限制本发明，凡采取等同替换或等效变换的方案所获得的技术方案，均落在本发明的保护范围内。

Claims (10)

  1. 一种新能源电站一次调频网络化控制方法，其特征在于，所述方法包括：
    根据新能源电站的历史运行数据，确定新能源电站的各发电单元的一次调频预估值；
    根据所述一次调频预估值，基于预先建立的逆变器有功功率模型，确定所述各发电单元的逆变器的各个时段的最优控制序列，所述最优控制序列包括多个逆变器有功功率的控制量；
    将所述各个时段的最优控制序列标注时标，并根据所述时段发送到各发电单元的执行装置，所述各发电单元的执行装置接收最优控制序列，并根据所述时标确定存储或不存储所述最优控制序列；
    各发电单元的执行装置根据接收和已存储的最优控制序列以及其所述时标，确定执行的所述控制量。
  2. 根据权利要求1所述的方法，其特征在于，所述历史运行数据包括至少24小时的历史有功功率数据、至少24小时的光照强度数据、至少24小时的风速数据中的一种或多种。
  3. 根据权利要求1所述的方法，其特征在于，所述逆变器有功功率模型通过以下步骤建立：
    建立逆变器PQ模式下输出有功功率的等效数学模型：
    Figure PCTCN2021109974-appb-100001
    其中，
    Figure PCTCN2021109974-appb-100002
    为逆变器的参考功率值与当前功率值之差，ΔP ref为逆变器实际的参考功率值与当前功率值之差，T d为逆变器接收到控制指令到开始执行的延时，Δi od为逆变器d轴当前的电流分量与上一时刻电流分量的差值，
    Figure PCTCN2021109974-appb-100003
    为有功 功率电流内环的时间常数，
    Figure PCTCN2021109974-appb-100004
    和
    Figure PCTCN2021109974-appb-100005
    分别为功率外环PI控制器的比例系数和积分系数，ΔP dg为当前时刻逆变器输出功率与上一时刻输出功率的差值，u od为逆变器输出端口电压在d轴的分量，ΔP int为ΔP ref和ΔP dg差值的积分量，s为拉普拉斯算子；
    基于所述等效数学模型，建立逆变器输出有功功率的状态空间模型：
    Figure PCTCN2021109974-appb-100006
    其中，
    Figure PCTCN2021109974-appb-100007
    Figure PCTCN2021109974-appb-100008
    对
    Figure PCTCN2021109974-appb-100009
    进行离散化，得到离散时间下逆变器输出有功功率的数学模型：
    x(k+1)＝Ax(k)+Bu(k)
    其中，x(k)＝[ΔP dg(k) ΔP int(k) Δi od(k) ΔP ref(k)] T，ΔP dg(k)为第k时刻逆变器输出功率与上一时刻输出功率的差值，Δi od(k)为逆变器d轴第k时刻的电流分量与上一时刻电流分量的差值，ΔP ref(k)为逆变器实际的参考功率值与第k时刻功率值之差，ΔP int(k)为ΔP ref(k)和ΔP dg(k)差值的积分量，
    Figure PCTCN2021109974-appb-100010
    Figure PCTCN2021109974-appb-100011
    为逆变器的参考功率值与第k时刻功率值之差，
    Figure PCTCN2021109974-appb-100012
    Figure PCTCN2021109974-appb-100013
    T p为采样时间。
  4. 根据权利要求3所述的方法，其特征在于，所述根据所述一次调频预估值，基于预先建立的逆变器有功功率模型，确定所述各发电单元的逆变器的各个时段的最优控制序列，包括：
    当以系统频率低于额定频率启用一次调频时，基于所述逆变器有功功率模型，利用预测控制的目标函数，确定所述各发电单元的逆变器的各个时段的最优控制序列，所述预测控制的目标函数表示为：
    Figure PCTCN2021109974-appb-100014
    其中，N p为预测域长度，N为新能源电站的发电单元数量，λ i(k)为第i个发电单元的权重系数，Φ i(k)＝-b iΔP i(k),b i＝{0,1}为第i个发电单元的成本函数，ΔP i(k)为第k时刻第i个发电单元的相对于第k-1时刻的功率变化量；
    所述目标函数的约束条件为：
    Figure PCTCN2021109974-appb-100015
    Figure PCTCN2021109974-appb-100016
    Figure PCTCN2021109974-appb-100017
    ΔP＝KΔf
    ΔP i min≤ΔP i(k)≤ΔP i max,i＝1,2…N
    x(k+1)＝Ax(k)+Bu(k)
    其中，C i(k)为第i个发电单元在第k时刻的置信度，初始值为1，ΔP i max和ΔP i min分别为一次调频预估值的上下限，ΔP为新能源电站参与一次调频所需的总功率，ΔP total为新能源电站参与一次调频的总预估值，P i(k)为第k时刻第i个发电单元的发电功率，Δf为系统频率偏差量，K为新能源电站的一次调频系数；x(k+1)＝Ax(k)+Bu(k)为离散时间下的逆变器有功功率模型；
    所述最优控制序列为：
    Figure PCTCN2021109974-appb-100018
    其中，u *(k)为第k时刻的最优控制序列，N c为控制域长度，
    Figure PCTCN2021109974-appb-100019
    为第k时刻预测的第k+i时刻逆变器控制量。
  5. 根据权利要求1所述的方法，其特征在于，所述各发电单元的执行装置接收最优控制序列，并根据所述时标确定存储或不存储所述最优控制序列，包括：
    若各发电单元的执行装置接收的最优控制序列的时标小于等于已存储的最优控制序列的时标，则不存储接收的最优控制序列；
    否则，存储接收的最优控制序列。
  6. 根据权利要求1所述的方法，其特征在于，所述各发电单元的执行装置根据接收和已存储的最优控制序列以及其所述时标，确定执行的所述控制量，包括：
    若当前时刻，各发电单元的执行装置接收的最优控制序列和其存储的最优控制序列的时标一致，则执行其存储的最优控制序列的第一个控制量。
  7. 根据权利要求1所述的方法，其特征在于，所述各发电单元的执行装置根据接收和已存储的最优控制序列以及其所述时标，确定执行的所述控制量，包括：
    若当前k时刻，第i个发电单元的执行装置存储的最优控制序列包为：
    Figure PCTCN2021109974-appb-100020
    其中，k l为当前k时刻第i个发电单元的执行装置存储的最优控制序列包的时标，
    Figure PCTCN2021109974-appb-100021
    为第k l时刻预测的第k l+i时刻第i个逆变器控制量；
    若当前k时刻，第i个发电单元的执行装置没有接收到最优控制序列包，执行控制量
    Figure PCTCN2021109974-appb-100022
    若当前k时刻，第i个发电单元的执行装置接收到的最优控制序列包的时标小于k l，执行控制量
    Figure PCTCN2021109974-appb-100023
  8. 根据权利要求7所述的方法，其特征在于，还包括：
    若当前k时刻，第i个发电单元的执行装置接收的最优控制序列包
    Figure PCTCN2021109974-appb-100024
    为：
    Figure PCTCN2021109974-appb-100025
    其中，k r为当前k时刻第i个发电单元的执行装置接收到的最优控制序列包的时标，
    Figure PCTCN2021109974-appb-100026
    为第k r时刻预测的第k r+i时刻第i个逆变器控制量
    若当前k时刻，第i个发电单元的执行装置接收到的最优控制序列包的时标k r大于k l，执行控制量
    Figure PCTCN2021109974-appb-100027
  9. 根据权利要求1所述的方法，其特征在于，所述根据所述一次调频预估值，基于预先设立的逆变器有功功率模型，确定所述各发电单元的逆变器的各个时段的最优控制序列，还包括：
    根据所述一次调频预估值，基于预先设立的逆变器有功功率模型，确定所述各发电单元的一次调频功率分配结果，根据预设置信度函数调整所述一次调频功率分配结果，根据调整后的一次调频功率分配结果，确定所述各发电单元的逆变器的各个时段的最优控制序列。
  10. 根据权利要求9所述的方法，其特征在于，所述置信度函数的数学表达式为：
    Figure PCTCN2021109974-appb-100028
    其中，C i(k+1)为第k+1时刻的置信度函数，P i *(k)第i个发电单元在第k时刻的功率参考值，e i(k)＝|P i(k)-P i *(k)| δ为第i个发电单元在第k时刻的功率偏差函数，其定义如下：
    Figure PCTCN2021109974-appb-100029
    其中，δ为设定的阈值，防止因正常的功率波动而造成置信度的下降，P i(k)为第k时刻第i个发电单元的发电功率。
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