Wind and light storage modular modeling method and system oriented to electromechanical transient state of power system

Abstract

The invention provides a wind and light storage modular modeling method and system oriented to an electromechanical transient state of a power system, relates to the technical field of power system simulation, and aims to solve the problems that in the prior art, the requirements of a user on modular recombination and customized adaptation of a model cannot be met, the model reuse rate is low, the flexibility is poor, and the modeling efficiency is high. And a unified modeling standard system is not formed yet, so that the development requirement of a novel power system is difficult to adapt. Comprising the following steps: based on energy form decoupling, carrying out modular decomposition and reconstruction on a wind and light storage dynamic model, decomposing the model into three links of energy conversion and control, energy buffering and energy output and control, and constructing a hierarchical integrated architecture; and based on the hierarchical integrated architecture, modular sub-models corresponding to the fan, the photovoltaic device and the energy storage device are constructed respectively, and dynamic combination is carried out to obtain a modeling result. According to the method, the problems in the prior art are solved, the model reuse rate and flexibility are improved, and the model simulation precision and efficiency are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system simulation, and in particular to a wind, solar and storage modular modeling method and system for electromechanical transients of power systems. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The increasing proportion of renewable energy sources, such as wind turbines, photovoltaics, and energy storage, in power systems is leading to increasingly complex structures and dynamic characteristics. Electromechanical transient modeling is fundamental to power system stability and dynamic simulation analysis. However, significant differences exist between wind, solar, and energy storage equipment manufacturers in controller architecture and control strategies. Traditional modeling methods are no longer adaptable to the complexity, fragmentation, and heterogeneity of dynamic models for wind, solar, and energy storage equipment. There is an urgent need to transition to a modular and flexible modeling architecture through innovative modeling approaches.

[0004] Current research on wind, solar, and storage electromechanical transient modeling in power systems primarily focuses on electromechanical transient simulation software for various power systems. Mainstream power system simulation software includes PSASP and PSD-BPA developed by the China Electric Power Research Institute, and PSS / E from PTI (USA). After years of development, these power system simulation software have established relatively complete wind, solar, and storage model libraries, with integrated models packaged as complete sets, creating a relatively complete wind, solar, and storage modeling system. However, these models generally utilize "black box" packaging, and the functional modules of the integrated wind, solar, and storage models are tightly coupled, making it difficult for users to modularly reorganize and customize the models according to actual simulation needs. This results in low model reuse and limits simulation flexibility.

[0005] Furthermore, a unified modeling standard system has yet to be established. Power system simulation software exhibits significant differences in key technical aspects such as model interfaces, parameter definitions, and model packaging. This severely hinders cross-platform model sharing and collaboration, requiring extensive adaptation and modification to reuse models across software, significantly increasing the time and cost of simulation research. Current modeling methods are no longer adaptable to the development needs of new power systems. Summary of the Invention

[0006] In order to overcome the shortcomings of the above-mentioned existing technologies, the present invention provides a modular modeling method and system for wind, solar and storage for electromechanical transients in power systems. A modular modeling system is constructed based on energy form decoupling to realize dynamic reorganization modeling of modular components and improve modeling flexibility; a universal model is constructed to realize model sharing and collaboration; through a model selection strategy, the accuracy and efficiency of the model in simulation are taken into account; a hierarchical integrated architecture is constructed and equipped with user-defined modeling and model verification functions, which improves the scalability and reliability of the modular modeling system and can adapt to the development needs of new power systems.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0008] The first aspect of the present invention provides a modular modeling method for wind, solar and storage systems for electromechanical transients in power systems, comprising:

[0009] Based on energy form decoupling, the wind, solar and energy storage dynamic model is modularly decomposed and reconstructed into three parts: energy conversion and control, energy buffering, and energy output and control.

[0010] Build a hierarchical integration architecture based on three links;

[0011] Based on the hierarchical integrated architecture, modular models corresponding to wind turbines, photovoltaics, and energy storage equipment are constructed separately and dynamically combined to obtain modeling results;

[0012] Among them, in the modeling, the specific process is: constructing a general model of wind, solar and energy storage;

[0013] According to the module selection strategy of key wind, solar and energy storage equipment in the power system, modules are selected for modeling;

[0014] Modeling is done through user-defined modules.

[0015] As an implementation method, the energy conversion and control link includes:

[0016] Wind speed model, aerodynamic model and pitch angle control model of wind power generation system;

[0017] Irradiation model, temperature model and photovoltaic array model of photovoltaic power generation system;

[0018] Pure electrochemical model or pumped storage model of energy storage system.

[0019] As an implementation method, the energy buffering step includes:

[0020] Mechanical system model of wind power generation system;

[0021] DC capacitor modules for photovoltaic power generation systems;

[0022] Capacitor model or mechanical rotor model of the energy storage system.

[0023] As an implementation method, the energy output and control link includes: a generator / converter model, an electrical control model, a high and low penetration control model, and a protection model.

[0024] As an implementation method, a hierarchical integrated architecture is constructed. Specifically, in the power system simulation toolkit STEPS simulation platform, a hierarchical integrated architecture of a device type layer, a model type layer, and a specific model layer is established.

[0025] As an implementation method, the module selection strategy for key wind, solar and energy storage equipment is as follows:

[0026] Through the power system flow calculation, the system power flow distribution is obtained;

[0027] Based on the system power flow distribution, the PR value of each power node is calculated using the improved PageRank algorithm;

[0028] Choose to use a detailed model or a simplified model for modeling based on the PR value of each power node.

[0029] As an implementation method, the PR value of each power node is calculated using an improved PageRank algorithm. The specific process is as follows:

[0030] Based on the system power flow distribution, a downstream distribution matrix considering active power is constructed and the distribution coefficient matrix is ​​calculated;

[0031] Based on the allocation coefficient matrix, the power distribution of the power flow lines and the link weights between each power node are calculated;

[0032] Based on the link weights between the power nodes, the PR value of each power node is obtained by iterative calculation.

[0033] A second aspect of the present invention provides a wind, solar, and storage modular modeling system for electromechanical transients in power systems, comprising:

[0034] The modular decomposition and reconstruction module is used to modularly decompose and reconstruct the wind, solar and energy storage dynamic model based on energy form decoupling, breaking it down into three parts: energy conversion and control, energy buffering, and energy output and control.

[0035] Hierarchical integration architecture building module, used to build a hierarchical integration architecture based on three links;

[0036] The modeling module is used to build modular models corresponding to wind turbines, photovoltaics and energy storage equipment based on a hierarchical integrated architecture, and dynamically combine them to obtain modeling results. The specific process in modeling is as follows: building a general model of wind, photovoltaics and energy storage; selecting modules for modeling based on the module selection strategy of key wind, photovoltaics and energy storage equipment in the power system; and modeling through user-defined modules.

[0037] A third aspect of the present invention provides a computer device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method described in the first aspect of the present invention are implemented.

[0038] The fourth aspect of the present invention aims to provide a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the method described in the first aspect of the present invention.

[0039] One or more of the above technical solutions have the following beneficial effects:

[0040] This embodiment aims to model wind, solar and energy storage equipment models that are complex, fragmented and heterogeneous. It combines energy form decoupling modular modeling, module selection strategies for key wind, solar and energy storage equipment, and a hierarchical integrated architecture to establish a standardized and easily scalable modular modeling system and framework. It can achieve "plug and play" and collaborative sharing, allowing users to modularly reorganize and customize the model according to actual simulation needs, thereby improving the model reuse rate and simulation flexibility, and thus meeting the development needs of new power systems.

[0041] In this embodiment, a modular modeling system is constructed based on energy form decoupling, which realizes dynamic recombinant modeling of module components and improves the flexibility of modeling.

[0042] In this embodiment, by constructing universal models of converter, electrical control, high and low penetration control and protection control, model sharing and collaboration between wind, solar and storage models are achieved, thereby improving the efficiency of modeling and simulation.

[0043] In this embodiment, a module selection strategy based on the identification of key wind, solar and storage devices in the power system is implemented, which combines the power system flow distribution with the PageRank algorithm, thereby improving the accuracy of module selection in modeling while taking into account the accuracy and efficiency of the model in simulation.

[0044] In this embodiment, a hierarchical integrated architecture is constructed in the STEPS simulation platform of the power system simulation toolkit and equipped with user-defined modeling and model verification functions, thereby improving the scalability and reliability of the modular modeling system.

[0045] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0047] Figure 1 This is a flow chart of a modular modeling method for wind, solar and storage for electromechanical transients in a power system according to the first embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of modular modeling based on energy form decoupling according to the first embodiment of the present invention;

[0049] Figure 3 This is a flowchart of a modular selection strategy according to the first embodiment of the present invention;

[0050] Figure 4 Schematic diagram of the active power control part of the general electrical control model according to the first embodiment of the present invention;

[0051] Figure 5 Schematic diagram of the frequency control part in the general electrical control model of the first embodiment of the present invention;

[0052] Figure 6 Schematic diagram of the reactive power control part of the general electrical model according to the first embodiment of the present invention;

[0053] Figure 7 Schematic diagram of the crossing state of the universal high-low crossing control model according to the first embodiment of the present invention;

[0054] Figure 8 Schematic diagram of a protection boundary curve of a universal protection model according to the first embodiment of the present invention;

[0055] Figure (a) shows the protection boundary curve of the general protection model under the low voltage ride-through state, and Figure (b) shows the protection boundary curve of the general protection model under the high voltage ride-through state.

[0056] Figure 9 Schematic diagram of three active standby operation modes of the wind turbine aerodynamic model according to the first embodiment of the present invention;

[0057] Figure 10 This is a flow chart of the derivative increment method for solving the maximum power point of the wind turbine aerodynamic model according to the first embodiment of the present invention;

[0058] Figure 11 Schematic diagram of the battery body model of the electrochemical energy storage system according to the first embodiment of the present invention;

[0059] Figure 12 Schematic diagram of the single / double-stage electrochemical energy storage control structure of the first embodiment of the present invention;

[0060] Among them, Figure (a) is a schematic diagram of the single-stage electrochemical energy storage control structure, and Figure (b) is a schematic diagram of the dual-stage electrochemical energy storage control structure;

[0061] Figure 13 This is a schematic diagram of a primary model of pumped storage according to the first embodiment of the present invention;

[0062] Figure 14 Schematic diagram of the rotor model of the energy storage system according to the first embodiment of the present invention;

[0063] Figure 15 Schematic diagram of the energy state model of the energy storage system according to the first embodiment of the present invention;

[0064] Figure 16 Schematic diagram of charge and discharge mode conversion of the energy storage system according to the first embodiment of the present invention;

[0065] Figure 17 Schematic diagram of the STEPS program architecture of the modular modeling and simulation platform according to the first embodiment of the present invention;

[0066] Figure 18 This is a schematic diagram of the hierarchical integration architecture of the first embodiment of the present invention. DETAILED DESCRIPTION

[0067] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0068] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0069] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0070] Example 1

[0071] This embodiment discloses a wind, solar and storage modular modeling method for electromechanical transients in power systems.

[0072] To more clearly illustrate this embodiment, the implementation process of wind, solar and storage modular modeling for power system electromechanical transients can be specifically described as follows:

[0073] A modular modeling approach for wind, solar, and energy storage systems for electromechanical transients in power systems, including:

[0074] S1. Based on energy form decoupling, the wind, solar and energy storage dynamic model is modularly decomposed and reconstructed into three parts: energy conversion and control, energy buffering, and energy output and control.

[0075] S2. Build a hierarchical integration architecture based on three links;

[0076] S3. Based on the hierarchical integrated architecture, modular models corresponding to wind turbines, photovoltaics, and energy storage equipment are constructed respectively, and dynamically combined to obtain modeling results;

[0077] Among them, in the modeling, the specific process is: constructing a general model of wind, solar and energy storage;

[0078] According to the module selection strategy of key wind, solar and energy storage equipment in the power system, modules are selected for modeling;

[0079] Modeling is done through user-defined modules.

[0080] like Figure 1 、 Figure 2 As shown, in step S1, based on energy form decoupling, the wind-solar-storage dynamic model is modularly decomposed and reconstructed into three links: energy conversion and control, energy buffering, and energy output and control.

[0081] In this embodiment, based on the differences in the energy conversion mechanisms of wind, photovoltaic, and energy storage devices, the present invention proposes a modular modeling system based on energy form decoupling. By decoupling the physical process of energy conversion and transmission paths, the complex system can be decomposed into three main links: energy conversion and control, energy buffering, and energy output and control. Further divisions can be made based on the characteristics of wind turbines, photovoltaics, and energy storage models, that is, the equipment is divided into several model types, and a modular structural block diagram of the wind, photovoltaic, and energy storage models can be further obtained. Among them:

[0082] (1) Energy conversion and control links.

[0083] This link characterizes the physical process and control mechanism of energy conversion. Specifically, it is the process of converting energy forms such as wind energy, solar energy, chemical energy, potential energy, etc. into mechanical energy or electrical energy. Its control unit is responsible for accurately controlling the energy conversion link and realizing dynamic optimization of conversion efficiency. Due to the differences in energy conversion of wind turbines, photovoltaics, and energy storage, it is necessary to establish it for different power generation systems. The energy conversion and control links include: wind speed model, aerodynamic model and pitch angle control model of wind power generation system; irradiation model, temperature model and photovoltaic array model of photovoltaic power generation system; electrochemical pure model or pumped storage model of energy storage system. Specifically:

[0084] 1) Wind power generation system

[0085] The wind speed model simulates the environmental input, and the aerodynamic model constructs the wind energy-mechanical energy conversion equation. The aerodynamic model can also provide blade speed command values ​​for impeller speed control. An optional external pitch angle control model can be used to adjust the wind turbine blade angle of attack.

[0086] 2) Photovoltaic power generation system

[0087] Based on the irradiation model and temperature model to obtain environmental parameters, the photovoltaic array model realizes the conversion from light energy to electrical energy and controls and regulates this process.

[0088] 3) Energy storage system

[0089] Only electrochemical energy storage and pumped hydro storage, which are currently widely used, are considered. The primary model of electrochemical energy storage uses a battery model to represent the conversion of chemical energy into electrical energy, and DC voltage control can be used to maintain the capacitor voltage at the rated value. The primary model of pumped hydro storage uses a turbine and water hammer model to simulate the dynamic hydraulic characteristics of potential energy to mechanical energy conversion, and a speed governor is used to achieve speed regulation. Auxiliary models are used to control and simulate the switching process between the charging and discharging states of the energy storage.

[0090] (2) Energy conversion and control links.

[0091] The energy conversion and control link serves as a dynamic buffer layer between the energy conversion and energy output links. This link achieves transient energy balance and power fluctuation smoothing through physical energy storage elements (such as mechanical rotors, capacitors, etc.).

[0092] The energy buffering link includes: the mechanical system model of the wind power generation system; the DC capacitor module of the photovoltaic power generation system; the capacitor model or mechanical rotor model of the energy storage system. Specifically:

[0093] The mechanical system model of the wind power generation system can simulate single / double mass blocks; the capacitor model of the photovoltaic power generation system; if electrochemical energy storage is used in the energy storage system, the energy buffer model is simulated as a capacitor; if pumped storage is used, the energy buffer model is simulated as a rotor.

[0094] (3) Energy output and control links

[0095] The energy output and control links include: generator / converter model, electrical control model, high and low voltage control model, and protection model.

[0096] The energy output and control phase is primarily responsible for regulating the converted energy and transmitting it to the grid according to grid requirements. Its control unit receives real-time grid status signals and dynamically adjusts the active and reactive power output strategies to ensure that the device's output characteristics match grid operating requirements.

[0097] Specifically, the energy output link for wind power generation systems and pumped hydropower storage is the generator converter, while the energy output link for photovoltaic and electrochemical energy storage is the inverter. They can all adopt a unified generator / converter model, which uses parameter configuration to represent the doubly fed / direct-drive wind turbine, photovoltaic inverter, and energy storage converter respectively. The control link adopts a unified electrical control model, high and low pass control, and protection model. The electrical control model and high and low pass control model provide active and reactive power reference signals to the converter model to achieve converter power output control, and the protection model is used to provide the generator tripping action logic. Therefore, the generator / converter model, electrical control model, high and low pass control model, and protection model can be used as a universal model for wind, solar and storage equipment.

[0098] After the above steps, a modular modeling system is constructed based on energy form decoupling, which breaks the strong coupling of the traditional "black box" model, realizes the dynamic reorganization modeling of module components, and improves the flexibility of modeling.

[0099] like Figure 1 As shown, in step S2, a hierarchical integration architecture is constructed based on three links.

[0100] In this embodiment, a hierarchical integrated architecture is constructed. Specifically, in the power system simulation toolkit STEPS simulation platform, a hierarchical integrated architecture of a device type layer, a model type layer, and a specific model layer is established.

[0101] Specifically, in this embodiment, modeling is performed on the STEPS simulation platform of the power system simulation toolkit.

[0102] The STEPS (Simulation Toolkit for Electrical Power Systems) is an open source simulation platform for large-scale AC / DC hybrid power system simulation. Figure 16 The figure below illustrates the STEPS program architecture. STEPS currently offers three major analysis functions: power system flow, short-circuit, and stability. The simulation scale is theoretically unlimited. The STEPS core program is developed in C++ and provides a Python interface for flexible access across various software platforms. Development began around 2008 and the software was officially released as open source under the MIT license in 2018. Users can access the STEPS source code directly from STEPS' official hosting website, Github, and the Gitee platform.

[0103] Build a hierarchical integration architecture, specifically:

[0104] The hierarchical inheritance architecture based on STEPS realizes modular dynamic reorganization, and divides the model into three layers: device type layer, model type layer, and specific model layer. There is an inheritance and derivation relationship between them, such as Figure 18 As shown, dynamic combination and expansion are achieved through the virtual function mechanism in C++ object-oriented programming.

[0105] (1) Device type layer: defines the basic properties of the device and the grid data acquisition interface, serving as the base class for all models. This class defines an abstract interface through pure virtual functions, forcing derived classes to override the virtual functions and implement specific function functions.

[0106] (2) Model type layer: Based on the energy-morphology decoupling theory, the dynamic model is divided into several model types. The signals or physical quantities of the same nature are transmitted between the model types, and a unified interface for cross-module interaction is defined.

[0107] (3) Specific model layer: Based on the differentiated characteristics of wind turbines, photovoltaics, and energy storage devices, specific model classes are derived to implement the underlying physical logic. Polymorphism is used to dynamically call derived class functions through base class pointers, allowing model types to be switched without modifying the upper-level logic.

[0108] After the above steps, the hierarchical integration architecture built on the STEPS platform has achieved dynamic expansion and cross-module collaboration of the model, improving the scalability of modular modeling and the stability of cross-module interaction.

[0109] like Figure 1 、 Figure 2 As shown, in step S3, based on the hierarchical integrated architecture, modular models corresponding to wind turbines, photovoltaics and energy storage equipment are constructed respectively, and dynamically combined to obtain modeling results; wherein, in the modeling, the specific process is: constructing a general model of wind, photovoltaic and energy storage; selecting modules for modeling according to the module selection strategy of key wind, photovoltaic and energy storage equipment in the power system; modeling through user-defined modules.

[0110] S3-1. Building a universal model for wind, solar, and energy storage based on a hierarchical integrated architecture

[0111] The universal model includes the generator / converter model, electrical control model, high and low-voltage control model, and protection model. To enable the sharing of universal models across wind turbines, photovoltaics, and energy storage systems, it is necessary to design a universal model that can take into account the characteristics of all wind, photovoltaic, and energy storage systems.

[0112] S3-1-1. Construction of general generator / converter model

[0113] The converter model is the interface model with the network. This model is simplified and somewhat ignores the converter's dynamics. Its inputs are command values ​​for power, voltage, and current. After delays and limiting, the output is the equivalent current injected into the network. Generator / converter models are primarily categorized as grid-following converters and grid-forming converters.

[0114] The grid-following converter uses a phase-locked loop to measure the voltage phase angle at the grid connection point to maintain synchronization with the grid. It also uses Park transformation to achieve decoupling control of active and reactive power. After the inverse Park transformation, the equivalent current injected into the network is formed, thus exhibiting current source characteristics. The grid injection current calculation formula of the grid-following converter is:

[0115]

[0116] Among them, I p , I q are active current and reactive current respectively, θ PLL is the measured voltage phase angle of the phase-locked loop, I G is the converter terminal current, The current vector injected into the grid, I x and I y are the real and imaginary parts of the grid injected current vector, respectively.

[0117] The current limiting methods of grid-following converters are generally divided into two types: active power priority and reactive power priority. Active power priority allocates the converter capacity to active power first, ensuring the output of active current first, and leaving the remaining capacity for reactive power. Reactive power priority is the opposite. The formula is as follows:

[0118]

[0119] Among them, I max is the upper limit of current, I d 'and I q ' are the active current and reactive current after passing through the current limiting link.

[0120] The grid-type converter directly controls the output virtual internal potential through the power loop to keep it synchronized with the grid, showing the characteristics of a voltage source. The current and voltage equation of the grid-type converter is as follows:

[0121]

[0122] Among them, R source and X source are the equivalent resistance and reactance of the converter, is the virtual internal potential, is the terminal voltage.

[0123] From formula (4), we can see that the injected current is coupled with the voltage of the corresponding node. Therefore, some transformation is needed to calculate the injected current of the grid. From formula (4), we can get:

[0124]

[0125] This transformation can be understood as converting the voltage source equivalent circuit into the current source equivalent circuit, that is, converting the equivalent impedance of the converter into admittance and adding it to the network admittance matrix. Therefore, the grid injection current of the grid-type converter is finally obtained as follows:

[0126]

[0127] Grid-type converters have better voltage and frequency support capabilities, which also means they have a higher risk of overcurrent. When simulating the current limit of a grid-type converter, the current should actually be limited indirectly by limiting the virtual internal potential. Therefore, it is possible to try to apply the current limiting strategy of a grid-type converter to a grid-type converter. The current limiting strategy is as follows:

[0128] (1) Using formula (6) to calculate the grid injection current without considering current limiting

[0129] (2) Perform Park transformation to obtain the active and reactive currents without considering current limiting. The formula is:

[0130]

[0131] Where θ is the node voltage phase angle.

[0132] (3) Using equations (2) and (3) to calculate I d and I q Current limiting is performed to obtain I d 'and I q '.

[0133] (4) to I d 'and I q 'Perform the inverse Park transform to obtain the grid injection current after current limiting The formula is:

[0134]

[0135] (5) Then reverse the virtual internal potential after current limiting, the formula is:

[0136]

[0137] After the above steps, the current limiting strategy can ensure that when the current does not exceed the limit, the virtual internal potential E is strictly equal to that when the current limiting model does not exist, and when the current reaches the limit, the virtual internal potential is effectively limited. This limitation is two-fold: on the one hand, the amplitude of the virtual potential, and on the other hand, the virtual power angle, which depends on whether the current limiting method is active priority or reactive priority.

[0138] S3-1-2. Construction of general electrical control model

[0139] The general electric control model is divided into two parts: active power control and reactive power control, which enables the model to support three types of equipment at the same time, and can simulate most control strategies in PSS / E, PSASP and PSD-BPA. The active power control part outputs active power instruction P cmd and active current command I pcmd , the reactive power control part outputs reactive power instruction Q cmd , reactive current command I qcmd and reactive voltage command E qcmd .

[0140] like Figure 4 As shown, ① is the speed control, and the speed deviation outputs the power reference signal through the speed regulator, which is applicable to the speed regulation process of wind power generation system and pumped storage system, where K pspeed and K ispeed are the proportional coefficient and integral coefficient of the speed regulator, ω and ω respectively ref ② is the speed control with torque control, which adjusts the torque by the speed deviation and then multiplies it with the speed to get the output power reference value. It is suitable for wind turbine systems and pumped storage systems where both torque and speed regulation are required. PP and K IP are the proportional and integral coefficients of the torque regulator respectively. ③ is power control, which can directly control the converter and is applicable to wind turbines, photovoltaics, and energy storage systems to achieve frequency modulation-dominated control. It is also used for direct control during the charge and discharge conversion process of pumped storage. ④ is DC voltage control, which can control the DC voltage of the capacitor to track the DC voltage reference value. It is applicable to photovoltaic power generation systems and electrochemical energy storage systems. V dc and V dcref are DC voltage and DC voltage reference value respectively. ⑤ is constant torque control, the torque is fixed at the initial value, the power setting value is determined by the speed, T0 is the initial torque, which is suitable for constant torque regulation of wind power generation system and pumped storage system. Under the charging condition of energy storage, ω ref The signs of ω and ω in the calculation are swapped.

[0141] In addition, the frequency control module is packaged separately, such as Figure 5It supports primary frequency regulation, secondary frequency regulation, and virtual inertia control. Its output, ΔP, can be added to the Pset parameter in the electrical control model, enabling the device to achieve frequency regulation. Within energy storage, ΔP also serves as the active power dispatch command value for upper-level control, controlling the storage's charge and discharge operations. Figure 5 In, K fint is the proportional coefficient of the secondary frequency modulation link, K vi is the proportional coefficient of the virtual inertia link, T vi is the time constant of the virtual inertia link, K droop is the proportional coefficient of the primary frequency modulation link, T droop is the time constant of the primary frequency modulation link.

[0142] Reactive power control supports constant voltage control, constant power factor control, and constant reactive power control, such as Figure 6 It also supports the use of superimposed line voltage drop compensation to control the voltage of another bus by detecting the voltage of any remote bus. pv and K VI are the proportional coefficient and integral coefficient of the voltage control link, P G Output active power for the device, PF ref is the power factor reference value, Q ref is the reactive power reference value.

[0143] S3-1-3. Construction of general high and low penetration control model

[0144] Currently, wind, solar and storage equipment are required to have high and low voltage ride-through capabilities. During low voltage ride-through, they are required to provide a certain amount of reactive power to the system to help voltage recovery, and during high voltage ride-through, they are required to absorb a certain amount of reactive power. Under voltage ride-through conditions, the normal electrical control model of the equipment is bypassed, and the control signal of the converter is determined by the high and low voltage ride-through control strategy. The control strategies vary from device to device, and the response characteristics vary greatly. Therefore, a three-stage voltage ride-through control strategy with good operability and versatility is adopted, and seven high and low voltage ride-through states are defined, such as Figure 7 shown.

[0145] The specific high and low wear control strategy switching logic is as follows:

[0146] (1) Under normal conditions, if the terminal voltage is lower than the low voltage crossing threshold V lvrt_th , then it enters the low voltage ride-through state, at this time the electrical control model is bypassed and its integral link is frozen, and the signal input of the wind turbine model is switched to the high and low voltage ride-through control model.

[0147] (2) In the low voltage ride-through state, if the terminal voltage is higher than the low voltage ride-through threshold value V lvrt_th, then it enters the low voltage ride-through recovery starting state. If the delay time T of the low voltage ride-through recovery starting time relay is lvrt_delay If it is 0, it means that there is no low pressure ride-through recovery starting state, and the system will directly enter the low pressure ride-through recovery state.

[0148] (3) When the low voltage ride-through recovery starting state is reached, if the terminal voltage is lower than the low voltage ride-through threshold value V lvrt_th , then it returns to the low voltage ride-through state. When entering the low voltage ride-through recovery starting point state, the low voltage ride-through recovery starting point time relay starts timing, T lvrt_delay It will then enter a low-pressure crossing recovery state.

[0149] (4) In the low voltage ride-through recovery state, if the terminal voltage is lower than the low voltage ride-through threshold value V lvrt_th , then it returns to the low voltage ride-through state. When the active current instruction I pcmd Restore to the active current command before entering the low voltage ride-through state and the reactive current command I qcmd When the reactive current instruction is restored to the level before entering the low voltage ride-through state, the system enters the normal state. At this time, the electrical control model cancels the bypass and integrates normally, and the signal input of the wind turbine model is switched to the electrical control model.

[0150] (5) Under normal conditions, if the terminal voltage is higher than the high voltage crossing threshold V hvrt_th , then it enters the high-voltage ride-through state, at this time the electrical control model is bypassed and its integral link is frozen, and the signal input of the wind turbine model is switched to the high-low ride-through control model.

[0151] (6) In the high voltage ride-through state, if the terminal voltage is lower than the high voltage ride-through threshold value V hvrt_th , then enter the high voltage ride through recovery starting state. If the high voltage ride through recovery starting time relay delay time T hvrt_delay When it is 0, it means that there is no low-pressure ride-through recovery starting state, and it will directly enter the high-pressure ride-through recovery state.

[0152] (7) When the high voltage ride-through is restored to the starting state, if the terminal voltage is higher than the high voltage ride-through threshold value V hvrt_th , then it returns to the high voltage ride-through state. When entering the high voltage ride-through recovery starting point state, the high voltage ride-through recovery starting point time relay starts timing, T lvrt_delay It will then enter a high-pressure crossing recovery state.

[0153] (8) In the high voltage ride through recovery state, if the terminal voltage is higher than the high voltage ride through threshold value V hvrt_th , then it returns to the high voltage ride-through state. When the active current instruction I pcmd Restore to the active current instruction before entering the high voltage ride-through state and the reactive current instruction I qcmdWhen the reactive current instruction is restored to the level before entering the high-voltage ride-through state, the system enters the normal state. At this time, the electrical control model cancels the bypass and integrates normally, and the signal input of the wind turbine model is switched to the electrical control model.

[0154] S3-1-4. Construction of general protection model

[0155] Some equipment grid connection guidelines stipulate that the equipment cannot be offline if the system fault is not serious enough to exceed the boundary limit and tolerance time limit. However, the boundary condition limit and tolerance time limit will vary with different engineering projects. Therefore, user-defined boundary curves are supported, such as Figure 8 shown.

[0156] The threshold for entering the low voltage ride-through state is generally set as the maximum value of the low voltage boundary protection curve. To align with the high and low voltage ride-through control model, the maximum (minimum) value of the device's low voltage (high voltage) boundary curve can be used as the low voltage ride-through threshold (high voltage ride-through threshold) of the high and low voltage ride-through control model. When the voltage falls below this threshold, the zero time of the low voltage boundary protection curve automatically aligns with the time when the voltage falls below the threshold. If the voltage reaches the boundary protection curve, the generator tripping protection relay will begin timing and will officially trip the generator after a certain delay. Once the generator tripping protection relay starts timing, it cannot be reversed. Proportional generator tripping is supported. If the generator tripping ratio is 1, all units will be tripped. If the generator tripping ratio is 0, no action is taken.

[0157] S3-2. Construction of wind power generation system model

[0158] There are three main types of wind turbines: constant speed, doubly fed, and direct drive. Although constant speed wind turbines were put into operation earlier, they have low efficiency, poor power regulation capability, and their market share is gradually declining. Therefore, the constant speed wind turbine model is not considered.

[0159] S3-2-1. Aerodynamic model construction

[0160] The aerodynamic model is used to simulate the characteristics of the fan converting wind energy into mechanical energy. In addition to calculating the mechanical power drawn by the fan impeller, it can also provide a speed reference value ω according to the control strategy. ref The aerodynamic model also calculates the initial values ​​of the pitch angle and rotor speed so that the mechanical power provided by the wind turbine is equal to the generated power calculated by the power flow. The aerodynamic model supports both linear and detailed models.

[0161] The linear model is a simplified model that can be used when faster simulation speeds are desired. For power system simulations involving grid disturbances, it is reasonable to assume that the wind speed remains constant for 5 to 30 seconds. For constant wind speed, the power change rate dP / dβ has an approximately linear relationship with the pitch angle β. Therefore, the calculation of the mechanical power output of the wind turbine can be significantly simplified using the following expression:

[0162] P mech =P mech0 -K a β(β-β0)(10)

[0163] Among them, P mech0 is the initial value of the wind wheel mechanical power, K a is the aerodynamic coefficient, and β0 is the initial pitch angle.

[0164] The speed reference value is obtained based on the maximum power tracking characteristics of the wind turbine. The program uses the typical fitting formula of power-speed engineering:

[0165]

[0166] Among them, a, b, and c are power-speed fitting coefficients.

[0167] The detailed model involves the precise calculation of the Cp function curve, which is shown in the following formula:

[0168]

[0169] Where λ is the tip speed ratio, c1 to c8 are fitting coefficients, L is the intermediate coefficient, and e is a natural constant.

[0170] It can support operation in three active standby modes: maximum power point tracking, underspeed, and overspeed. Figure 9 The maximum power point is solved by the derivative increment method, but it can only give a rough interval of the operating point. Therefore, a more accurate solution can be found by further using the dichotomy method, which can ensure that the calculation accuracy is within 10-10. The specific process is as follows: Figure 9 Therefore, the simulation of the detailed model is very accurate, but its solution is a very time-consuming process.

[0171] The three active power standby modes are introduced as follows:

[0172] (1) Maximum power point tracking mode

[0173] According to the current wind speed V w and C corresponding to the pitch angle β p Curve, find the impeller speed at the maximum power point of the curve as the speed reference value ω ref As shown in the following formula:

[0174]

[0175] (2) Underspeed operation mode

[0176] According to the wind speed V w and C corresponding to the pitch angle β p Curve, at the C p Find an impeller speed P within the range to the left of the maximum power point of the curve m , so that the wind turbine extracts mechanical power P from the wind m and rotor braking power P mech Equal, and use this impeller speed as the speed reference value ω ref If the mechanical power absorbed at the maximum power point is less than the rotor braking power, the wind turbine switches to the maximum power point tracking mode, that is, the speed corresponding to the maximum power point is used as the speed reference value ω ref , when the mechanical power absorbed at the maximum power point is greater than the rotor braking power, it returns to the underspeed operation mode. As shown in the following formula:

[0177]

[0178] Speed ​​ω ref Affected by the maximum impeller speed ω max Or minimum impeller speed ω min The restrictions are as follows:

[0179]

[0180] (3) Overspeed operation mode

[0181] The dynamic process of overspeed operation mode is similar to that of underspeed operation mode, except that ω operates at C p Maximum power point ω MPPT The right side will not be described here.

[0182] S3-2-2. Construction of pitch angle control model

[0183] The pitch angle control model generates a pitch angle signal based on the speed and reference signal, as well as the power and reference value signal. This signal is then input into the aerodynamic model to achieve mechanical power control of the wind turbine. If a pitch angle control model does not exist, a fixed-pitch wind turbine can be simulated.

[0184] S3-2-3. Mechanical system model construction

[0185] The transmission chain model adopts a typical dual-mass model, which simulates the wind turbine blades and the generator rotor as an inertial body respectively, and takes into account the flexibility and damping of the transmission shaft.

[0186] S3-2-4. Wind speed model construction

[0187] The wind speed model generates a wind speed signal and inputs it into the aerodynamic model. This is achieved by reading a wind speed file. The wind speed values ​​at different time points can be set in the file, so theoretically any wind type can be simulated.

[0188] S3-3. Construction of photovoltaic power generation system model

[0189] Photovoltaic power generation system models can be divided into single-stage and multi-stage topologies according to the number of intermediate converters used. Currently, only the single-stage grid-connected photovoltaic power station model, which is widely used, is considered.

[0190] S3-3-1. Photovoltaic array model construction

[0191] The photovoltaic array model is used to simulate the characteristics of photovoltaic panels converting light energy into electrical energy. It supports both simple and complex models.

[0192] The simple model uses a general engineering calculation formula to calculate the output power P of the photovoltaic panel pv , and according to the given power reserve factor K rp This function realizes power reserve regardless of temperature. As shown in the following formula:

[0193]

[0194] Among them, S is the light intensity, S ref is the reference value of light intensity, P msta is the maximum power under standard test conditions, and b is a constant related to battery materials.

[0195] The detailed model simulates the volt-ampere characteristics of the photovoltaic panel. In addition to calculating the output power of the photovoltaic panel, it can also provide a DC voltage reference value U according to the control strategy. dcref During the initialization phase, the PV array model is responsible for calculating the initial DC voltage and can infer the initial light intensity based on the PV power flow results.

[0196] The photovoltaic characteristics of this model require the specification of the short-circuit current I sc , open circuit voltage V oc , load current I at maximum power point m and load voltage V m The four technical parameters are then converted under the actual temperature T and light intensity S, and the formula is:

[0197]

[0198] The output current of the photovoltaic panel is:

[0199]

[0200] Similar to the detailed aerodynamic model, the PV panel can be operated in three standby modes: maximum power point, overvoltage, and undervoltage by controlling the DC voltage of the capacitor. The calculation method for each operating point is similar to that used in the wind turbine aerodynamic model. Simply replace the rotor's Cp function with the PV panel's volt-ampere characteristic function, and replace the independent variables pitch angle and tip speed ratio with light intensity and DC voltage, respectively. This will not be further detailed here.

[0201] S3-3-2. Capacitor model construction

[0202] The capacitance model only characterizes the charging and discharging rules of the capacitor, and its calculation formula is as follows:

[0203]

[0204] Where C is the capacitance, V dc is the capacitor voltage.

[0205] S3-3-3. Construction of illumination model and temperature model

[0206] In order to simulate various environmental conditions, the light and temperature are obtained by reading files. When the model does not exist, the light and temperature remain unchanged at the initial values.

[0207] S3-4. Energy storage system model construction

[0208] Only the two most widely used energy storage types, electrochemical energy storage and pumped storage, are considered. Both charging and discharging operations are supported. In the discharge mode, all physical quantities such as speed and power are positive, while in the charging mode they are negative.

[0209] S3-4-1. One-time model construction

[0210] The primary model of electrochemical energy storage mainly consists of electrochemical body model and DC voltage control. Figure 11 The model shown, where E oc is the potential inside the battery, close to the static voltage; R b is the ohmic resistance of the battery; R p 、C p The polarization resistance and capacitance of the battery are used to describe the entire polarization characteristics. It can simulate single-stage / double-stage electrochemical energy storage control, and the presence and absence of DC / DC circuits, such as Figure 12 When the DC / DC circuit exists, the DC capacitor voltage will be controlled by the DC / DC circuit to the rated value V dcN ,When the DC / DC circuit does not exist, the DC capacitor voltage response system changes and is not a constant value.

[0211] like Figure 13As shown in Figure 1, the primary model of a pumped storage system consists of a regulator, a servo mechanism, and a prime mover. The regulator typically uses a PI controller, which offers various control modes, including power control, speed control, and opening control. It generates an opening signal based on system requirements and transmits it to the servo mechanism. The servo mechanism, which can be simulated using a first-order inertia link, receives the opening signal and drives the guide vanes to generate the desired mechanical energy from the turbine.

[0212] S3-4-2. Energy buffer model construction

[0213] like Figure 14 As shown in Figure 1, if electrochemical energy storage is used in the energy storage system, the energy buffer model is simulated as a capacitor. If pumped hydropower storage is used, the energy buffer model is simulated as a rotor. When simulated as a capacitor, the equation is as shown in Equation (19). When simulated as a rotor, the equation is the rotor motion equation. In the figure, H is the rotor's moment of inertia.

[0214] In actual simulations, the capacitor voltage is always positive, while the speed fluctuates between positive and negative due to the energy storage system's charge and discharge states. When the speed is near zero, the speed is in the denominator, which can lead to inaccurate simulations. Therefore, when the speed decreases to a certain value (specified in the program as 0.001 pu), the rotor is directly locked, with the speed at zero and no integral operation. When the rotor starts, it increases directly from 0.001 pu during discharge and from -0.001 pu during charge. The D coefficient simulates the effect of braking torque. During shutdown, the braking torque only takes effect when the mechanical power and output power reach zero before the speed, effectively ensuring that the speed eventually decays to zero.

[0215] S3-4-3. Energy state model construction

[0216] The energy state model is a simplified abstract model, such as Figure 15 As shown in the figure, it can simulate the energy state of various forms of energy storage. The energy state ranges from 0 to 1, where 1 represents that the stored energy has reached the maximum storage capacity of the energy storage system. At this time, charging is not possible and only discharging is allowed. 0 represents that the energy storage system has no energy storage. At this time, discharging is not possible and only charging is allowed. out is the discharge efficiency, K in is the charging efficiency, T total The rated discharge time is the time it takes for the energy state value to decrease from 1 to 0 when the energy storage system is discharged at the rated power. Therefore, the total energy that can be stored in the energy storage system can be calculated using the rated discharge time. The formula is:

[0217] E total =M base ×T total ×1000(20)

[0218] Among them, M base is the benchmark capacity of the energy storage system, in MVA, E total It is the total energy that can be stored in the energy storage system, in kJ.

[0219] S3-4-4. Auxiliary model construction

[0220] On the one hand, the auxiliary model simulates the switching logic of the charging and discharging conditions, and on the other hand, it needs to simulate the internal physical state changes and external characteristics of the energy storage system during the switching process; electrochemical energy storage has a fast response speed, and the present invention believes that it can complete the switching instantaneously without the need for an auxiliary model.

[0221] (1) Working condition switching logic.

[0222] This embodiment proposes 7 energy storage charge and discharge switching conditions, and supports automatic or manual switching. Figure 16 The switching process is primarily controlled by the grid's active power dispatch command value and energy status. In automatic control mode, the active power dispatch command value tracks the output of the frequency control module. In manual control mode, it can be set via a specified interface. When the power output or absorption of the unit falls below the minimum value within the normal operating range, the unit will not start based on efficiency. When the energy status value is too low or too high, the energy storage system will automatically be controlled to enter a no-load condition.

[0223] (2) Characteristic simulation of the switching process. The auxiliary model provides the power reference value Pref and the speed reference value ω to other models. ref , opening reference value Y ref The primary model or electrical control model receives and responds to these signals. During charging or discharging conditions, they are equal to the dispatch command values, but during switching, they transition to the dispatch command values. During shutdown, when the mechanical power and active power command values ​​decrease to 0, the active power circuits of the primary model and electrical control model are reinitialized to 0.

[0224] After the above steps, a modular model of energy storage was constructed. Through the primary model of electrochemical / pumped storage, energy buffer model and charge-discharge switching logic, flexible modeling of the energy state and operating condition conversion of the energy storage system was achieved.

[0225] S3-5. Based on the module selection strategy of key wind, solar and energy storage equipment in the power system or user customization, modular sub-models corresponding to wind turbines, photovoltaics and energy storage equipment are constructed respectively, and dynamically combined to obtain modeling results.

[0226] In this embodiment, the universal model of the wind, solar and storage device can be separately modeled and encapsulated, converted into an independent, reusable universal model, and can be converted into an instantiated object as a class member variable in different device models.

[0227] Therefore, users can freely choose specific models to use under each model type, thereby achieving flexible module combinations. When expanding models, it is only necessary to develop specific models with specific functions under a certain model type, avoiding the need to redevelop a full set of models. In the general model part, developers only need to worry about the input and output of the general model, and no longer need to repeatedly process the complex logic within the model. If the model logic needs to be adjusted, only the shared model needs to be modified, which can achieve simultaneous modification of wind, solar and storage device models, which significantly saves model development resources and time.

[0228] S3-5-1. Based on the module selection strategy for identifying key wind, solar and storage equipment in the power system, select modules for modeling.

[0229] For power system simulation, the use of detailed models can usually produce more accurate simulation results. However, the introduction of too many detailed models for simulation requires a large amount of calculation and takes a long time. On the contrary, the use of simple models can reduce the amount of simulation calculations, but it will sacrifice the accuracy of the simulation. In order to take into account both the efficiency and accuracy of power system simulation, it is crucial to select a model with an appropriate degree of complexity. The module selection strategy proposed in the present invention improves the PageRank algorithm through the power distribution analysis model to achieve accurate identification of key wind, solar and storage equipment in the power system, solving the contradiction between "low efficiency of detailed modeling of the entire system" and "insufficient accuracy of fully simplified modeling" in traditional modeling, and realizing modular selection and adaptation of wind, solar and storage models. This strategy not only improves the simulation efficiency, but also ensures simulation accuracy by dynamically matching the complexity of the model.

[0230] The PageRank algorithm can solve the problem of sorting important nodes in the network and has been widely used in various fields. The traditional PageRank algorithm only distributes link weights based on the average of node out-degree, does not integrate the power flow characteristics of the power system, and cannot reflect the actual contribution of the equipment to the power flow distribution. The algorithm represents the entire network as a directed graph, and the node-associated edges represent the links between the nodes. The importance of a node in this algorithm is measured by the PR value, which is determined by the PR value of the linked node and its own PR value. In the PageRank algorithm, the calculation of the PR value is an iterative process. The formula is:

[0231]

[0232] Among them, j∈i is the set pointing to node i; L(j) is the number of nodes linked out of node j; n is the number of all nodes in the network; σ is the algorithm damping coefficient, which is used to ensure the convergence of the algorithm.

[0233] like Figure 3 As shown in the figure, the module selection strategy for key wind, solar and energy storage equipment is as follows:

[0234] (1) The power flow distribution of the system is obtained through power system flow calculation.

[0235] (2) Based on the system power flow distribution, the PR value of each power node is calculated using the improved PageRank algorithm.

[0236] For a network of n nodes, each node has an intrinsic importance of (1-σ) / n. Node j linked to i will contribute to the importance of PR(j), and this importance is shared by all nodes linked out of node j. After iterative calculation, the PR value of each node can be obtained, which tends to converge. The larger the PR value of the node, the more important the node is in the network. However, the PageRank algorithm distributes the link weights of the model during the iteration process evenly according to the node out-degree, without considering the importance of each node itself. For power systems, the distribution of node link weights should be determined by the actual line flow conditions. Therefore, it is necessary to consider the contribution of the active output of power generation equipment such as wind, solar, and storage to the line flow conditions in the system. Since the difference between the detailed model and the simple model in the wind, solar, and storage model is mainly related to active power, only the active flow is designed here.

[0237] The specific process is:

[0238] 1) Based on the system power flow distribution, a downstream distribution matrix considering active power is constructed and the distribution coefficient matrix is ​​calculated.

[0239] Let A=(a ij ) n×n is the downstream distribution matrix of an n-node system, denoted by P L(i,j) is the active power transmitted from node i to node j, then the construction formula of the downstream distribution matrix between nodes is:

[0240]

[0241] Where i, j = 1, 2, ..., n; is the total injected active power of node j.

[0242] According to the power distribution analytical model algorithm, the distribution coefficient matrix of power source i flowing to the line is:

[0243] K=(k ij ) n×n =ET (P GG ) -1 A -1 (twenty three)

[0244] Where E is the identity matrix; is the active power output of the power supply on node k,

[0245] 2) Based on the distribution coefficient matrix, the power distribution of the power flow line and the link weights between each power node are calculated.

[0246] Computing Power G k Power distribution to line L(i,j) and the link weight ω between each node L (i,j), the specific process is:

[0247] Calculate the power distribution of the power supply to the line, the formula is:

[0248]

[0249] in, Power supply G k Power distribution to line L(i,j).

[0250] Determine the importance of a node based on its power output and define the power importance weight. The formula is:

[0251]

[0252] Among them, ∑P G is the total power generation of the system.

[0253] Define the link strength ω between system nodes based on the device importance weight and power flow distribution L (i, j), to represent the link weight between each node, the formula is:

[0254]

[0255] 3) Based on the link weights between the power nodes, the PR value of each power node is obtained by iterative calculation.

[0256] Since the power flow components of the lines with larger link weights are more likely to come from more important power sources, the algorithm is used to calculate the PR value of each power node iteratively as follows:

[0257]

[0258] Among them, S(j) is the sum of all outbound weights of node j,

[0259] The node PR value obtained after iterative convergence of formula (27) comprehensively considers the system topology relationship and the contribution of power output to the link weight, and can well reflect the impact of power equipment on the system.

[0260] (3) Choose to use a detailed model or a simplified model for modeling based on the PR value of each power node.

[0261] After the above steps, by accurately identifying the key wind, solar and storage equipment in the power system, the efficiency and accuracy of the power system simulation are taken into account, and the modular selection and adaptation of the wind, solar and storage model is realized.

[0262] S3-5-2. User-defined modeling

[0263] In addition to allowing users to select a specific model to use under a certain model type, it also supports user-defined modular modeling, thereby achieving flexible expansion of modules.

[0264] The steps for user-defined modeling are as follows:

[0265] (1) Select the required basic links according to the transfer function block diagram of the user-defined model. The basic links include the first-order inertia link, integral link, proportional-integral link, proportional-integral-differential link, saturation link, etc.

[0266] (2) Declare the definition of the user-defined model in a header file. This header file should be placed in the corresponding dynamic model category folder under the header / model directory in the STEPS project.

[0267] (3) Implement the user-defined model. The implementation file should be created in the corresponding dynamic model category folder under the source / model directory of the STEPS project. All functions defined in the header file must be implemented here. The two most core functions are initialize and run. The initialize function is used to initialize the dynamic simulation, that is, to reversely deduce the various variables in the model based on the flow results to obtain the initial state of each module. The run function is used to calculate the integral during the dynamic process and update the output.

[0268] (4) Design the input and output parameter data format of the model, and update the input and output parameter data format and interface of the user-defined model.

[0269] The proposed modular modeling framework has been implemented in STEPS, and specific models have been integrated into STEPS's dynamic model database. For each model type, users can select a specific model based on their simulation needs and input a specific data format to the program to complete the model creation. Alternatively, they can follow the above process to create a custom model within the established modular modeling framework.

[0270] After the above steps, the rapid development of personalized models based on basic links was achieved, which significantly improved the openness of the modular modeling platform and the user's independent expansion capabilities.

[0271] S3-6. Verify the constructed model

[0272] To improve the reliability and usability of modular modeling, the following model verification functions are designed. Model verification generates model warning or error logs to ensure the accuracy of model construction and resource utilization efficiency. Specifically, they include:

[0273] (1) Model integrity check

[0274] Model integrity checking is used to identify missing or redundant models. Missing models can cause inaccurate simulation results. While model redundancy does not affect simulation results, its presence in large-scale system simulations can waste significant memory resources. For example, if the primary model of an energy storage system is electrochemical, auxiliary models are unnecessary. If the photovoltaic array model is simple, a capacitor model is not necessary.

[0275] (2) Model matching check

[0276] The model must match the physical characteristics of the device. For energy storage power generation systems, when simulating electrochemical energy storage, the energy buffer model must use a capacitor model; when simulating pumped storage systems, the energy buffer model must use a rotor model.

[0277] (3) Control mode matching check

[0278] Control mode matching checks ensure that the control logic matches the device's operating characteristics. For example, speed control in the electrical control model is applicable only to wind power generation systems and pumped storage systems; DC voltage control is applicable to photovoltaic power generation systems and electrochemical energy storage systems. When the photovoltaic array model in a photovoltaic power generation system uses a simple model, the electrical control model must use power control; when a complex model is used, the electrical control model must use DC voltage control. The auxiliary model of pumped storage requires both speed control and power control in both the primary model and the electrical control model; otherwise, the switching operation will not be completed smoothly.

[0279] In this embodiment, based on the energy form decoupling theory, the wind, solar and storage dynamic model is modularly decomposed and reconstructed, and a hierarchical integrated architecture is established in the STEPS simulation platform; the flow data is imported into STEPS and flow calculations are performed to determine the active flow distribution in the system; the key wind, solar and storage devices of the power system are identified according to the improved PageRank algorithm, and the model is modularly selected or the user performs customized modeling in STEPS according to simulation requirements; the dynamic model data is imported into the dynamic model database of STEPS, and the model is checked and verified to ensure the reliability of the model.

[0280] After modeling, the modular wind, solar, and storage modeling method in this embodiment can be applied to the flexible modeling and efficient simulation of new power systems, better adapting to the development needs of new power systems and providing modeling and simulation technical support for the study of electromechanical transient characteristics of new power systems. By modularly combining different new energy models, it is possible to assess the additional regulation capacity required at a specific penetration rate. An aggregation model of millions of electric vehicles can be established to achieve vehicle interaction. It can also be applied to virtual power plants, increasing the benefits of participating in the peak-shaving market. Current modeling methods are no longer able to adapt to the development needs of new power systems.

[0281] Example 2

[0282] The purpose of this embodiment is to provide a modular modeling system for wind, solar, and storage systems for electromechanical transients in power systems, including:

[0283] The modular decomposition and reconstruction module is used to modularly decompose and reconstruct the wind, solar and energy storage dynamic model based on energy form decoupling, breaking it down into three parts: energy conversion and control, energy buffering, and energy output and control.

[0284] Hierarchical integration architecture building module, used to build a hierarchical integration architecture based on three links;

[0285] The modeling module is used to build modular models corresponding to wind turbines, photovoltaics and energy storage equipment based on a hierarchical integrated architecture, and dynamically combine them to obtain modeling results. The specific process in modeling is as follows: building a general model of wind, photovoltaics and energy storage; selecting modules for modeling based on the module selection strategy of key wind, photovoltaics and energy storage equipment in the power system; and modeling through user-defined modules.

[0286] The method steps in Example 1 are implemented based on providing a wind, solar and storage modular modeling system for electromechanical transients of power systems.

[0287] Example 3

[0288] The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0289] Example 4

[0290] The purpose of this embodiment is to provide a computer-readable storage medium.

[0291] A computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the above method.

[0292] Example 5

[0293] The purpose of this embodiment is to provide a computer program product containing instructions, which, when running on a computer, enables the computer to execute the methods and functions involved in any of the above embodiments.

[0294] The steps involved in the apparatus of the above embodiment correspond to those of the method embodiment 1. For detailed implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0295] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0296] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A modular modeling method for wind, solar and storage systems for electromechanical transients in power systems, characterized by: include: Based on energy form decoupling, the wind, solar and energy storage dynamic model is modularly decomposed and reconstructed into three parts: energy conversion and control, energy buffering, and energy output and control. Build a hierarchical integration architecture based on three links; Based on the hierarchical integrated architecture, modular models corresponding to wind turbines, photovoltaics, and energy storage equipment are constructed separately and dynamically combined to obtain modeling results; Among them, in the modeling, the specific process is: constructing a general model of wind, solar and energy storage; According to the module selection strategy of key wind, solar and energy storage equipment in the power system, modules are selected for modeling; Modeling is done through user-defined modules.

2. The wind, solar and storage modular modeling method for electromechanical transients of power systems according to claim 1 is characterized in that: The energy conversion and control steps include: Wind speed model, aerodynamic model and pitch angle control model of wind power generation system; Irradiation model, temperature model and photovoltaic array model of photovoltaic power generation system; Pure electrochemical model or pumped storage model of energy storage system.

3. The wind, solar and storage modular modeling method for electromechanical transients of power systems according to claim 1 is characterized in that: Energy buffering includes: Mechanical system model of wind power generation system; DC capacitor modules for photovoltaic power generation systems; Capacitor model or mechanical rotor model of the energy storage system.

4. The wind, solar and storage modular modeling method for electromechanical transients of power systems according to claim 1 is characterized in that: The energy output and control links include: generator / converter model, electrical control model, high and low penetration control model, and protection model.

5. The wind, solar and storage modular modeling method for electromechanical transients of power systems according to claim 1 is characterized in that: Construct a hierarchical integration architecture. Specifically, in the STEPS simulation platform of the power system simulation toolkit, establish a hierarchical integration architecture of the device type layer, model type layer and specific model layer.

6. The wind, solar and storage modular modeling method for electromechanical transients of power systems according to claim 1 is characterized in that: The module selection strategy for key wind, solar and energy storage equipment is as follows: Through the power system flow calculation, the system power flow distribution is obtained; Based on the system power flow distribution, the PR value of each power node is calculated using the improved PageRank algorithm; Choose to use a detailed model or a simplified model for modeling based on the PR value of each power node.

7. The wind, solar and storage modular modeling method for electromechanical transients of power systems according to claim 6 is characterized in that: The PR value of each power node is calculated using the improved PageRank algorithm. The specific process is as follows: Based on the system power flow distribution, a downstream distribution matrix considering active power is constructed and the distribution coefficient matrix is ​​calculated; Based on the allocation coefficient matrix, the power distribution of the power flow lines and the link weights between each power node are calculated; Based on the link weights between the power nodes, the PR value of each power node is obtained by iterative calculation.

8. A modular modeling system for wind, solar and storage systems for electromechanical transients in power systems, characterized by: include: The modular decomposition and reconstruction module is used to modularly decompose and reconstruct the wind, solar and energy storage dynamic model based on energy form decoupling, breaking it down into three parts: energy conversion and control, energy buffering, and energy output and control. Hierarchical integration architecture building module, used to build a hierarchical integration architecture based on three links; The modeling module is used to build modular models corresponding to wind turbines, photovoltaics and energy storage equipment based on a hierarchical integrated architecture, and dynamically combine them to obtain modeling results. The specific process in modeling is as follows: building a general model of wind, photovoltaics and energy storage; selecting modules for modeling based on the module selection strategy of key wind, photovoltaics and energy storage equipment in the power system; and modeling through user-defined modules.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are performed.

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