Method, device and equipment for determining grid-connected power capacity of new energy and storage medium

Abstract

The application discloses a new energy grid-connected power capacity determination method and device, equipment and a storage medium. The power resource data of the power system is obtained, the power resource data includes target unbalanced power and target new energy grid-connected power capacity, and a preset frequency response model is used to determine the frequency change rate and the minimum frequency drop of the power system under the preset constraint condition according to the power resource data, so as to determine the frequency stability boundary of the new power system with a high new energy proportion, and to determine the frequency stability of the power grid after experiencing active power disturbance. If the frequency change rate and the minimum frequency drop are within the preset frequency stability domain, the target new energy grid-connected power capacity is determined as the new energy grid-connected power capacity of the power system, so as to reasonably plan the access capacity of the new energy, adapt to the operation scene evaluation of the new power system under the new frequency stability constraint in the future, and improve the frequency stability of large-scale new energy grid connection.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular to a method, apparatus, equipment and storage medium for determining the grid-connected power capacity of new energy sources. Background Technology

[0002] With the increasing penetration rate of new energy sources such as wind and solar power, the power system is gradually transforming from traditional thermal power generation to new energy power generation. However, the decoupling characteristics of new energy power electronics and their maximum power point tracking (MPPT) mode reduce the inertia level and frequency regulation capability of the power system. When disturbances cause the frequency change rate to exceed the limit, the power system triggers distributed generation protection actions, ultimately leading to large-scale power outages. Furthermore, the commissioning of ultra-high-voltage, large-capacity inter-regional DC transmission has disrupted inter-regional inertia support and power response under disturbance events, severely deteriorating system frequency stability under large disturbances. Therefore, against the backdrop of the current rapid increase in new energy penetration, the contradiction between the weakened grid frequency stability and the actual demand for the safe and stable grid connection of large-scale new energy sources is becoming increasingly prominent. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and storage medium for determining the grid-connected power capacity of new energy sources, in order to solve the technical problem of poor frequency stability in large-scale grid-connected new energy sources.

[0004] To address the aforementioned technical problems, firstly, this application provides a method for determining the grid-connected power capacity of new energy sources, comprising: Acquire power resource data of the power system, including target imbalance power and target renewable energy grid-connected power capacity; Using a pre-defined frequency response model and based on power resource data, the rate of frequency change and the minimum frequency drop of the power system under pre-defined constraints are determined. If the rate of frequency change and the minimum frequency drop are within the preset frequency stability range, then the target renewable energy grid-connected power capacity is determined as the renewable energy grid-connected power capacity of the power system.

[0005] Preferably, using a pre-defined frequency response model and based on power resource data, the minimum frequency change rate and frequency drop of the power system under pre-defined constraints are determined, including: Power resource data is input into a pre-set simulation software. Based on the frequency response model and pre-set constraints, the frequency change rate and minimum frequency drop of the power system are determined. The functional expression of the frequency response model is as follows: ; in, Indicates frequency deviation. This represents the equivalent inertia of the power system. Represents the Laplace operator. This represents the sum of power increases in a power system under the influence of multiple power frequency regulation sub-models during frequency disturbances. This refers to the change in unbalanced power in a power system during frequency disturbances.

[0006] Preferably, the power frequency regulation sub-model includes a renewable energy frequency regulation response sub-model, which includes either a grid-connected frequency regulation response sub-model or a grid-connected frequency regulation response sub-model. The functional expression of the renewable energy frequency regulation response sub-model is as follows: ; in, This indicates the frequency modulation power under grid-type control. For the Laplace operator, This represents the virtual inertia control coefficient under mesh control. This represents the frequency measurement delay time constant. For frequency deviation, This represents the primary frequency control coefficient under mesh control. This indicates the frequency modulation power under network-type control. This represents the virtual inertia control coefficient under network-type control. This represents the primary frequency regulation control coefficient under network-type control. This indicates the upper limit of the primary frequency regulation power of new energy sources. This indicates the lower limit of the primary frequency regulation power of new energy sources.

[0007] Preferably, the power frequency regulation sub-model also includes a DC transmission frequency regulation response sub-model, the functional expression of which is: ; in, This indicates the frequency modulation power of DC transmission. This indicates the upward adjustment power of DC transmission. This indicates the downward adjustment power of DC transmission. Indicates frequency deviation. This represents the frequency positive reference value deviation. This indicates a negative reference value deviation in frequency.

[0008] Preferably, the power frequency regulation sub-model includes a frequency regulation response sub-model for energy storage power stations, and the functional expression of the frequency regulation response sub-model for energy storage power stations is as follows: ; in, This indicates the frequency regulation power of the energy storage power station. For the Laplace operator, This represents the time constant for energy storage power conversion. Sag control adjustment parameters Indicates the frequency reference value. This indicates frequency deviation.

[0009] As a preferred option, the power frequency regulation sub-model also includes the primary frequency regulation response sub-model of traditional synchronous generator units, which includes the primary frequency regulation response sub-model of hydropower synchronous generator units and the primary frequency regulation response sub-model of thermal power synchronous generator units.

[0010] Preferably, the preset constraints include a frequency change rate constraint and a minimum frequency drop constraint. The frequency change rate constraint is as follows: < ; The minimum frequency drop constraint is: > ; in, This represents the rate of change of frequency during frequency disturbances. This indicates the preset frequency change rate limit value. This indicates the lowest frequency drop. This indicates the preset low-frequency load shearing start-up setting value.

[0011] Secondly, this application provides a device for determining the grid-connected power capacity of new energy sources, comprising: The acquisition module is used to acquire power resource data of the power system, including target unbalanced power and target renewable energy grid-connected power capacity; The first determining module is used to determine the frequency change rate and the minimum frequency drop of the power system under preset constraints by using a preset frequency response model and power resource data. The second determining module is used to determine the target renewable energy grid-connected power capacity as the renewable energy grid-connected power capacity of the power system if the frequency change rate and the minimum frequency drop value are within the preset frequency stability range.

[0012] Thirdly, this application provides a computer device, including a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, it implements the method for determining the grid-connected power capacity of new energy sources as described in the first aspect.

[0013] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for determining the grid-connected power capacity of new energy sources as described in the first aspect.

[0014] Compared with the prior art, this application has the following beneficial effects: By acquiring power resource data from the power system, including target unbalanced power and target renewable energy grid-connected power capacity, and using a pre-defined frequency response model, the frequency change rate and minimum frequency drop of the power system under pre-defined constraints are determined based on the power resource data. This determines the frequency stability boundary of the new power system with a high proportion of renewable energy, thereby assessing the frequency stability of the grid after active power disturbances. If the frequency change rate and minimum frequency drop are within the pre-defined frequency stability domain, the target renewable energy grid-connected power capacity is determined as the renewable energy grid-connected power capacity of the power system. This allows for the rational planning of renewable energy access capacity, adapting to the evaluation of new power system operation scenarios under future new frequency stability constraints, and improving the frequency stability of large-scale renewable energy grid connection. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the method for determining the grid-connected power capacity of new energy sources according to an embodiment of this application; Figure 2 This is a schematic diagram of a frequency response model of a power system shown in an embodiment of this application; Figure 3 This is a schematic diagram of the constraint domain for the minimum frequency drop and the rate of frequency change, as shown in an embodiment of this application. Figure 4 This is a schematic diagram illustrating the preset frequency stability domain in an embodiment of this application; Figure 5 This is a schematic diagram of a grid-type frequency modulation response sub-model shown in an embodiment of this application; Figure 6 This is a schematic diagram of a network-type frequency modulation response sub-model shown in an embodiment of this application; Figure 7 This is a schematic diagram of the frequency response sub-model of a hydroelectric synchronous generator unit as shown in an embodiment of this application; Figure 8 This is a schematic diagram of the frequency response sub-model of a thermal power synchronous generator unit as shown in an embodiment of this application; Figure 9 This is a schematic diagram of a DC transmission frequency response sub-model shown in an embodiment of this application; Figure 10 This is a schematic diagram of the frequency response sub-model of an energy storage power station as shown in an embodiment of this application; Figure 11 This is a schematic diagram of a typical three-machine, nine-node system as shown in the embodiments of this application; Figure 12 This is a schematic diagram illustrating the results of the minimum frequency drop and the maximum frequency change rate in an embodiment of this application. Figure 13 This is a schematic diagram illustrating the frequency stability region in an embodiment of this application; Figure 14This is a schematic diagram illustrating simulation results in an embodiment of this application; Figure 15 This is a schematic diagram of the structure of the device for determining the grid-connected power capacity of new energy sources, as shown in the embodiments of this application. Figure 16 This is a schematic diagram of the structure of a computer device shown in an embodiment of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0017] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for determining the grid-connected power capacity of new energy sources, provided in an embodiment of this application. The method for determining the grid-connected power capacity of new energy sources in this application can be applied to computer equipment, including but not limited to smartphones, laptops, tablets, desktop computers, physical servers, and cloud servers. Figure 1 As shown, the method for determining the grid-connected power capacity of new energy sources in this embodiment includes steps S101 to S103, which are detailed below: Step 101: Obtain power resource data of the power system, including target unbalanced power and target renewable energy grid-connected power capacity.

[0018] In this step, the target unbalanced power is the unbalanced active power of the power system, and the grid-connected power capacity of new energy sources is the installed capacity of new energy sources connected to the power system.

[0019] Step S102: Using a preset frequency response model, based on the power resource data, determine the frequency change rate and minimum frequency drop of the power system under preset constraints. Optionally, the power resource data also includes the power system's electricity load data.

[0020] In this step, the ratio between the target unbalanced power and the total active power of the power system is calculated to obtain the unbalanced power ratio; the ratio between the target renewable energy grid-connected power capacity and the total grid-connected installed capacity of the power system is calculated to obtain the renewable energy proportion; based on the frequency response model, the minimum frequency change rate and frequency drop of the power system are estimated under the above unbalanced power ratio and renewable energy proportion.

[0021] The rate of change of frequency and the minimum frequency sag are indicators of frequency stability under frequency disturbances. Under a certain unbalanced power, the factors affecting the instantaneous rate of change of frequency during a disturbance are inertia and the magnitude of the unbalanced power. The factors affecting the minimum frequency sag include system inertia, generator primary frequency regulation, load frequency regulation, and the magnitude of the unbalanced power. Therefore, a frequency response model can be established based on the above factors to evaluate the aforementioned frequency stability indicators and the renewable energy carrying capacity of the power system.

[0022] The frequency response model comprises an inertia and load frequency response model consisting of sub-models for the primary frequency regulation response of traditional synchronous generator units, the frequency regulation response of DC transmission, the frequency regulation response of energy storage power stations, and the frequency regulation response of new energy sources. For example... Figure 2 The frequency response model of the power system shown is based on the classic SFR (System Frequency Response Model), taking into account the power system inertia and load frequency response, primary frequency regulation of synchronous units, and frequency regulation of new energy sources to construct the power system frequency response model. Compared with the classic SFR model, which is only applicable to power systems with pure thermal power units and relatively fast frequency regulation speed, the frequency response model of this application has stronger adaptability. Figure 2 middle, (i=1,2,3,4,5) represent the ratios of the grid-connected capacity of energy storage power stations, DC power transmission, new energy units, thermal power units, and hydropower units to their capacity base values, respectively. and This refers to the deviation between the system frequency and its reference value; Represents unbalanced power; (i=1,2,3,4,5) represent the frequency regulation power of energy storage power stations, DC power transmission, new energy units, thermal power units and hydropower units, respectively.

[0023] Optionally, the preset constraints include a frequency change rate constraint and a minimum frequency drop constraint, wherein the frequency change rate constraint is: < ; The minimum frequency drop constraint is as follows: > ; in, This represents the rate of change of frequency during frequency disturbances. This indicates the preset frequency change rate limit value. This indicates the lowest frequency drop. This indicates the preset low-frequency load shearing start-up setting value.

[0024] In this optional embodiment, the power grid currently primarily uses the maximum frequency deviation under disturbance as a constraint. When a power system experiences a power deficit, the frequency of a low-inertia system changes rapidly, resulting in a significant frequency drop. Once the frequency drops to the setting value of the low-frequency load shedding protection device, the load shedding action will trigger a large-scale power outage. To avoid triggering the low-frequency load shedding protection device due to frequency drop, the frequency at the lowest point must be higher than the frequency limit that triggers the low-frequency load shedding protection device. > .

[0025] In high-proportion power electronic power systems where inertia is gradually decreasing, a large RoCoF under disturbance can lead to pole slippage in synchronous generators, causing internal structural damage and grid disconnection of distributed generation, threatening the safe operation of the units. Therefore, it is considered as a system frequency constraint indicator in the future. At the instant of disturbance, inertia is at its minimum and RoCoF is at its maximum. At this point, there is no frequency deviation, i.e., no generator governor or load frequency regulation effect; the rate of frequency change depends only on the system inertia and the magnitude of the disturbance imbalance power. If the constraint is that the RoCoF after the disturbance does not exceed a certain threshold, then the RoCoF at the instant of the disturbance should be used as the benchmark. < .

[0026] Step S103: If the frequency change rate and the minimum frequency drop value are within the preset frequency stability range, then the target renewable energy grid-connected power capacity is determined as the renewable energy grid-connected power capacity of the power system.

[0027] In this step, such as Figure 3 As shown, based on the frequency response model, under given parameter conditions, the frequency stability evaluation index of the system under different imbalance power ratios and the proportion of new energy sources is estimated, and the results are as follows: Figure 3 The frequency drop minimum value shown in a and Figure 3 The frequency change rate shown in b, and further obtained as follows Figure 3 The constraint range for the minimum frequency drop shown in c is... Figure 3 The constraint domain for the rate of change of frequency, as shown by d. Figure 4 As shown, the constraint domains of the minimum frequency drop and the rate of frequency change are superimposed, and the intersection of the two is taken to obtain the preset frequency stability domain of the power system. The boundary of the preset frequency stability domain represents, on the one hand, the maximum proportion of new energy sources that the system can support under a given imbalance power condition, and on the other hand, the maximum imbalance power ratio that the system can withstand under a given proportion of new energy sources.

[0028] Optionally, it can be determined whether the planned scenario of the power grid to be evaluated (i.e., under the conditions of target unbalanced power and target renewable energy grid-connected power capacity) is within the frequency stable domain. If it is within the frequency stable domain, it is determined that the power grid to be evaluated does not have the risk of frequency exceeding the limit; if it is not within the frequency stable domain, the planned access capacity of renewable energy generation (i.e., the target renewable energy grid-connected power capacity) is reduced, and the evaluation is re-determined.

[0029] In some embodiments, Figure 1 Based on the illustrated embodiment, step S102 includes: The power resource data is input into a preset simulation software. Based on the frequency response model and the preset constraints, the frequency change rate and the minimum frequency drop of the power system are determined. The functional expression of the frequency response model is: ; in, Indicates frequency deviation. This represents the equivalent inertia of the power system. Represents the Laplace operator. This represents the sum of power increases in a power system under the influence of multiple power frequency regulation sub-models during frequency disturbances. This refers to the change in unbalanced power in a power system during frequency disturbances.

[0030] In this embodiment, inertia in a power system is defined as the ability to impede changes in the system's frequency state, and is generally quantified using the inertial time constant H. For a single generator, it is typically expressed as the ratio of its rotor kinetic energy at its rated angular velocity to its rated capacity.

[0031] ; in, , , , , , These are the generator inertial time constant, rotor kinetic energy at rated angular velocity, rated capacity, moment of inertia, and rated angular velocity, respectively.

[0032] For the entire power system, the equivalent inertial time constant is expressed as: ; , This is the system's equivalent inertial time constant; , Let i be the rotor kinetic energy and rated capacity of the i-th conventional synchronous generator at its rated angular velocity. This is the baseline value for system capacity; , It is the sum of the rotor kinetic energy of a traditional synchronous generator and the collection of traditional generators.

[0033] The preset simulation software can be Matlab Simulink. It can be understood that the above frequency response model is loaded into Matlab Simulink and the power resource data is input into the simulation software so that the simulation software can automatically call the frequency response model to process the power resource data and obtain the frequency change rate and the minimum frequency drop value.

[0034] Optionally, the new energy frequency regulation response sub-model is a grid-following frequency regulation response sub-model, and the function expression of the grid-following frequency regulation response sub-model is: ; in, This indicates the frequency modulation power under grid-type control. For the Laplace operator, This represents the virtual inertia control coefficient under mesh control. This represents the frequency measurement delay time constant. For frequency deviation, This represents the primary frequency control coefficient under mesh control. This indicates the upper limit of the primary frequency regulation power of new energy sources. This indicates the lower limit of the primary frequency regulation power of new energy sources.

[0035] In this optional embodiment, such as Figure 5 The grid-connected frequency regulation response sub-model shown is based on phase-locked loop synchronous grid connection of grid-connected new energy units. Its frequency regulation control adds a frequency regulation power command value to the active power command value, and realizes the active power output tracking the command value through converter control to complete the frequency regulation task.

[0036] Optionally, the frequency regulation response sub-model of the new energy source is a frequency regulation response sub-model under grid-type control, and the functional expression of the frequency regulation response sub-model under grid-type control is: ; in, This indicates the frequency modulation power under network-type control. For the Laplace operator, This represents the virtual inertia control coefficient under network-type control. This represents the primary frequency regulation control coefficient under network-type control. For frequency deviation, This indicates the upper limit of the primary frequency regulation power of new energy sources. This indicates the lower limit of the primary frequency regulation power of new energy sources.

[0037] In this optional embodiment, such as Figure 6 The grid-connected frequency regulation response sub-model shown is different from the grid-following new energy units. The grid-connected new energy units adopt a self-synchronous grid-connected operation mode and are not affected by the phase-locked loop. They can actively provide frequency support and realize the ability to actively connect to the grid.

[0038] In some embodiments, the primary frequency regulation response sub-model of the traditional synchronous generator unit includes a primary frequency regulation response sub-model of the hydropower synchronous generator unit, and the functional expression of the primary frequency regulation response sub-model of the hydropower synchronous generator unit is: ; in, This indicates the frequency regulation power of the hydroelectric synchronous generator unit. For the Laplace operator, This represents the time constant of the water hammer effect. This represents the time constant of the pressure regulating valve and the servo motor. Indicates servo gain. This represents the time constant of the main servo motor. Indicates the adjustment coefficient. Indicates the rate of decline during transient adjustment. Indicates the transient adjustment time constant. Indicates the frequency reference value. For frequency.

[0039] In this embodiment, as Figure 7 The illustrated sub-model of the primary frequency regulation response of a hydroelectric synchronous generator unit shows that the governor's basic function is to control speed and load. The most basic speed / load control function of the turbine is to control the guide vane position by feeding back the speed error signal. To ensure stable parallel operation of multiple units, the governor should have a drooping characteristic. The purpose of this drooping is to ensure a reasonable load sharing among the generator units. The typical drooping rate is set to about 5%, meaning that a 5% speed deviation causes a 100% change in guide vane position or power output. Furthermore, due to the inertia of water, the turbine has a unique response: changes in guide vane position cause initial turbine power changes opposite to the desired changes. To obtain stable control performance, temporary drooping rate compensation is required.

[0040] In some embodiments, the primary frequency regulation response sub-model of the traditional synchronous generator unit includes a primary frequency regulation response sub-model of a thermal power synchronous generator unit, and the functional expression of the primary frequency regulation response sub-model of the thermal power synchronous generator unit is: ; in, This indicates the frequency regulation power of a thermal power synchronous generator unit. This represents the reheater time constant. This indicates the proportion of power generated by the high-pressure cylinder in the total power of the steam turbine. For the Laplace operator, This represents the time constant of the steam volume effect. This represents the time constant of the speed relay. This represents the time constant of the servo motor. Indicates the frequency reference value. For frequency, This indicates the droop rate of a thermal power synchronous generator unit.

[0041] In this embodiment, as Figure 8 The primary frequency regulation response sub-model of the thermal power synchronous generator unit shown illustrates that the turbine's speed control system has three basic functions: normal speed / load control, overspeed control, and overspeed tripping. For studies considering the system's renewable energy carrying capacity under frequency stability constraints, only normal speed / load control needs to be considered. The standard approach is to use CV (CV valve) speed error proportional control. In the analysis, the nonlinearity of the CV valve and cam can be approximately ignored.

[0042] In some embodiments, the power frequency regulation sub-model further includes a DC transmission frequency regulation response sub-model, the functional expression of which is: ; in, This indicates the frequency modulation power of DC transmission. This indicates the upward adjustment power of DC transmission. This indicates the downward adjustment power of DC transmission. Indicates frequency deviation. This represents the frequency positive reference value deviation. This indicates a negative reference value deviation in frequency.

[0043] In this embodiment, as Figure 9 The DC transmission frequency response sub-model is shown. In AC / DC hybrid systems, DC frequency limit control (FLC) can effectively improve the system frequency stability.

[0044] Optionally, ; ; in, This represents the proportional gain of the PI controller. This represents the integral coefficient of the PI controller. This indicates the upper limit of the frequency modulation power of DC transmission. This indicates the lower limit of the frequency modulation power of DC transmission.

[0045] In some embodiments, the power frequency regulation sub-model includes an energy storage power station frequency regulation response sub-model, and the functional expression of the energy storage power station frequency regulation response sub-model is as follows: ; in, This indicates the frequency regulation power of the energy storage power station. For the Laplace operator, This represents the time constant for energy storage power conversion. Sag control adjustment parameters Indicates the frequency reference value. This indicates frequency deviation.

[0046] In this embodiment, as Figure 10 The frequency response sub-model of the energy storage power station is shown. For energy storage, the conventional control method is droop control. The droop control strategy is to control the energy storage to participate in primary frequency regulation by simulating the droop characteristics of the generator set.

[0047] As an example, not a limitation, a typical three-machine, nine-node system with wind power integration is used as a case study to evaluate the system's renewable energy carrying capacity under frequency stability constraints. The system model structure is attached below. Figure 11 As shown in the table below, the parameters of each synchronous generator unit and wind turbine unit in the system are as follows:

[0048] The total grid-connected installed capacity of the system is 1040 MVA (synchronous turbine grid-connected capacity + renewable energy grid-connected capacity), with a total load of 630 MW. The renewable energy units have no frequency regulation capability. The rated frequency is 50 Hz. Gen1 is a hydropower synchronous turbine, Gen2 and Gen3 are thermal power synchronous turbines, WF1, WF2, and WF3 represent three renewable energy wind farms, and Load1, Load2, and Load3 represent three loads. The installed capacities of Gen1, Gen2, and Gen3 account for 44%, 34%, and 22% of the total synchronous turbine installed capacity, respectively. The inertia of Gen1, Gen2, and Gen3 are 9.66 s, 4.165 s, and 2.765 s, respectively.

[0049] Take the total load as the power base value The system has no energy storage power station or DC power transmission, therefore = =0. Assume the proportion of new energy grid-connected capacity in the system is n, and the proportion of synchronous generator units is (1-n). However, since new energy units lack frequency regulation capabilities, therefore... =0, =56%×1040×(1-n) / , =44%×1040×(1-n) / The system's equivalent inertia is =1040×(1-n)×(44%×9.66 +34%×4.165+22%×2.765)(s). Load frequency regulation effect coefficient. The typical value is between 1 and 2, and the example system uses 1.5.

[0050] For frequency stability constraints, it is assumed that the low-frequency load shedding action start setting value is 49.0Hz, that is, the maximum allowable frequency drop depth is 1.0Hz, and the frequency change rate limit value is 1Hz / s.

[0051] Based on the frequency response model, the minimum frequency drop and the maximum rate of frequency change can be estimated. Figure 12 This represents the estimated results for these two frequency stability indices.

[0052] The shaded area in the overhead view represents the frequency-stable region that satisfies the frequency stability constraints. The frequency-stable region of the system can be obtained by taking the intersection of the frequency-stable regions of the two frequency stability indices, such as... Figure 13 As shown, observations reveal that the stable region of the frequency drop depth constraint is contained within the stable region of the maximum frequency change rate constraint. This indicates that the defined frequency drop depth constraint is more stringent than the maximum frequency change rate constraint, and the system frequency stability is primarily limited by the frequency drop depth constraint.

[0053] Based on the estimation results of the two frequency stability indices, and according to the method for determining the grid-connected power capacity of new energy sources considering frequency stability constraints, the new energy carrying capacity of the system under 10%, 20%, 30%, and 40% unbalanced power can be evaluated. The results are shown in the table below:

[0054] Taking into account both frequency stability constraints, the maximum new energy carrying capacity of the system under 10%, 20%, 30%, and 40% unbalanced power is 52%, 3%, 0%, and 0%, respectively. It can be seen that even without the grid connection of new energy generators, the simulation system can hardly withstand more than 20% unbalanced power.

[0055] In the PSCAD / EMTDC simulation environment, time-domain simulation of the example system was performed. Based on the comparison between the estimation results of the frequency response model and the time-domain simulation results, the effectiveness of the proposed method for determining the grid-connected power capacity of new energy considering frequency stability constraints was verified.

[0056] When the simulation system is running stably, at t=20s, the system suddenly increases the load by 63MW, that is, the system has a 10% unbalanced power. Observe the dynamic frequency response process of the system. Figure 14The figures show the system frequency waveforms estimated by time-domain simulation and frequency response model for example systems with wind power installed capacity of 30% (a), 50% (b), and 70% (c). Comparing the frequency response waveforms of the time-domain simulation and the frequency response model, the waveforms are very similar, indicating that the frequency response model can better reflect the frequency response characteristics of the system. Therefore, this method has good adaptability for assessing the renewable energy carrying capacity of the power grid.

[0057] It should be noted that: 1. Compared with the classic SFR model, this application introduces frequency response models for new energy power generation and hydropower generation, constructing an SFR model with stronger applicability to new power systems, suitable for power systems containing hydropower and new energy power generation; 2. Considering the frequency response characteristics of various specific power generation resources within the system to be evaluated, compared with existing new energy carrying capacity assessment methods that target the minimum inertia of the overall system, it is particularly suitable for screening extreme scenarios that can reflect the maximum new energy carrying capacity of the system to be evaluated; 3. For specific systems to be evaluated, compared with the full-state time-domain simulation method, the power grid new energy carrying capacity assessment method considering frequency stability constraints is simple and efficient, and is very suitable for applications in the engineering field.

[0058] To implement the method for determining the grid-connected power capacity of new energy sources corresponding to the above method embodiments, in order to achieve the corresponding functions and technical effects. See [link / reference]. Figure 15 , Figure 15 This diagram illustrates a structural block diagram of a device for determining the grid-connected power capacity of new energy sources according to an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The device for determining the grid-connected power capacity of new energy sources provided in this embodiment includes: The acquisition module 1501 is used to acquire power resource data of the power system, the power resource data including target unbalanced power and target renewable energy grid-connected power capacity; The first determining module 1502 is used to determine the frequency change rate and the minimum frequency drop of the power system under preset constraints based on the power resource data using a preset frequency response model. The second determining module 1503 is used to determine the target renewable energy grid-connected power capacity as the renewable energy grid-connected power capacity of the power system if the frequency change rate and the minimum frequency drop value are within a preset frequency stability range.

[0059] In some embodiments, the first determining module 1502 is specifically used for: The power resource data is input into a preset simulation software. Based on the frequency response model and the preset constraints, the frequency change rate and the minimum frequency drop of the power system are determined. The functional expression of the frequency response model is: ; in, Indicates frequency deviation. This represents the equivalent inertia of the power system. Represents the Laplace operator. This represents the sum of power increases in a power system under the influence of multiple power frequency regulation sub-models during frequency disturbances. This refers to the change in unbalanced power in a power system during frequency disturbances.

[0060] In some embodiments, the power frequency regulation sub-model includes the renewable energy frequency regulation response sub-model, which includes a grid-connected frequency regulation response sub-model or a grid-connected frequency regulation response sub-model. The functional expression of the renewable energy frequency regulation response sub-model is as follows: ; in, This indicates the frequency modulation power under grid-type control. For the Laplace operator, This represents the virtual inertia control coefficient under mesh control. This represents the frequency measurement delay time constant. For frequency deviation, This represents the primary frequency control coefficient under mesh control. This indicates the frequency modulation power under network-type control. This represents the virtual inertia control coefficient under network-type control. This represents the primary frequency regulation control coefficient under network-type control. This indicates the upper limit of the primary frequency regulation power of new energy sources. This indicates the lower limit of the primary frequency regulation power of new energy sources.

[0061] In some embodiments, the power frequency regulation sub-model further includes a DC transmission frequency regulation response sub-model, the functional expression of which is: ; in, This indicates the frequency modulation power of DC transmission. This indicates the upward adjustment power of DC transmission. This indicates the downward adjustment power of DC transmission. Indicates frequency deviation. This represents the frequency positive reference value deviation. This indicates a negative reference value deviation in frequency.

[0062] In some embodiments, the power frequency regulation sub-model includes an energy storage power station frequency regulation response sub-model, and the functional expression of the energy storage power station frequency regulation response sub-model is: ; in, This indicates the frequency regulation power of the energy storage power station. For the Laplace operator, This represents the time constant for energy storage power conversion. Sag control adjustment parameters Indicates the frequency reference value. This indicates frequency deviation.

[0063] In some embodiments, the power frequency regulation sub-model further includes a primary frequency regulation response sub-model for traditional synchronous generator units, which includes a primary frequency regulation response sub-model for hydropower synchronous generator units and a primary frequency regulation response sub-model for thermal power synchronous generator units.

[0064] In some embodiments, the preset constraints include a frequency change rate constraint and a minimum frequency drop constraint, wherein the frequency change rate constraint is: < ; The minimum frequency drop constraint is as follows: > ; in, This represents the rate of change of frequency during frequency disturbances. This indicates the preset frequency change rate limit value. This indicates the lowest frequency drop. This indicates the preset low-frequency load shearing start-up setting value.

[0065] The aforementioned device for determining the grid-connected power capacity of new energy sources can implement the method for determining the grid-connected power capacity of new energy sources described in the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application embodiment can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0066] Figure 16 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 16 As shown, the computer device 16 of this embodiment includes: at least one processor 160 ( Figure 16 (Only one is shown in the diagram) a processor, a memory 161, and a computer program 162 stored in the memory 161 and executable on the at least one processor 160, wherein the processor 160 executes the computer program 162 to implement the steps in any of the above method embodiments.

[0067] The computer device 16 may be a computing device such as a smartphone, tablet, desktop computer, or cloud server. This computer device may include, but is not limited to, a processor 160 and a memory 161. Those skilled in the art will understand that... Figure 16 The computer device 16 is merely an example and does not constitute a limitation on the computer device 16. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0068] The processor 160 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0069] In some embodiments, the memory 161 may be an internal storage unit of the computer device 16, such as a hard disk or memory of the computer device 16. In other embodiments, the memory 161 may be an external storage device of the computer device 16, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device 16. Furthermore, the memory 161 may include both internal and external storage units of the computer device 16. The memory 161 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 161 can also be used to temporarily store data that has been output or will be output.

[0070] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in any of the above method embodiments.

[0071] This application provides a computer program product that, when run on a computer device, enables the computer device to execute the steps described in the various method embodiments above.

[0072] In the several embodiments provided in this application, it will be understood that each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.

[0073] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.

Claims

1. A method for determining the grid-connected power capacity of new energy sources, characterized in that, include: Acquire power resource data of the power system, the power resource data including target imbalance power and target renewable energy grid-connected power capacity; Using a preset frequency response model, and based on the power resource data, the frequency change rate and minimum frequency drop of the power system under preset constraints are determined. If the frequency change rate and the minimum frequency drop are within the preset frequency stability range, then the target renewable energy grid-connected power capacity is determined as the renewable energy grid-connected power capacity of the power system. The step of using a preset frequency response model to determine the frequency change rate and minimum frequency drop of the power system under preset constraints, based on the power resource data, includes: The power resource data is input into a preset simulation software. Based on the frequency response model and the preset constraints, the frequency change rate and the minimum frequency drop of the power system are determined. The functional expression of the frequency response model is: ； in, Indicates frequency deviation. Represents the system's equivalent inertial time constant. Represents the Laplace operator. This represents the sum of power increases in a power system under the influence of multiple power frequency regulation sub-models during frequency disturbances. This represents the change in unbalanced power in the power system during frequency disturbances. The power frequency regulation sub-model includes a renewable energy frequency regulation response sub-model, which further includes a grid-connected frequency regulation response sub-model and a grid-connected frequency regulation response sub-model. The functional expression of the renewable energy frequency regulation response sub-model is as follows: ； in, This indicates the frequency modulation power under grid-type control. For the Laplace operator, This represents the virtual inertia control coefficient under mesh control. This represents the frequency measurement delay time constant. For frequency deviation, This represents the primary frequency control coefficient under mesh control. This indicates the frequency modulation power under network-type control. This represents the virtual inertia control coefficient under network-type control. This represents the primary frequency regulation control coefficient under network-type control. This indicates the upper limit of the primary frequency regulation power of new energy sources. This indicates the lower limit of the primary frequency regulation power of new energy sources; This indicates the frequency reference value.

2. The method for determining the grid-connected power capacity of new energy sources as described in claim 1, characterized in that, The power frequency regulation sub-model also includes a DC transmission frequency regulation response sub-model, the functional expression of which is: ； in, This indicates the frequency modulation power of DC transmission. This indicates the upward adjustment power of DC transmission. This indicates the downward adjustment power of DC transmission. Indicates frequency deviation. This represents the frequency positive reference value deviation. This indicates a negative reference value deviation in frequency.

3. The method for determining the grid-connected power capacity of new energy sources as described in claim 1, characterized in that, The power frequency regulation sub-model includes a frequency regulation response sub-model for energy storage power stations, and the functional expression of the frequency regulation response sub-model for energy storage power stations is as follows: ； in, This indicates the frequency regulation power of the energy storage power station. For the Laplace operator, This represents the time constant for energy storage power conversion. This indicates the droop control diastolic parameter. Indicates the frequency reference value. This indicates frequency deviation.

4. The method for determining the grid-connected power capacity of new energy sources as described in claim 1, characterized in that, The power frequency regulation sub-model also includes a primary frequency regulation response sub-model for traditional synchronous generator units, which includes a primary frequency regulation response sub-model for hydropower synchronous generator units and a primary frequency regulation response sub-model for thermal power synchronous generator units.

5. The method for determining the grid-connected power capacity of new energy sources as described in claim 1, characterized in that, The preset constraints include a frequency change rate constraint and a minimum frequency drop constraint. The frequency change rate constraint is as follows: < ; The minimum frequency drop constraint is as follows: > ; in, This represents the rate of change of frequency during frequency disturbances. This indicates the preset frequency change rate limit value. This indicates the lowest frequency drop. This indicates the preset low-frequency load shearing start-up setting value.

6. A device for determining the grid-connected power capacity of a new energy source, characterized in that, include: The acquisition module is used to acquire power resource data of the power system, including target unbalanced power and target renewable energy grid-connected power capacity; The first determining module is used to determine the frequency change rate and the minimum frequency drop of the power system under preset constraints based on the power resource data using a preset frequency response model. The second determining module is used to determine the target renewable energy grid-connected power capacity as the renewable energy grid-connected power capacity of the power system if the frequency change rate and the minimum frequency drop value are within a preset frequency stability range. The first determining module is specifically used for: The power resource data is input into a preset simulation software. Based on the frequency response model and the preset constraints, the frequency change rate and the minimum frequency drop of the power system are determined. The functional expression of the frequency response model is: ； in, Indicates frequency deviation. Represents the system's equivalent inertial time constant. Represents the Laplace operator. This represents the sum of power increases in a power system under the influence of multiple power frequency regulation sub-models during frequency disturbances. This represents the change in unbalanced power in the power system during frequency disturbances; the power frequency regulation sub-model includes the renewable energy frequency regulation response sub-model, which includes a grid-connected frequency regulation response sub-model and a grid-connected frequency regulation response sub-model. The functional expression of the renewable energy frequency regulation response sub-model is: ； in, This indicates the frequency modulation power under grid-type control. For the Laplace operator, This represents the virtual inertia control coefficient under mesh control. This represents the frequency measurement delay time constant. For frequency deviation, This represents the primary frequency control coefficient under mesh control. This indicates the frequency modulation power under network-type control. This represents the virtual inertia control coefficient under network-type control. This represents the primary frequency regulation control coefficient under network-type control. This indicates the upper limit of the primary frequency regulation power of new energy sources. This indicates the lower limit of the primary frequency regulation power of new energy sources; This indicates the frequency reference value.

7. A computer device, characterized in that, It includes a processor and a memory, the memory being used to store a computer program, which, when executed by the processor, implements the method for determining the grid-connected power capacity of new energy sources as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the method for determining the grid-connected power capacity of new energy sources as described in any one of claims 1 to 5.

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