Optimization method and system for frequency stability of virtual power plant and computer equipment

Abstract

The invention discloses a virtual power plant frequency stability optimization method and system and computer equipment, and relates to the technical field of power system control, and the method comprises the steps: obtaining a stability parameter of a virtual power plant, and carrying out the per-unit processing of the stability parameter, and obtaining per-unit data; calculating a frequency stability evaluation value of the virtual power plant, if the stability evaluation value is smaller than a preset threshold value, performing iterative setting on the stability parameter through the setting factor to obtain per unit data after iterative setting, and readjusting the setting factor according to the number of iterations; recalculating according to the per-unit data after iteration setting to obtain a frequency stability evaluation update value of the virtual power plant until the frequency stability update value is not less than a preset threshold value; and calculating the frequency stability improvement rate of the virtual power plant through the per-unit data before the iterative setting and the per-unit data after the iterative setting. According to the method, the frequency stability optimization of the virtual power plant can be accurately and quickly realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system control, and particularly relates to a virtual power plant frequency stability optimization method, system and computer equipment. BACKGROUND

[0002] At present, with the increasing proportion of renewable energy in the power system, the virtual power plant composed of distributed wind power, photovoltaic and energy storage systems has become a key means to improve the flexibility and stability of the power grid. The virtual power plant aggregates dispersed resources to simulate the inertia response and frequency modulation characteristics of traditional synchronous generators to make up for the decline in stability of the power grid due to the reduction of synchronous units.

[0003] However, when optimizing the frequency control of the virtual power plant, due to the fact that the virtual power plant contains many heterogeneous devices such as wind turbines, photovoltaic inverters and energy storage converters, the physical units and orders of magnitude of the key parameters such as output power, inertia coefficient and droop coefficient (such as MW, kW, S, Hz, etc.) are greatly different, which makes it difficult for the control algorithm to uniformly model and collaboratively calculate these parameters. Because of the difference in device capacity and operating state, the traditional control strategy based on actual value is easy to cause uneven distribution of adjustment tasks among different capacity devices, and cannot realize the fair and efficient use of resources. The method in the related art relies on isolated and non-collaborative control instructions when dealing with power surges, and the optimization process is complex and inefficient, and lacks a unified and quantitative index to evaluate the state and optimization effect of frequency stability in real time, which causes the core indicators such as system frequency change rate and frequency minimum point to easily exceed the safety threshold in the scenario with high penetration of new energy.

[0004] Therefore, there is a need for a virtual power plant frequency stability optimization method that can uniformly process heterogeneous parameters and achieve rapid collaborative optimization. SUMMARY

[0005] The purpose of the present application is to provide a virtual power plant frequency stability optimization method, system and computer equipment. The virtual power plant frequency stability optimization can be accurately and quickly realized.

[0006] To achieve the above purpose, the present application provides the following solutions: In a first aspect, the application provides a virtual power plant frequency stability optimization method, comprising: obtaining stability parameters of the virtual power plant, and performing dimensionless processing on the stability parameters to obtain dimensionless data; calculating a frequency stability evaluation value of the virtual power plant according to the dimensionless data, and comparing the stability evaluation value with a preset threshold; if the stability evaluation value is less than the preset threshold, iteratively adjusting the stability parameters by an adjustment factor to obtain iteratively adjusted dimensionless data, and readjusting the adjustment factor according to the number of iterations; recalculating the frequency stability evaluation update value of the virtual power plant according to the iteratively adjusted dimensionless data, until the frequency stability update value is not less than the preset threshold; and calculating a frequency stability improvement rate of the virtual power plant by using the dimensionless data before iteration and the dimensionless data after iteration.

[0007] Exemplarily, the stability parameters include distributed wind farm output power, distributed photovoltaic power station output power, distributed energy storage station charge and discharge power, distributed energy storage station state of charge, virtual power plant grid connection point actual frequency, virtual power plant electrical load power, distributed wind farm virtual inertia coefficient of wind turbine, distributed photovoltaic droop coefficient, and equivalent inertia coefficient of virtual power plant.

[0008] Exemplarily, the dimensionless data obtained by performing dimensionless processing on the stability parameters includes: calculating a ratio of the distributed wind farm output power to the rated power of the wind turbine as the dimensionless value of the distributed wind farm output power; calculating a ratio of the distributed photovoltaic power station output power to the rated output power of the inverter as the dimensionless value of the distributed photovoltaic power station output power; calculating a ratio of the distributed energy storage station charge and discharge power to the rated charge and discharge power of the energy storage converter as the dimensionless value of the distributed energy storage station charge and discharge power; calculating a ratio of the distributed energy storage station state of charge to the rated value as the dimensionless value of the distributed energy storage station state of charge; calculating a ratio between a deviation of the virtual power plant grid connection point actual frequency from the rated frequency and the rated frequency as the dimensionless value of the virtual power plant grid connection point actual frequency; calculating a ratio between the virtual power plant electrical load power and the maximum electrical load as the dimensionless value of the virtual power plant electrical load power; calculating a ratio of the distributed wind farm virtual inertia coefficient of wind turbine to the rated inertia coefficient as the dimensionless value of the distributed wind farm virtual inertia coefficient of wind turbine; calculating a ratio of the distributed photovoltaic droop coefficient multiplied by the rated output power of the inverter to the rated frequency as the dimensionless value of the distributed photovoltaic droop coefficient; and calculating a ratio of the equivalent inertia coefficient of virtual power plant to the rated inertia coefficient as the dimensionless value of the equivalent inertia coefficient of virtual power plant.

[0009] For example, calculating the frequency stability assessment value of the virtual power plant based on the per-unit data includes: confirming the number of wind turbines, energy storage converters, and photovoltaic inverters in the virtual power plant, and calculating the frequency stability assessment value in combination with the per-unit data, as shown in the following formula: in, J The number of wind turbines in the virtual power plant. L The number of energy storage converters in the virtual power plant. K The number of photovoltaic inverters in the virtual power plant. For distributed wind farms j The per-unit value of the output power of a typhoon generator. For the first distributed photovoltaic power station k The per-unit value of the inverter's output power. For distributed energy storage stations l The per-unit value of the charging and discharging power of the Taiwan energy storage converter. For distributed energy storage stations l The per-unit value of the state of charge of Taiwan's energy storage. This represents the per-unit value of the frequency at the virtual power plant's grid connection point. This represents the per-unit value of the virtual power plant's electrical load power. For distributed wind farms j The per-unit value of the virtual inertia coefficient of a typhoon generator. For the first distributed photovoltaic power station k The per-unit value of the droop factor of the inverter; This represents the per-unit value of the equivalent inertia coefficient of the virtual power plant. M This represents the frequency stability assessment value.

[0010] For example, the stability parameter is iteratively tuned using a tuning factor to obtain the iteratively tuned per-unit data, as shown in the following formula: in, For the first distributed wind farm after adjustment j The per-unit value of the output power of a typhoon generator. For the first distributed photovoltaic power station after adjustment k The per-unit value of the inverter's output power. For the first distributed wind farm after adjustment j The per-unit value of the virtual inertia coefficient of a typhoon generator. For the first distributed photovoltaic power station after adjustment k The per-unit value of the droop factor for the Taiwanese inverter. For the first distributed energy storage station after adjustment l The per-unit value of the charging and discharging power of the energy storage converter. the tuning factor.

[0011] Exemplarily, the re-adjusting the tuning factor according to the iteration number comprises: confirming an initial tuning factor, an iteration step and a current iteration number; confirming an adjustment value according to the iteration step and the current iteration number; re-adjusting the tuning factor according to the adjustment value and the initial tuning factor, as shown in the following formula: wherein, the tuning factor, represents the initial tuning factor, x represents the iteration number, and d represents the iteration step, wherein the current iteration number is not greater than a maximum iteration number, and the initial tuning factor and the iteration step are preset constants.

[0012] Exemplarily, the frequency stability improvement rate of the virtual power plant is calculated by the reference data before iteration tuning and the reference data after iteration tuning, comprising: calculating a previous stability index by the reference data before iteration tuning; calculating a current stability index by the reference data after iteration tuning; The frequency stability improvement rate is calculated according to the current stability index and the previous stability index, as shown in the following formula: wherein, represents the frequency stability improvement rate, represents the current stability index, represents the previous stability index.

[0013] Exemplarily, the previous stability index is calculated by the reference data before iteration tuning, as shown in the following formula: The current stability index is calculated by the reference data after iteration tuning, as shown in the following formula: wherein, respectively represent the contribution weight of the wind turbine inertia, the photovoltaic droop, the energy storage response and the system inertia to the frequency stability in the virtual power plant.

[0014] In a second aspect, the application further provides a virtual power plant frequency stability optimization system, comprising: a data acquisition module, configured to acquire stability parameters of the virtual power plant, and to perform unit normalization on the stability parameters to obtain unit data; a data calculation module, configured to calculate a frequency stability evaluation value of the virtual power plant according to the unit data, and to compare the stability evaluation value with a preset threshold; an iterative setting module, configured to, when the stability evaluation value is less than the preset threshold, perform iterative setting on the stability parameters by using a setting factor, to obtain the unit data after iterative setting, and to readjust the setting factor according to an iteration number; the iterative setting module is further configured to recalculate the frequency stability evaluation update value of the virtual power plant according to the unit data after iterative setting, until the frequency stability update value is not less than the preset threshold; and an evaluation module, configured to calculate a frequency stability improvement rate of the virtual power plant by using the unit data before iterative setting and the unit data after iterative setting.

[0015] In a third aspect, the application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor executes the computer program to implement the virtual power plant frequency stability optimization method of any one of the above aspects.

[0016] According to the specific embodiments provided in the application, the following technical effects are disclosed: The virtual power plant frequency stability optimization method, system and computer device provided by the application can convert parameters of different units and different dimensions into dimensionless unit data through unit normalization on the acquired original stability parameters, solve the problem that heterogeneous parameters cannot be cooperatively calculated in a unified model, perform frequency stability evaluation and iterative setting based on the unit data, make the control strategy focus on the relative operating state of each device, ensure that all distributed resources can participate in adjustment according to their own capabilities, introduce and dynamically set a factor when the evaluation value does not meet the requirements, ensure that the optimization process always advances towards the goal of improving frequency stability until the preset safety standard is reached. The application can accurately and quickly realize virtual power plant frequency stability optimization, and finally quantify the optimization results through the frequency stability improvement rate. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0018] Figure 1 The flow chart of the optimization method of the frequency stability of the virtual power plant in the embodiment of the present application.

[0019] Figure 2 The execution block diagram of the optimization method of the frequency stability of the virtual power plant in the embodiment of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without any creative work fall within the protection scope of the present application.

[0021] As shown in Figure 1 The embodiment of the present application provides an optimization method of the frequency stability of a virtual power plant, which comprises the following steps: S110, obtaining the stability parameter of the virtual power plant and performing per-unit processing on the stability parameter to obtain per-unit data.

[0022] S120, calculating the frequency stability evaluation value of the virtual power plant according to the per-unit data and comparing the stability evaluation value with a preset threshold.

[0023] S130, if the stability evaluation value is less than the preset threshold, performing iterative setting on the stability parameter by a setting factor to obtain the per-unit data after iterative setting, and readjusting the setting factor according to the iteration number.

[0024] S140, recalculating the frequency stability evaluation update value of the virtual power plant according to the per-unit data after iterative setting until the frequency stability update value is not less than the preset threshold.

[0025] S150, calculating the frequency stability improvement rate of the virtual power plant by the per-unit data before iterative setting and the per-unit data after iterative setting.

[0026] The virtual power plant frequency stability optimization method provided in the embodiments of the present application solves the problem that heterogeneous parameters cannot be cooperatively calculated in a unified model by performing normalization processing on the obtained original stability parameters, converting parameters of different units and different dimensions into dimensionless normalized data, performing frequency stability evaluation and iterative setting based on the normalized data, so that the control strategy focuses on the relative operating state of each device, ensuring that all distributed resources can participate in adjustment according to their own capabilities, and when the evaluation value does not meet the requirements, by introducing and dynamically setting a factor, it is ensured that the optimization process always advances towards the goal of improving the frequency stability until the preset safety standard is reached. The present application can accurately and quickly realize the optimization of the frequency stability of the virtual power plant, and finally quantify the optimization results through the frequency stability improvement rate.

[0027] Exemplarily, the stability parameters described above include distributed wind farm output power , distributed photovoltaic power station output power , distributed energy storage station charging and discharging power , state of charge of distributed energy storage station , actual frequency of virtual power plant grid connection point , virtual power plant electrical load power , virtual inertia coefficient of distributed wind turbine , droop coefficient of distributed photovoltaic , equivalent inertia coefficient of virtual power plant .

[0028] After obtaining the stability parameters described above, normalized data is obtained by performing normalization processing on the stability parameters, wherein the normalized data includes a normalized value corresponding to each of the plurality of parameters. For a single parameter, the normalized value is the ratio between the actual value of the parameter and the selected rated value (reference value), and the normalized value is a dimensionless value that represents the multiple of the actual value relative to the rated value. Therefore, the normalization process is a process of obtaining the normalized value by comparing the actual value and the rated value.

[0029] For the stability parameters described above, in the process of obtaining the normalized value, the selected rated value is the rated value of a single device in the power plant, so the normalized value needs to be calculated according to the actual value of the single device. Specifically, the process of calculating the normalized data is as follows: The ratio of the output power of a distributed wind farm to the rated power of its wind turbines is calculated as the per-unit value of the distributed wind farm's output power; the ratio of the output power of a distributed photovoltaic power station to the rated output power of its inverter is calculated as the per-unit value of the distributed photovoltaic power station's output power; the ratio of the charging and discharging power of a distributed energy storage station to the rated charging and discharging power of its energy storage converter is calculated as the per-unit value of the distributed energy storage station's charging and discharging power; the ratio of the state of charge (SBC) of a distributed energy storage station to its rated SBC is calculated as the per-unit value of the distributed energy storage station's SBC; and the deviation between the actual frequency and the rated frequency at the grid connection point of the virtual power plant is calculated as the per-unit value of the rated frequency. The ratio between the two values ​​is used as the per-unit value of the actual frequency of the virtual power plant's grid connection point; the ratio between the virtual power plant's electrical load power and the maximum electrical load is used as the per-unit value of the virtual power plant's electrical load power; the ratio between the virtual inertia coefficient and the rated inertia coefficient of the distributed wind farm's turbines is used as the per-unit value of the virtual inertia coefficient of a single turbine in the distributed wind farm; the ratio of the distributed photovoltaic droop coefficient multiplied by the inverter's rated output power to the rated frequency is used as the per-unit value of the distributed photovoltaic droop coefficient; the ratio between the virtual power plant's equivalent inertia coefficient and the rated inertia coefficient is used as the per-unit value of the virtual power plant's equivalent inertia coefficient.

[0030] The calculation process described above is shown in the following formula: (1) In the above formula, For distributed wind farms j The per-unit value of the output power of a typhoon generator. Rated power of a single wind turbine in a distributed wind farm; For the first distributed photovoltaic power station k The per-unit value of the inverter's output power. Rated power of a single inverter in a distributed photovoltaic power station; For distributed energy storage stations l The per-unit value of the charging and discharging power of the energy storage converter. The rated power of a single inverter in a distributed energy storage station; For distributed energy storage stations l The per-unit value of the state of charge of Taiwan's energy storage. The rated value for the state of charge of a single energy storage unit in a distributed energy storage station; This represents the per-unit value of the frequency at the virtual power plant's grid connection point. This is the rated value of the power grid frequency; This represents the per-unit value of the virtual power plant's electrical load power. This represents the maximum electrical load of the virtual power plant. For distributed wind farms j The per-unit value of the virtual inertia coefficient of a typhoon generator. This represents the rated virtual inertia of a single wind turbine in a distributed wind farm. For the first distributed photovoltaic power station k The per-unit value of the droop factor of the inverter; This represents the per-unit value of the equivalent inertia coefficient of the virtual power plant. This is the rated value of the equivalent inertia coefficient of the virtual power plant.

[0031] In the above calculation process, when obtaining per-unit values ​​for parameters such as the output power of distributed wind farms, the output power of distributed photovoltaic power stations, the charging and discharging power of distributed energy storage stations, and the state of charge of distributed energy storage stations, a specific single device was used, such as the [device name missing]. j The output power of the typhoon fan is compared with the rated power of a single fan to calculate the per-unit value.

[0032] After obtaining the per-unit data of the stability parameters, step S120 above is executed. The frequency stability assessment value of the virtual power plant is calculated using the per-unit values ​​of each parameter. This includes: confirming the number of wind turbines, energy storage converters, and photovoltaic inverters in the virtual power plant, and calculating the frequency stability assessment value based on the per-unit data. The calculation method is as follows: (2) in, J This represents the number of wind turbines in the virtual power plant. L This refers to the number of energy storage converters in the virtual power plant. K This represents the number of photovoltaic inverters in the virtual power plant. For distributed wind farms j The per-unit value of the output power of a typhoon generator. For the first distributed photovoltaic power station k The per-unit value of the inverter's output power. For distributed energy storage stations l The per-unit value of the charging and discharging power of the energy storage converter. For distributed energy storage stations l The per-unit value of the state of charge of Taiwan's energy storage. This represents the per-unit value of the frequency at the virtual power plant's grid connection point. This represents the per-unit value of the virtual power plant's electrical load power. For distributed wind farms j The per-unit value of the virtual inertia coefficient of a typhoon generator. For the first distributed photovoltaic power station k The per-unit value of the droop factor of the inverter; This represents the per-unit value of the equivalent inertia coefficient of the virtual power plant. M This represents the frequency stability assessment value.

[0033] After calculating the stability assessment value, the frequency stability assessment value is compared with a preset threshold. For example, the preset threshold is set to 0. , without setting the parameters of the virtual power plant, if , the stability parameters of the virtual power plant need to be set.

[0034] The application embodiment adopts the setting factor The output power of the distributed wind farm in the virtual power plant, the output power of the distributed photovoltaic power station, the virtual inertia coefficient of the distributed wind farm fan, the droop coefficient of the distributed photovoltaic, and the charge and discharge power of the distributed energy storage station are set. To get the setting after the standard data, that is, the standard value of each parameter after setting. The calculation method is as follows: (3) Among them, is the standard value of the output power of the first j wind turbine of the distributed wind farm after setting, is the standard value of the output power of the first k inverter of the distributed photovoltaic power station after setting, is the standard value of the virtual inertia coefficient of the first j wind turbine of the distributed wind farm after setting, is the standard value of the droop coefficient of the first k inverter of the distributed photovoltaic power station after setting, is the standard value of the charge and discharge power of the first l energy storage converter of the distributed energy storage station after setting, is the setting factor.

[0035] After adjusting by the setting factor, the standard data after setting is obtained. The frequency stability evaluation value can be recalculated by the standard data after setting, and it is judged whether it is greater than or equal to the preset threshold 0.

[0036] The setting by the setting factor is an iterative process. In each setting process, the setting factor is adjusted. In step S130, the setting factor is adjusted according to the iteration number, including: confirming the initial setting factor, the iteration step and the current iteration number; confirming the adjustment value according to the iteration step and the current iteration number; and adjusting the setting factor according to the adjustment value and the initial setting factor, as shown in the following formula: (4) In the formula, is the setting factor, represents an initial setting factor, x is an iteration number, d is an iteration step, wherein the current iteration number is not greater than a maximum iteration number, the initial setting factor and the iteration step are preset constants. Exemplarily, the iteration step is 0.1, the maximum iteration number is 10 times, and the initial setting factor is 1.0. In the process of each iteration, the setting factor is updated, and the per-unit value obtained by adjusting the updated setting factor also changes until the virtual power plant frequency stability evaluation value .

[0037] In the step S150, the frequency stability improvement rate of the virtual power plant is calculated by using the per-unit data before iteration setting and the per-unit data after iteration setting, including: The previous stability index is calculated by using the per-unit data before iteration setting, the current stability index is calculated by using the per-unit data after iteration setting, and the frequency stability improvement rate is calculated according to the current stability index and the previous stability index, as shown in the following formula: (5) wherein, represents the frequency stability improvement rate, represents the current stability index, represents the previous stability index.

[0038] The previous stability index is calculated by using the per-unit data before iteration setting, as shown in the following formula: (6) The current stability index is calculated by using the per-unit data after iteration setting, as shown in the following formula: (7) In the above formula, are respectively the contribution weights of the wind turbine inertia, the photovoltaic droop, the energy storage response and the system inertia to the frequency stability in the virtual power plant.

[0039] The previous stability index and the current stability index are calculated by using the above formulas (6) and (7), and the frequency stability improvement rate is calculated according to the above formula (5). Thus, the optimization result can be quantitatively reflected.

[0040] As shown in Figure 2 , it is an execution block diagram of the optimization method of the virtual power plant frequency stability in the embodiment of the present application. The steps of the method will be described below: Figure 2 ​Firstly, after the beginning of the method, the stability parameters required for the frequency stability optimization in the virtual power plant are collected, including the distributed wind farm output power, the distributed photovoltaic power station output power, the distributed energy storage station charging and discharging power, the distributed energy storage station state of charge, the virtual power plant grid connection point actual frequency, the virtual power plant electric load power, the distributed wind turbine virtual inertia coefficient, the distributed photovoltaic droop coefficient, and the virtual power plant equivalent inertia coefficient.

[0041] Then, the stability parameters are normalized to obtain the normalized values corresponding to the stability parameters. The virtual power plant frequency stability evaluation value M is calculated according to the normalized values, and it is judged whether M is not less than 0. If yes, it means that the virtual power plant parameters do not need to be set. If M is less than 0, it means that the evaluation value is too small, and the virtual power plant parameters need to be set by the setting factor. After completing the setting of the current round, the setting factor is updated according to the iteration number and the iteration step for the next iteration calculation process, and the stability parameter machine normalized value is adjusted. The normalized value after updating is used to calculate the power plant frequency stability evaluation value, until M is greater than or equal to 0.

[0042] Finally, the virtual power plant stability index before setting and the virtual power plant stability index after setting are calculated respectively, so as to confirm the frequency stability improvement rate, so as to quantitatively show the optimization result.

[0043] The following will be described in combination with specific examples and data, but the content of the application is not limited to the following examples only. Those skilled in the art can make various modifications or modifications to the present application, and these equivalent forms are also within the scope defined by the present application.

[0044] Taking a distributed virtual power plant in East China as an example, the virtual power plant includes 15 2.5MW wind turbines (total installed capacity 37.5MW), 40 630kW centralized photovoltaic inverters (total installed capacity 25.2MW), 8 1.5MW / 3MWh lithium battery energy storage converters (total installed capacity 12MW / 24MWh), connected to a 220kV substation 10kV bus operation, system rated frequency 50Hz, maximum electric load 50MW.

[0045] The parameters required for the virtual power plant frequency stability optimization at time t are collected, including: the virtual power plant distributed wind farm output power 2MW, the distributed photovoltaic power station output power 500KW, the distributed energy storage station charging and discharging power -300KW, the state of charge of the distributed energy storage station 60%, the virtual power plant grid connection point actual frequency 49.5HZ, the virtual power plant electric load power Virtual inertia coefficient of 45MW, distributed wind farm wind turbine Droop coefficient of 2S, distributed photovoltaic 0.005 Equivalent inertia coefficient of virtual power plant 3S.

[0046] Rated power of single wind turbine of distributed wind farm 2.5MW; rated power of single inverter of distributed photovoltaic power station 630KW; rated power of single inverter of distributed energy storage station 1500KW; rated value of single energy storage state of charge of distributed energy storage station 100%; rated value of grid frequency is 50HZ; maximum electrical load of virtual power plant 50MW; rated value of virtual inertia of single wind turbine of distributed wind farm 4S; rated value of equivalent inertia coefficient of virtual power plant 6S.

[0047] According to the above formula (1), the relevant data are substituted to obtain: After completing the unit, according to the above formula (2), the data are substituted to obtain: At this time, M<0, so the frequency stability of virtual power plant needs to be optimized. The setting factor The output power of distributed wind farm, the output power of distributed photovoltaic power station, the virtual inertia coefficient of distributed wind farm wind turbine, the droop coefficient of distributed photovoltaic, and the charge and discharge power of distributed energy storage station in virtual power plant are set, wherein , , x is the iteration number, the maximum iteration number is set to 10 times, and the set virtual power plant parameter calculation formula (3) is in. The iteration calculation of virtual power plant frequency stability evaluation value and virtual power plant parameter optimization value is carried out until .

[0048] According to the above formula (6) and (7), the virtual power plant frequency stability index before and after setting is calculated respectively: According to the calculation result, the stability improvement rate is further calculated: Finally, the optimization result is displayed in a quantitative way. The method can effectively improve the frequency stability of virtual power plant.

[0049] The embodiment of the present application also provides a virtual power plant frequency stability optimization system, comprising a data acquisition module, a data calculation module, an iterative setting module and an evaluation module.

[0050] The data acquisition module is used for acquiring stability parameters of the virtual power plant and performing per-unit processing on the stability parameters to obtain per-unit data; the data calculation module is used for calculating a frequency stability evaluation value of the virtual power plant according to the per-unit data and comparing the stability evaluation value with a preset threshold; the iterative setting module is used for performing iterative setting on the stability parameters by using a setting factor when the stability evaluation value is less than the preset threshold, to obtain iteratively set per-unit data, and readjusting the setting factor according to an iteration number; the iterative setting module is also used for recalculating a frequency stability evaluation update value of the virtual power plant according to the iteratively set per-unit data, until the frequency stability update value is not less than the preset threshold; and the evaluation module is used for calculating a frequency stability improvement rate of the virtual power plant by using the per-unit data before the iterative setting and the per-unit data after the iterative setting.

[0051] The embodiment of the present application also provides a computer device, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor executes the computer program to implement the virtual power plant frequency stability optimization method.

[0052] The virtual power plant frequency stability optimization method, system and computer device provided in the embodiment of the present application solve the problem that heterogeneous parameters cannot be cooperatively calculated in a unified model by performing per-unit processing on the acquired original stability parameters, converting parameters of different units and different dimensions into dimensionless per-unit data, performing frequency stability evaluation and iterative setting based on the per-unit data, making the control strategy focus on the relative operating state of each device, ensuring that all distributed resources can participate in adjustment according to their own capabilities, introducing and dynamically setting a factor when the evaluation value does not meet the requirements, ensuring that the optimization process always advances towards the goal of improving frequency stability until the preset safety standard is reached. The present application can accurately and quickly realize virtual power plant frequency stability optimization, and finally quantize the optimization result through the frequency stability improvement rate.

[0053] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of the related data need to comply with relevant regulations.

[0054] The database involved in each of the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, and the like, without being limited thereto. The processor involved in each of the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, and the like, without being limited thereto.

[0055] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but it should be considered that any combination of the technical features is within the scope of the present disclosure, as long as there is no contradiction.

[0056] The principles and implementation modes of the present application are described by applying specific examples herein, and the above embodiments are only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation modes and application ranges will be changed according to the idea of the present application. In summary, the content of the present description should not be understood as a limitation of the present application.

Claims

1. A method for optimizing the frequency stability of a virtual power plant, characterized in that, The optimization method for the frequency stability of the virtual power plant includes: The stability parameters of the virtual power plant are obtained, and the stability parameters are processed by per-unit processing to obtain per-unit data; The frequency stability assessment value of the virtual power plant is calculated based on the per-unit data, and the stability assessment value is compared with a preset threshold. If the stability evaluation value is less than the preset threshold, the stability parameter is iteratively tuned by a tuning factor to obtain the iteratively tuned per-unit data, and the tuning factor is readjusted according to the number of iterations. The frequency stability assessment update value of the virtual power plant is recalculated based on the per-unit data after iterative tuning, until the frequency stability update value is not less than the preset threshold. The frequency stability improvement rate of the virtual power plant is calculated using the per-unit data before and after iterative tuning.

2. The method for optimizing the frequency stability of a virtual power plant according to claim 1, characterized in that, The stability parameters include the output power of distributed wind farms, the output power of distributed photovoltaic power stations, the charging and discharging power of distributed energy storage stations, the state of charge of distributed energy storage stations, the actual frequency of the virtual power plant grid connection point, the power load of the virtual power plant, the virtual inertia coefficient of the distributed wind farm turbines, the droop coefficient of the distributed photovoltaic system, and the equivalent inertia coefficient of the virtual power plant.

3. The method for optimizing the frequency stability of a virtual power plant according to claim 2, characterized in that, The stability parameters are processed by per-unit processing to obtain per-unit data, including: The ratio of the output power of the distributed wind farm to the rated power of the wind turbine is calculated as the per-unit value of the output power of the distributed wind farm. The ratio of the output power of the distributed photovoltaic power station to the rated output power of the inverter is calculated as the per-unit value of the output power of the distributed photovoltaic power station. The ratio of the charging and discharging power of the distributed energy storage station to the rated charging and discharging power of the energy storage converter is calculated as the per-unit value of the charging and discharging power of the distributed energy storage station. The ratio of the state of charge (SBC) of the distributed energy storage station to its rated value is calculated as the per-unit value of the SBC of the distributed energy storage station. The ratio between the deviation of the actual frequency of the virtual power plant's grid connection point from the rated frequency and the rated frequency is calculated as the per-unit value of the actual frequency of the virtual power plant's grid connection point. The ratio between the virtual power plant's electrical load power and the maximum electrical load is calculated as the per-unit value of the virtual power plant's electrical load power; The ratio of the virtual inertia coefficient of the wind turbines in the distributed wind farm to the rated inertia coefficient is calculated as the per-unit value of the virtual inertia coefficient of a single wind turbine in the distributed wind farm. The ratio of the product of the distributed photovoltaic droop coefficient and the rated output power of the inverter to the rated frequency is calculated and used as the per-unit value of the distributed photovoltaic droop coefficient. The ratio of the equivalent inertia coefficient of the virtual power plant to the rated inertia coefficient is calculated as the per-unit value of the equivalent inertia coefficient of the virtual power plant.

4. The method for optimizing the frequency stability of a virtual power plant according to claim 2, characterized in that, The frequency stability assessment value of the virtual power plant is calculated based on the per-unit data, including: Confirm the number of wind turbines, energy storage converters, and photovoltaic inverters in the virtual power plant, and calculate the frequency stability assessment value based on the per-unit data. The calculation method is as follows: in, J The number of wind turbines in the virtual power plant. L The number of energy storage converters in the virtual power plant. K The number of photovoltaic inverters in the virtual power plant. For distributed wind farms j The per-unit value of the output power of a typhoon generator. For the first distributed photovoltaic power station k The per-unit value of the inverter's output power. For distributed energy storage stations l The per-unit value of the charging and discharging power of the energy storage converter. For distributed energy storage stations l The per-unit value of the state of charge of Taiwan's energy storage. This represents the per-unit value of the frequency at the virtual power plant's grid connection point. This represents the per-unit value of the virtual power plant's electrical load power. For distributed wind farms j The per-unit value of the virtual inertia coefficient of a typhoon generator. For the first distributed photovoltaic power station k The per-unit value of the droop factor of the inverter; This represents the per-unit value of the equivalent inertia coefficient of the virtual power plant. M This represents the frequency stability assessment value.

5. The method for optimizing the frequency stability of a virtual power plant according to claim 4, characterized in that, The stability parameter is iteratively tuned using a tuning factor to obtain the per-unit data after iterative tuning, as shown in the following formula: in, For the first distributed wind farm after adjustment j The per-unit value of the output power of a typhoon generator. For the first distributed photovoltaic power station after adjustment k The per-unit value of the inverter's output power. For the first distributed wind farm after adjustment j The per-unit value of the virtual inertia coefficient of a typhoon generator. For the first distributed photovoltaic power station after adjustment k The per-unit value of the droop factor for the Taiwanese inverter. For the first distributed energy storage station after adjustment l The per-unit value of the charging and discharging power of the energy storage converter. The tuning factor is denoted as .

6. The method for optimizing the frequency stability of a virtual power plant according to claim 1, characterized in that, The tuning factor is readjusted based on the number of iterations, including: Confirm the initial tuning factor, iteration step size, and current iteration number; The adjustment value is determined based on the iteration step size and the current iteration number; The tuning factor is readjusted based on the adjustment value and the initial tuning factor, as shown in the following formula: In the formula, For the tuning factor, Let x be the initial tuning factor, d be the iteration number, and d be the iteration step size, wherein the current iteration number is not greater than the maximum iteration number, and the initial tuning factor and the iteration step size are preset constants.

7. The method for optimizing the frequency stability of a virtual power plant according to claim 5, characterized in that, The frequency stability improvement rate of the virtual power plant is calculated using the per-unit data before and after iterative tuning, including: The prior stable index is calculated using the per-unit data prior to iterative tuning; The current stability index is calculated using the per-unit data after iterative tuning. The frequency stability improvement rate is calculated based on the current stability index and the prior stability index, as shown in the following formula: in, This indicates the frequency stability improvement rate. This indicates the current stability index. This indicates the prior stability index.

8. The method for optimizing the frequency stability of a virtual power plant according to claim 7, characterized in that, The prior stability index is calculated using the per-unit data prior to iterative tuning, as shown in the following formula: The current stability index is calculated using the per-unit data after iterative tuning, as shown in the following formula: in, These represent the contribution weights of wind turbine inertia, photovoltaic droop, energy storage response, and system inertia to frequency stability in a virtual power plant.

9. An optimization system for the frequency stability of a virtual power plant, characterized in that, The virtual power plant frequency stability optimization system includes: The data acquisition module is used to acquire the stability parameters of the virtual power plant and perform per-unit processing on the stability parameters to obtain per-unit data; The data calculation module is used to calculate the frequency stability assessment value of the virtual power plant based on the per-unit data, and compare the stability assessment value with a preset threshold. The iterative tuning module is used to iteratively tune the stability parameter by a tuning factor when the stability evaluation value is less than the preset threshold, so as to obtain the per-unit data after iterative tuning, and readjust the tuning factor according to the number of iterations; The iterative tuning module is also used to recalculate the frequency stability assessment update value of the virtual power plant based on the per-unit data after iterative tuning, until the frequency stability update value is not less than the preset threshold. The evaluation module is used to calculate the frequency stability improvement rate of the virtual power plant using the per-unit data before and after iterative tuning.

10. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for optimizing the frequency stability of a virtual power plant according to any one of claims 1-8.

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