Power system frequency risk quantification and coordination control method and system

Abstract

The invention discloses a power system frequency risk quantification and coordination control method and system, and belongs to the technical field of power systems and automation thereof. The method comprises the following steps: collecting power grid online operation data and system working conditions; frequency response curves of the system after different faults are obtained through fault simulation; dividing the frequency into different frequency bands according to a binary criterion formed by a plurality of frequency threshold values and corresponding duration time, and distributing an initial weight coefficient for each frequency band; obtaining a dynamic weight coefficient of each frequency band by combining the frequency change rate in the frequency band and a preset weight gain coefficient; based on the dynamic weight coefficient of each frequency band, performing time integration on the frequency deviation value in each frequency band to obtain a transient frequency risk assessment value; and judging the system stability and the risk level, and giving a corresponding frequency control strategy according to the risk level so as to coordinate and control resources. According to the method, the short-time power disturbance and the permanent power disturbance can be effectively distinguished, and a basis is provided for on-line and off-line safe and stable control.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power system and its automation, and particularly relates to a power system frequency risk quantification and coordinated control method and system. BACKGROUND

[0002] High proportion of new energy and large capacity DC access promote the existing power system to accelerate the transformation and upgrading to the new power system. However, for the new power system, a large number of synchronous machines are replaced, the inertia level of the system is continuously reduced, the dynamic active and reactive power support capability is reduced, and the frequency stability level of the system as a whole presents a downward trend. The transient frequency safety has become a bottleneck problem restricting the development of the new power system, and in recent years, a number of large frequency difference disturbance events have occurred at home and abroad.

[0003] For the strong coupling of large-scale new energy and multi-infeed DC in the receiving end power grid, in addition to the permanent power shortage faults such as DC blocking that traditionally attract attention, short-circuit faults may also cause low voltage ride through (LVRT) of new energy and flexible DC, and commutation failure of conventional DC, and the dynamic response process of new energy and DC is coupled, causing a large amount of short-term power impact on the system on the time scale of seconds, leading to rapid and large amplitude decrease of system frequency, and in serious cases, even triggering power grid low-frequency load shedding action and causing large-area power outage.

[0004] At present, scholars at home and abroad have carried out a series of researches on the evaluation method of power system frequency safety risk. Some literatures propose to divide the system frequency into different frequency bands based on multi-binary table criterion, and assign corresponding weight coefficients to each frequency band, so as to construct a transient frequency stability margin index, and realize quantitative evaluation of different frequency drop depths. Further research based on this idea further considers the transient characteristics of photovoltaic units, combines the system frequency safety operation requirements and photovoltaic protection configuration, and refines the weight assignment of each frequency band, and establishes a frequency stability margin index considering the frequency deviation at the initial disturbance.

[0005] However, the existing frequency safety risk quantification method still has certain limitations. On the one hand, the existing index cannot effectively distinguish the risk difference between the frequency event with long duration but small deviation amplitude and the frequency event with short duration but serious drop, and cannot accurately rank and classify the risk of the two types of frequency response curves with different forms. On the other hand, the existing researches are mostly focused on the construction of risk index itself, and there is still a lack of coordinated control scheme suitable for different levels of frequency risk, which makes it difficult to give differentiated emergency control strategies according to the risk quantification results, thereby limiting its application effect in actual system online decision-making and coordinated defense. SUMMARY

[0006] In view of the problems of the prior art, the present application provides a power system frequency risk quantification and coordinated control method and system, which realizes accurate assessment of the influence of frequency events with long duration but small offset and frequency events with short time but serious drop on the system frequency safety risk, and gives corresponding coordinated control strategies.

[0007] In order to achieve the above-mentioned purposes, the present application adopts the following technical solutions:

[0008] In a first aspect, a power system frequency risk quantification and coordinated control method comprises the following steps:

[0009] Collecting power grid online operation data and system operating conditions;

[0010] Obtaining frequency response curves of the system after different faults through fault simulation based on the power grid online operation data and system operating conditions;

[0011] According to the frequency response curves, dividing the frequency into different frequency bands according to a binary criterion composed of multiple frequency threshold values and corresponding durations, and assigning an initial weight coefficient to each frequency band; obtaining a dynamic weight coefficient of each frequency band according to the initial weight coefficient, the frequency change rate in the frequency band, and a preset weight gain coefficient; and obtaining a transient frequency risk evaluation value by time integration of the frequency deviation in each frequency band based on the dynamic weight coefficient of each frequency band;

[0012] According to the transient frequency risk evaluation value, judging the system stability and risk level, and giving a corresponding frequency control strategy to coordinate the control resources according to the risk level.

[0013] According to some embodiments of the first aspect, collecting power grid online operation data and system operating conditions comprises: obtaining corresponding power flow files and stability files; and obtaining generator, new energy, load models, and models and parameters of various elements related to frequency simulation from the power flow files and stability files.

[0014] According to some embodiments of the first aspect, obtaining frequency response curves of the system after different faults through fault simulation comprises: simulating different types of power grid faults, including at least one of DC blocking, new energy off-grid, and at least one of DC commutation failure, new energy or flexible into low wear; and obtaining system frequency response curves under permanent power disturbance and system frequency response curves under short-term power disturbance according to the frequency response of the system under the corresponding fault.

[0015] According to some embodiments of the first aspect, the dynamic weight coefficient of each frequency band is calculated according to the following formula:

[0016]

[0017] wherein, K iK is the initial weight coefficient corresponding to the i-th frequency band, K i ′ is the corresponding dynamic weight coefficient, α i K is the weight gain coefficient corresponding to the i-th frequency band, f(t) is the frequency of the system at time t, represents the frequency change rate.

[0018] According to some embodiments of the first aspect, the weight gain coefficient is set according to the following rules: the lower the frequency value of the frequency band, the greater the value of the set weight gain coefficient; the lower the system inertia level, the greater the value of the set weight gain coefficient.

[0019] According to some embodiments of the first aspect, the transient frequency risk assessment value is obtained by time integration of the frequency deviation amount in each frequency band, and the formula is as follows:

[0020]

[0021]

[0022] wherein F′ is the transient frequency risk assessment value, K1′ to K′ N are the weight gain coefficients corresponding to the first frequency band to the N-th frequency band, f N is the stable power of the system, t0 is the initial time after the system is disturbed, t i is the time when the frequency is lower than the frequency threshold value of the i-th frequency band during the frequency drop process, t i ′ is the time when the frequency is higher than the frequency threshold value of the i-th frequency band during the frequency rise process, t′0 is the time when the frequency returns to the stable power f N or the time when the simulation ends.

[0023] The second aspect is a power system frequency risk quantification and coordinated control system, comprising:

[0024] an information collection module for collecting power grid online operation data and system working conditions;

[0025] a frequency response curve determination module for obtaining the frequency response curves of the system after different faults through fault simulation based on the power grid online operation data and system working conditions;

[0026] a risk assessment index calculation module for dividing the frequency into different frequency bands according to a binary criterion composed of multiple frequency threshold values and corresponding durations, and assigning an initial weight coefficient to each frequency band; obtaining a dynamic weight coefficient for each frequency band according to the initial weight coefficient, the frequency change rate in the frequency band and a preset weight gain coefficient; and obtaining a transient frequency risk assessment value by time integration of the frequency deviation amount in each frequency band based on the dynamic weight coefficient of each frequency band;

[0027] The coordination strategy module is configured to determine system stability and risk level according to the transient frequency risk assessment value, and give corresponding frequency control strategies to coordinate control resources according to the risk level.

[0028] According to some embodiments of the second aspect, collecting power grid online operation data and system working conditions comprises: obtaining corresponding power flow files and stability files; and obtaining generator, new energy, load models and models and parameters of various elements related to frequency simulation from the power flow files and the stability files.

[0029] According to some embodiments of the second aspect, the frequency response curves of the system after different faults are obtained through fault simulation, comprising: simulating different types of power grid faults, including at least one of DC blocking and new energy off-grid, and at least one of DC commutation failure and new energy or flexible entering low penetration; and obtaining the system frequency response curve under permanent power disturbance and the system frequency response curve under short-term power disturbance according to the frequency response of the system under the corresponding fault.

[0030] According to some embodiments of the second aspect, the dynamic weight coefficient calculation formula of each frequency band is as follows:

[0031]

[0032] wherein, K i is the initial weight coefficient corresponding to the i-th frequency band, K i ′ is the corresponding dynamic weight coefficient, a i is the weight gain coefficient corresponding to the i-th frequency band, f(t) is the frequency of the system at time t, represents the frequency change rate.

[0033] According to some embodiments of the second aspect, the value of the weight gain coefficient is set according to the following rules: the lower the frequency value of the frequency band, the greater the value of the set weight gain coefficient; the lower the system inertia level, the greater the value of the set weight gain coefficient.

[0034] According to some embodiments of the second aspect, the transient frequency risk assessment value is obtained by time integration of the frequency deviation amount in each frequency band, and the formula is as follows:

[0035]

[0036] wherein, F′ is the transient frequency risk assessment value, K1′ to K′ N are the weight gain coefficients corresponding to the first frequency band to the N-th frequency band, f N is the system stable power, t0 is the initial time after the system is disturbed, t it is the time when the frequency is lower than the frequency threshold value of the i-th frequency band during the frequency reduction process i t' is the time when the frequency is higher than the frequency threshold value of the i-th frequency band during the frequency increase process, t'0 is the time when the frequency returns to the stable power f N or the time when the simulation ends.

[0037] In a third aspect, an electronic device includes one or more processors; memory; and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the programs, when executed by the processors, implement the power system frequency risk quantification and coordinated control method as described in the first aspect.

[0038] In a fourth aspect, a computer-readable storage medium has stored thereon a computer program, which, when executed by a processor, implements the power system frequency risk quantification and coordinated control method as described in the first aspect.

[0039] In a fifth aspect, a computer program product includes a computer program, which, when executed by a processor, implements the power system frequency risk quantification and coordinated control method as described in the first aspect.

[0040] Compared with the prior art, the present application has the following beneficial effects: The present application collects power grid operation mode data to determine the operating state and operating characteristics of the system units, loads, new energy units and DC; obtains the frequency response curve of the system after different faults based on online data simulation; determines the risk weight coefficient of each frequency band by introducing a weight gain coefficient considering the frequency change rate, to obtain a transient frequency risk assessment index, which is a risk index that can distinguish between a frequency response curve with long duration but small offset and a frequency response curve with severe drop in a short time; and based on the calculation result of the transient frequency risk assessment index, a frequency coordinated control strategy participated by energy storage is given. This method can effectively distinguish between short-term power disturbance and permanent power disturbance, and provides a reliable basis for online and offline safety and stability control of the system. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 A power system frequency risk quantification and coordinated control method flowchart is provided for the embodiments of the present application;

[0042] Figure 2 A frequency segmentation schematic diagram is provided for the embodiments of the present application;

[0043] Figure 3 A comparison diagram of frequency response curves after two fault disturbances is provided for the embodiments of the present application;

[0044] Figure 4A comparison chart of the frequency control effect of the method provided by the embodiment of the present application. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings.

[0046] The first embodiment of the present application provides a frequency risk quantification and coordinated control method considering frequency dynamic process, referring to Figure 1 , the method comprises the following steps:

[0047] S1, collecting power grid online operation data and system working conditions, including corresponding power flow files and stability files.

[0048] Collecting power grid online operation data and system working conditions, including corresponding power flow files and stability files; the power flow files should contain real-time states of network topology, generator output, load level and bus voltage, and the stability files need to contain detailed models of generators, loads, new energy units and parameters of control systems of the elements, to ensure simulation accuracy.

[0049] Obtaining corresponding power flow files and stability files; obtaining generator, new energy, load models and models and parameters of various elements related to frequency simulation from the power flow files and stability files.

[0050] S2, obtaining system frequency response curves with different characteristics through different fault simulations and analyzing them.

[0051] According to the power grid online operation data and system working conditions, simulating different types of power grid faults, including at least one of DC blocking and new energy off-grid, and at least one of DC commutation failure, new energy or flexible into low wear; obtaining system frequency response curve under permanent power disturbance and system frequency response curve under short-term power disturbance according to the frequency response of the system under corresponding fault.

[0052] Specifically, time domain simulation is performed by setting different fault types, disturbance positions and disturbance amplitudes, and the fault types include permanent power shortage disturbance and short-term power shock disturbance. Permanent power shortage disturbance includes but is not limited to AC line N-1 / N-2 opening, large generator set or DC transmission line blocking, etc., and short-term power shock disturbance includes but is not limited to DC system commutation failure, new energy unit off-grid due to transient overvoltage, etc. The frequency response characteristics of the system under the above faults are obtained through time domain simulation, and permanent power disturbance frequency response curve representing continuous power imbalance and short-term power disturbance frequency response curve representing short-term power mutation are generated accordingly.

[0053] The frequency response curve is a trajectory of system frequency variation with time, i.e. a function image of f(t). Its horizontal axis is time t, and its vertical axis is system real-time frequency f. The curve records the complete dynamic process from before the fault occurs, the fault occurs, the frequency drops to the lowest point, and then recovers to the steady state.

[0054] S3, constructing a transient frequency risk assessment index of frequency change rate.

[0055] According to the frequency response curve, a binary table criterion containing frequency threshold value and maximum offset duration information is used to judge the acceptability of transient frequency offset. In order to quantify the degree of frequency offset, considering the influence of different frequency drop depths on frequency stability, the frequency is divided into different frequency bands based on multiple binary table criteria (f cr.1 , T cr.1 ), (f cr.2 , T cr.2 )... (f cr.n , T cr.n ). f cr.i , T cr.i are the frequency threshold value and the maximum offset duration of the i-th frequency band respectively. The frequency threshold value is a predefined frequency threshold value. The maximum offset duration is the duration of the frequency response curve in the specified frequency band. Different initial weight coefficients are assigned to each frequency band, and weighted integration is performed according to different initial weight coefficients, so that the initial transient frequency risk assessment index F is obtained as follows:

[0056]

[0057] Where t0 is the initial time after the system is disturbed; t i is the time when the frequency drops below f cr.i ; t i ' is the time when the frequency rises above f cr.i ; t'0 is the time when the frequency recovers to the system stable frequency f N or the simulation ends; K i is the weight coefficient corresponding to the i-th frequency band when the frequency falls in the frequency band; f(t) represents the frequency of the system at time t. N is the number of frequency bands, and n is the number of frequency bands minus one.

[0058] Referring to Figure 2 , taking four frequencies as an example, f N is the system stable frequency, f cr.1 is the threshold value of the first frequency band, i.e. f N to f cr.1 forms the first frequency band, and the time from t0 to t1 in the frequency drop process and the time from t'1 to t'0 in the frequency rise process are the durations of the frequency response curve in the first frequency band, corresponding to Tcr.1 In this example, the initial transient frequency risk assessment index F is calculated as follows:

[0059]

[0060]

[0061] The transient stability assessment index F is used to characterize the cumulative effect of frequency. The calculation method of the initial transient frequency risk assessment index F has certain limitations, i.e., there is a case in which the risk index calculation results of a frequency response curve with long duration and small offset and a frequency response curve with severe drop in a short time are the same. Therefore, a weight gain coefficient a considering the rate of change of frequency is introduced to improve the initial weight coefficient, and the improved weight coefficient K i ′ is calculated as follows:

[0062]

[0063] where a i is the weight gain coefficient corresponding to the frequency falling in the ith frequency band. The improved weight coefficient K i ′ is also called real-time weight coefficient.

[0064] Then the corresponding transient frequency risk assessment index F′ is calculated as follows:

[0065]

[0066] According to the embodiments of the present application, the determination method of the initial weight coefficient and the weight gain coefficient under each frequency band is as follows.

[0067] According to the requirements of the power system safety and stability operation guide, the stable frequency f NFor 50Hz, the frequency of the system when it is stable, the duration of the system frequency below 49.5Hz in emergency situation should not exceed 10min, considering that the first action value of low frequency load shedding is 49Hz and the delay is 0.2s, and the photovoltaic unit starts instantaneous tripping protection when the system frequency drops to 48Hz. Therefore, according to the requirements of frequency stability safety and the frequency control measures in the actual power grid, the transient frequency response curve can be divided into four intervals of 49.5-50Hz, 49-49.5Hz, 48-49Hz and below 48Hz, and initial weight coefficients K0, K1, K2 and K3 are respectively assigned. When calculating the initial weight coefficient of an interval (frequency band), the frequency difference is obtained by comparing the stable frequency of the system with the upper limit of the interval, and the product of the frequency difference, the initial weight coefficient of the interval and the maximum duration is taken as the transient stability evaluation index. The initial weight coefficient is inversely calculated by taking the transient stability evaluation index F as 1 when the system reaches critical stability. Taking the frequency band of 49-49.5Hz as an example: when the system frequency is lower than 49.5Hz, the duration of this period cannot exceed 10min, and the frequency response is lower than 49.5Hz for 600s, which is in a critical stable state, at this time, the transient frequency stability evaluation index F=1, then K1x(50-49.5)x600=1, K1=0.0033 is obtained.

[0068] The frequency band division and initial weight coefficient are calculated by the same method as follows:

[0069]

[0070] The value of the weight gain coefficient is set according to the following rules: the lower the frequency value of the frequency band, the greater the value of the set weight gain coefficient; the lower the system inertia level, the greater the value of the set weight gain coefficient.

[0071] The weight gain coefficient is represented by α, which reflects the influence of the frequency change rate in different frequency bands on the system stability risk during the frequency drop process, and the value of α also needs to be referred to the actual operation state of the power grid, such as the smaller the inertia of the power grid, the more violent the frequency change, and the greater the threat to the system stability, so the value of α needs to be appropriately increased to evaluate the frequency risk more finely. The specific value of the weight gain coefficient can be set based on the frequency duration requirement in the power system safety and stability operation guide and the low frequency load shedding and new energy unit protection setting value.

[0072] In the embodiment of the application, the weight gain coefficient α is listed as the following formula:

[0073]

[0074] S4, a coordinated control strategy based on the transient frequency risk evaluation index is formulated.

[0075] According to the calculation result of the transient frequency risk assessment index F', the size of the system risk is judged, if the larger the calculated value is, the greater the risk of the system after the short-time disturbance power is, according to the different calculated values, different flexible control resources are determined to be used for frequency emergency control. For example, generator tripping, DC modulation and other stability control measures are taken to deal with the problem of frequency rise, and modulation DC, load shedding, pump cutting and other stability control measures are taken to deal with the problem of frequency drop.

[0076] In order to verify the effect of the method, verification experiments are carried out in the embodiment. By setting the experimental parameters, building the experimental environment, and comparing the transient frequency risk assessment indexes in two cases.

[0077] The verification experiment is modified from the IEEE 10-machine 39-node simulation system. Among them, there are 6 synchronous machines, the inertia time constant H = 3.4s; there are 3 wind farms, the rated power of a single machine is 1.5MW, the number of machines is 100, 50 and 50 respectively, the output is 100MW, 50MW and 50MW respectively, the specified power control is used during low voltage period, the power control value is 0, the initial value is restored according to the actual power control during low voltage period, and the rated active power is restored according to 45%; there is one energy storage power station, the rated power of a single machine is 0.5MW, a total of 400 machines, the output is 50MW when the power grid is stably running; there is one DC receiving node, the receiving power is 150MW, the total load of the system is 1000MW, the constant power model is used, and the frequency factor L = 2.0.

[0078] Two different types of disturbances occur respectively. Disturbance 1: DC power emergency drop, power shortage 15MW; Disturbance 2: set short-circuit fault, DC commutation failure, new energy enters low voltage ride through, short-time disturbance power 400MW.

[0079] Figure 3 The two frequency response curves of the system after the disturbance fault occur, the one with large frequency deviation (green) is the short-circuit fault superimposed with DC commutation failure, and the one with small frequency deviation (blue) is the DC power emergency drop. The transient frequency risk assessment index F of the two is obtained by the initial transient frequency risk assessment index calculation method, which is 0.0127, and the risk size cannot be judged, but through the frequency response curve, it can be seen that the low frequency phenomenon caused by short-time power disturbance has the risk of triggering the low frequency load shedding action of the power grid, and in serious cases, it will also cause high and low frequency cascading reaction, which is more dangerous to the power grid. By improving the transient frequency risk assessment index calculation method, the transient frequency risk assessment index F' of the low frequency phenomenon caused by short-time power disturbance is 0.0147, and the transient frequency risk assessment index F' of the low frequency phenomenon caused by permanent power disturbance is 0.0139. It can be seen that by introducing the weight gain coefficient, the risk size of short-time power disturbance and permanent power disturbance to the system can be better distinguished.

[0080] Figure 4 For the introduction of the electrochemical energy storage with fast response speed, power bidirectional adjustable and other advantages to participate in frequency emergency control, the frequency response curves of two types of disturbances are shown in the figure, in which the frequency deviation of the green color is short-circuit fault superimposed on direct current commutation failure, and the frequency deviation of the blue color is direct current power emergency drop. It can be seen that after the energy storage participates in the frequency emergency control, the situation that the system frequency triggers the low frequency load shedding threshold after the short-term power disturbance can be effectively avoided. Compared with the frequency response curve without the participation of the energy storage in the emergency control, the minimum point of the system frequency is effectively improved.

[0081] Based on the same technical concept as the method embodiment, another embodiment of the present application also provides a power system frequency risk quantification and coordinated control system, comprising:

[0082] an information collection module, configured to collect power grid online operation data and system working conditions;

[0083] a frequency response curve determination module, configured to obtain frequency response curves of the system after different faults through fault simulation based on the power grid online operation data and the system working conditions;

[0084] a risk assessment index calculation module, configured to divide the frequency into different frequency bands according to a binary criterion composed of multiple frequency threshold values and corresponding durations, and assign an initial weight coefficient to each frequency band; obtain a dynamic weight coefficient of each frequency band according to the initial weight coefficient, the frequency change rate in the frequency band and a preset weight gain coefficient; and obtain a transient frequency risk assessment value by time integration of the frequency deviation amount in each frequency band based on the dynamic weight coefficient of each frequency band;

[0085] a coordinated control strategy module, configured to judge the system stability and risk level according to the transient frequency risk assessment value, and give a corresponding frequency control strategy to coordinate the control resources according to the risk level.

[0086] In the present embodiment, the information collection module collects the power grid online operation data and the system working conditions, including: obtaining corresponding power flow files and stability files; obtaining the generator, new energy, load model and the model and parameters of various elements related to frequency simulation from the power flow files and stability files.

[0087] The frequency response curve determination module obtains the frequency response curves of the system after different faults through fault simulation, including: simulating different types of power grid faults, including at least one of direct current blocking and new energy off-grid, and at least one of direct current commutation failure and new energy or flexible direct current into low penetration; obtaining the system frequency response curve under permanent power disturbance and the system frequency response curve under short-term power disturbance according to the frequency response of the system under the corresponding fault.

[0088] In the risk assessment index calculation module, the dynamic weight coefficient calculation formula of each frequency band is as follows:

[0089]

[0090] K i'= K i * (1 + a i * f (t) ), i = 1, 2, …, N i K i'= K i * (1 + a i * f (t) ), i = 1, 2, …, N i K i'= K i * (1 + a i * f (t) ), i = 1, 2, …, N i K i'= K i * (1 + a i * f (t) ), i = 1, 2, …, N f (t) is the frequency of the system at time t, and f (t) represents the frequency change rate.

[0091] The weight gain coefficient is set according to the following rules: the lower the frequency value of the frequency band, the greater the value of the set weight gain coefficient; the lower the system inertia level, the greater the value of the set weight gain coefficient.

[0092] The transient frequency risk assessment value is obtained by time integration of the frequency deviation amount in each frequency band, and the formula is as follows:

[0093]

[0094] K i'= K i * (1 + a i * f (t) ), i = 1, 2, …, N N K i'= K i * (1 + a i * f (t) ), i = 1, 2, …, N N f is the stable power of the system, t0 is the initial time after the system is disturbed, t i t i is the time when the frequency is lower than the frequency threshold of the i-th frequency band in the frequency drop process, t i t i'is the time when the frequency is higher than the frequency threshold of the i-th frequency band in the frequency rise process, t'0 is the time when the frequency recovers to the stable power f N or the simulation ends.

[0095] It should be understood that the power system frequency risk quantification and coordinated control system in the embodiment can realize all the technical solutions in the above method embodiment, and the functions of each functional module can be realized according to the method in the above method embodiment, and the specific implementation process can be referred to the related description in the above method embodiment, which will not be described here.

[0096] Another embodiment of the application also provides an electronic device, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the program is executed by the processor to realize the power system frequency risk quantification and coordinated control method as described above.

[0097] Another embodiment of the present application also provides a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the power system frequency risk quantification and coordinated control method as described above.

[0098] Those skilled in the art will appreciate that embodiments of the present application can be provided as methods, apparatus (systems), electronic devices or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage devices, etc.) embodying computer usable program code.

[0099] The present application is described with reference to flowcharts of methods according to embodiments of the present application. It will be understood that each block of the flowcharts, and combinations of blocks in the flowcharts, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing device, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowcharts block or blocks. Figure 1 The processor(s) involved in each embodiment can be a general purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic, a quantum computing based data processing logic, an artificial intelligence processor, etc., without limitation.

[0100] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowcharts block or blocks. Figure 1 The function specified in the flowcharts block or blocks.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in the flowcharts block or blocks. Figure 1 The function specified in the flowcharts block or blocks.

Claims

1. A method for quantifying and coordinating the control of frequency risk in a power system, characterized in that, The method includes: Collect online power grid operation data and system operating conditions; Based on online power grid operation data and system operating conditions, frequency response curves of the system after different faults are obtained through fault simulation. Based on the frequency response curve, and according to the binary criterion consisting of multiple frequency thresholds and corresponding durations, the frequency is divided into different frequency bands, and an initial weighting coefficient is assigned to each frequency band. Based on the initial weighting coefficient, the frequency change rate within the frequency band, and the preset weighting gain coefficient, the dynamic weighting coefficient of each frequency band is obtained. Based on the dynamic weighting coefficient of each frequency band, the transient frequency risk assessment value is obtained by integrating the frequency deviation within each frequency band over time. Based on the transient frequency risk assessment value, the system stability and risk level are determined, and corresponding frequency control strategies are given according to the risk level to coordinate control resources.

2. The method according to claim 1, characterized in that, Collect online power grid operation data and system operating conditions, including: obtaining the corresponding power flow files and stability files; and obtaining generator, new energy, load models, and various component models and parameters related to frequency simulation from the power flow files and stability files.

3. The method according to claim 1, characterized in that, The frequency response curves of the system after different faults are obtained through fault simulation, including: simulating different types of grid faults, including at least one of DC blocking, new energy disconnection, and at least one of DC commutation failure, new energy or flexible DC entering the low-voltage circuit; and obtaining the system frequency response curves under permanent power disturbance and short-term power disturbance based on the system frequency response under the corresponding fault.

4. The method according to claim 1, characterized in that, The formula for calculating the dynamic weighting coefficient for each frequency band is as follows: Among them, K i K represents the initial weighting coefficients corresponding to frequencies falling in the i-th frequency band. i ′ For the corresponding dynamic weighting coefficient, α i Let f(t) be the weighting gain coefficient corresponding to the frequency falling in the i-th frequency band, and f(t) be the system frequency at time t. It represents the rate of change of frequency.

5. The method according to claim 1, characterized in that, The weight gain coefficient is set according to the following rules: the lower the frequency band, the larger the weight gain coefficient is set; the lower the system inertia level, the larger the weight gain coefficient is set.

6. The method according to claim 1, characterized in that, The transient frequency risk assessment value is obtained by integrating the frequency deviation within each frequency band over time, as shown in the following formula: Among them, F ′ K1 is the transient frequency risk assessment value. ′ To K ′ N f represents the weighting gain coefficients corresponding to the first to the Nth frequency bands. N The system's stable power is given by t0, which is the initial time after the system experiences a disturbance. i t represents the moment when the frequency drops below the frequency threshold of the i-th frequency band during the frequency decline process. i ′ t represents the moment when the frequency rises above the frequency threshold of the i-th frequency band during the frequency increase process. ′ 0 represents the frequency recovering to the system's stable power f. N Or the moment the simulation ends.

7. A power system frequency risk quantification and coordination control system, characterized in that, include: The information collection module is used to collect online power grid operation data and system operating conditions; The frequency response curve determination module is used to obtain the frequency response curves of the system after different faults through fault simulation based on the online operation data of the power grid and the system operating conditions. The risk assessment index calculation module is used to divide the frequency into different frequency bands based on the frequency response curve and the binary criteria consisting of multiple frequency threshold values ​​and corresponding durations, and to assign an initial weight coefficient to each frequency band; based on the initial weight coefficient, the frequency change rate within the frequency band, and the preset weight gain coefficient, the dynamic weight coefficient of each frequency band is obtained; based on the dynamic weight coefficient of each frequency band, the transient frequency risk assessment value is obtained by integrating the frequency deviation within each frequency band over time. The collaborative control strategy module is used to determine the system stability and risk level based on the transient frequency risk assessment value, and to provide corresponding frequency control strategies to coordinate control resources according to the risk level.

8. The system according to claim 7, characterized in that, Collect online power grid operation data and system operating conditions, including: obtaining the corresponding power flow files and stability files; and obtaining generator, new energy, load models, and various component models and parameters related to frequency simulation from the power flow files and stability files.

9. The system according to claim 7, characterized in that, The frequency response curves of the system after different faults are obtained through fault simulation, including: simulating different types of grid faults, including at least one of DC blocking, new energy disconnection, and at least one of DC commutation failure, new energy or flexible DC entering the low-voltage circuit; and obtaining the system frequency response curves under permanent power disturbance and short-term power disturbance based on the system frequency response under the corresponding fault.

10. The system according to claim 7, characterized in that, The formula for calculating the dynamic weighting coefficient for each frequency band is as follows: Among them, K i K represents the initial weighting coefficients corresponding to frequencies falling in the i-th frequency band. i ′ For the corresponding dynamic weighting coefficient, α i Let f(t) be the weighting gain coefficient corresponding to the frequency falling in the i-th frequency band, and f(t) be the system frequency at time t. It represents the rate of change of frequency.

11. The system according to claim 7, characterized in that, The weight gain coefficient is set according to the following rules: the lower the frequency band, the larger the weight gain coefficient is set; the lower the system inertia level, the larger the weight gain coefficient is set.

12. The system according to claim 7, characterized in that, The transient frequency risk assessment value is obtained by integrating the frequency deviation within each frequency band over time, as shown in the following formula: Among them, F ′ K1 is the transient frequency risk assessment value. ′ To K ′ N f represents the weighting gain coefficients corresponding to the first to the Nth frequency bands. N The system's stable power is given by t0, which is the initial time after the system experiences a disturbance. i t represents the moment when the frequency drops below the frequency threshold of the i-th frequency band during the frequency decline process. i ′ t represents the moment when the frequency rises above the frequency threshold of the i-th frequency band during the frequency increase process. ′ 0 represents the frequency recovering to the system's stable power f. N Or the moment the simulation ends.

13. An electronic device, characterized in that, The device includes: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, wherein when the programs are executed by the processors, they implement the power system frequency risk quantification and coordinated control method as described in any one of claims 1 to 6.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the power system frequency risk quantification and coordinated control method as described in any one of claims 1 to 6.

15. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the power system frequency risk quantification and coordinated control method as described in any one of claims 1 to 6.

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