Wind, light and storage modular modeling method and system for electromechanical transient of power system

Abstract

The application provides a wind-solar-storage modular modeling method and system for electromechanical transient of a power system, relates to the technical field of power system simulation, and aims at the problem that the prior art cannot meet the needs of users for model modular recombination and customized adaptation, has low model reuse rate and poor flexibility, and has not formed a unified modeling standard system, so that it is difficult to adapt to the development needs of new power systems.The application comprises the following steps: based on energy form decoupling, modular decomposition and reconstruction are carried out on a wind-solar-storage dynamic model, the wind-solar-storage dynamic model is decomposed into three links of energy conversion and control, energy buffering, and energy output and control, and a hierarchical integrated architecture is constructed; based on the hierarchical integrated architecture, modular submodels corresponding to wind turbines, photovoltaic devices and energy storage devices are constructed, and dynamic combination is carried out, so that a modeling result is obtained.The application solves the problems in the prior art, improves the model reuse rate and flexibility, and improves the model simulation precision and efficiency.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power system simulation, and particularly relates to a wind-solar-storage modular modeling method and system for power system electromechanical transient. BACKGROUND

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

[0003] The proportion of new energy represented by wind turbines, photovoltaic and energy storage in the power system is increasing, and the structure and dynamic characteristics of the power system are becoming more and more complex. Electromechanical transient modeling is the basis for power system stability and dynamic simulation analysis. However, different wind-solar-storage equipment manufacturers have significant differences in controller structure, control strategy, etc. The traditional modeling method has been difficult to adapt to the complex, fragmented and heterogeneous characteristics of the dynamic model of wind-solar-storage equipment, and it is urgent to realize the transformation to a modular flexible modeling architecture through innovative modeling methods.

[0004] At present, the research on wind-solar-storage electromechanical transient modeling of power system mainly focuses on various power system electromechanical transient simulation software. The current mainstream power system simulation software, including PSASP developed by China Electric Power Research Institute, PSD-BPA and PSS / E of American PTI company, etc. After years of development, these power system simulation software at present, although a relatively complete wind-solar-storage model library has been established, the integrated models have been packaged, and a relatively complete wind-solar-storage modeling system has been established. However, these models generally use "black box" packaging, and there is strong coupling between the functional modules of the integrated wind-solar-storage model, which makes it difficult for users to modularize and customize the model according to actual simulation requirements, the model reuse rate is low and the flexibility of simulation is limited.

[0005] In addition, there is no unified modeling standard system at present, and there are significant differences in model interface, parameter definition, model packaging and other key technical links among various power system simulation software, which seriously hinders cross-platform model sharing and collaboration, resulting in the need for a large amount of adaptation and modification for cross-software model reuse, significantly increasing the time and cost of simulation research. The current modeling method has been difficult to adapt to the development needs of new power systems. SUMMARY

[0006] To overcome the above deficiencies of the prior art, the application provides a wind-solar-storage modular modeling method and system for electromechanical transient of a power system, a modular modeling system is constructed based on energy form decoupling, dynamic recombination modeling of modular components is realized, modeling flexibility is improved, a general model is constructed, model sharing and cooperation are realized, the accuracy and efficiency of the model in simulation are considered through a model selection strategy, a hierarchical integrated architecture is constructed, user-defined modeling and model verification functions are provided, the scalability and reliability of the modular modeling system are improved, and the development needs of a new power system can be met.

[0007] To achieve the above object, one or more embodiments of the application provide the following technical solutions:

[0008] A first aspect of the application provides a wind-solar-storage modular modeling method for electromechanical transient of a power system, comprising:

[0009] Based on energy form decoupling, wind-solar-storage dynamic models are modularly decomposed and reconstructed into three links of energy conversion and control, energy buffering, and energy output and control;

[0010] Based on the three links, a hierarchical integrated architecture is constructed;

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

[0012] In the modeling, the specific process is as follows: a wind-solar-storage general model is constructed;

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

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

[0015] As an implementation mode, the energy conversion and control link comprises:

[0016] A wind speed model, an aerodynamics model, and a pitch angle control model of a wind power system;

[0017] An irradiation model, a temperature model, and a photovoltaic array model of a photovoltaic power system;

[0018] An electrochemical pure model or a pumped storage model of an energy storage system.

[0019] As an implementation mode, the energy buffering link comprises:

[0020] A mechanical system model of a wind power system;

[0021] A direct current capacitor module of a photovoltaic power system;

[0022] A capacitor model or a mechanical rotor model of the energy storage system.

[0023] As an implementation form, the energy output and control link includes a generator / rectifier model, an electrical control model, a high-low pass control model and a protection model.

[0024] As an implementation form, a hierarchical integrated architecture is constructed, specifically, in a power system simulation tool package (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 form, a module selection strategy of key wind-solar energy storage equipment is specifically as follows:

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

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

[0028] According to the PR value of each power supply node, a detailed model or a simplified model is selected for modeling.

[0029] As an implementation form, the PR value of each power supply node is calculated through an improved PageRank algorithm, and the specific process is as follows:

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

[0031] Based on the distribution coefficient matrix, the power distribution of the power supply flow line and the link weight between each power supply node are calculated;

[0032] Based on the link weight between each power supply node, the PR value of each power supply node is iteratively calculated.

[0033] The second aspect of the present application provides a wind-solar energy storage modular modeling system for power system electromechanical transient, which comprises:

[0034] The modular decomposition and reconstruction module is used for modular decomposition and reconstruction of the wind-solar energy storage dynamic model based on energy form decoupling, and the wind-solar energy storage dynamic model is decomposed into three links of energy conversion and control, energy buffer and energy output and control.

[0035] The hierarchical integrated architecture construction module is used for constructing a hierarchical integrated architecture based on the three links.

[0036] The modeling module is used for constructing modularized models corresponding to the wind turbine, the photovoltaic device and the energy storage device respectively based on a hierarchical integrated architecture, and dynamically combining to obtain a modeling result; wherein, in the modeling, the specific process is: constructing a general wind-solar-storage model; selecting modules for modeling according to a module selection strategy of key wind-solar-storage devices of the power system; and modeling through user-defined modules.

[0037] The third aspect of the present application provides a computer device, comprising a memory, a processor and a program stored in the memory and executable on the processor, wherein the processor implements the steps in the method of the first aspect of the present application when executing the program.

[0038] The fourth aspect of the present application aims to provide a computer readable storage medium having a program stored thereon, wherein the program is executable by a processor to implement the steps in the method of the first aspect of the present application.

[0039] The above one or more technical solutions have the following beneficial effects:

[0040] In the embodiment, for the wind-solar-storage device modeling with the characteristics of complexity, fragmentation and heterogeneity, the energy form decoupling modularized modeling, the module selection strategy of key wind-solar-storage devices and the hierarchical integrated architecture are combined to establish a standardized and easily expandable modularized modeling system and framework, which can realize "plug and play" and collaborative sharing, so that users can modularly recombine and customize the model according to actual simulation requirements, improve the model reuse rate and simulation flexibility, and further meet the development needs of new power systems.

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

[0042] In the embodiment, by constructing the general models of the converter, electrical control, high-low penetration control and protection control, model sharing and collaboration among wind-solar-storage models are realized, and the modeling and simulation efficiency is improved.

[0043] In the embodiment, based on the module selection strategy of key wind-solar-storage devices of the power system, i.e., combining the power system flow distribution and the PageRank algorithm, the accuracy of module selection in modeling is improved, while the accuracy and efficiency of the model in simulation are taken into account.

[0044] In the embodiment, in the simulation tool package STEPS simulation platform of the power system, the hierarchical integrated architecture is constructed and equipped with user-defined modeling and model verification functions, which improves the expandability and reliability of the modularized modeling system.

[0045] Advantages of the additional aspects of the application will become apparent in the following description, which is given by way of example only, from the description of the drawings, or from the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0046] The accompanying drawings, which form a part of this specification, are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification. The embodiments of the application, together with their

[0047] Figure 1 Flow chart of wind-solar-storage modular modeling method for power system electromechanical transient of embodiment one of the present application;

[0048] Figure 2 Modular modeling schematic diagram based on energy form decoupling of embodiment one of the present application;

[0049] Figure 3 Modular selection strategy flow chart of embodiment one of the present application;

[0050] Figure 4 Active control part schematic diagram of general electric control model of embodiment one of the present application;

[0051] Figure 5 Frequency control part schematic diagram in general electric control model of embodiment one of the present application;

[0052] Figure 6 Reactive control part schematic diagram of general electric model of embodiment one of the present application;

[0053] Figure 7 Crossing state schematic diagram of general high-low crossing control model of embodiment one of the present application;

[0054] Figure 8 Protection boundary curve schematic diagram of general protection model of embodiment one of the present application;

[0055] Wherein, figure (a) is a protection boundary curve schematic diagram of general protection model in low voltage crossing state, figure (b) is a protection boundary curve schematic diagram of general protection model in high voltage crossing state;

[0056] Figure 9 Three active reserve operation mode schematic diagram of wind turbine aerodynamics model of embodiment one of the present application;

[0057] Figure 10 Derivative increment method flow chart for solving maximum power point of wind turbine aerodynamics model of embodiment one of the present application;

[0058] Figure 11 Electrochemical energy storage system battery body model schematic diagram of embodiment one of the present application;

[0059] Figure 12 Figure 1 is a single / two-stage electrochemical energy storage control structure schematic diagram of the embodiment one of the present application;

[0060] Figure 1(a) is a single-stage electrochemical energy storage control structure schematic diagram, and Figure 1(b) is a two-stage electrochemical energy storage control structure schematic diagram;

[0061] Figure 13 Figure 2 is a primary model schematic diagram of pumped storage of the embodiment one of the present application;

[0062] Figure 14 Figure 3 is a rotor model schematic diagram of the energy storage system of the embodiment one of the present application;

[0063] Figure 15 Figure 4 is an energy state model schematic diagram of the energy storage system of the embodiment one of the present application;

[0064] Figure 16 Figure 5 is a charge and discharge working condition conversion schematic diagram of the energy storage system of the embodiment one of the present application;

[0065] Figure 17 Figure 6 is a modular modeling simulation platform STEPS program architecture schematic diagram of the embodiment one of the present application;

[0066] Figure 18 Figure 7 is a hierarchical integration architecture schematic diagram of the embodiment one of the present application. DETAILED DESCRIPTION

[0067] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

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

[0069] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0070] Embodiment one

[0071] The present embodiment discloses a wind-solar-storage modular modeling method for power system electromechanical transient.

[0072] In order to more clearly set forth the present embodiment, the wind-solar-storage modular modeling implementation process for power system electromechanical transient can be specifically described as follows:

[0073] The wind-solar-storage modular modeling method for power system electromechanical transient comprises:

[0074] S1, based on energy form decoupling, the wind light storage dynamic model is modularly decomposed and reconstructed, which is divided into energy conversion and control, energy buffer, energy output and control three links;

[0075] S2, based on the three links, a hierarchical integrated architecture is constructed;

[0076] S3, based on the hierarchical integrated architecture, the corresponding modular model of the wind turbine, photovoltaic and energy storage device is constructed respectively, and dynamic combination is carried out to obtain the modeling result;

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

[0078] According to the module selection strategy of key wind light storage energy equipment of power system, the module is selected for modeling;

[0079] Modeling is carried out through user-defined module.

[0080] As shown in Figure 1 , Figure 2 In step S1, based on energy form decoupling, the wind light storage dynamic model is modularly decomposed and reconstructed, which is divided into energy conversion and control, energy buffer, energy output and control three links.

[0081] In this embodiment, according to the difference of energy conversion mechanism of wind light storage three types of equipment, the application proposes a modular modeling system based on energy form decoupling. By decoupling the physical process of energy conversion and transmission path, the complex system can be divided into three main links of energy conversion and control, energy buffer, energy output and control. According to the characteristics of wind turbine, photovoltaic and energy storage model, the equipment is further divided into several model types, and the modular structure diagram of wind light storage model can be further obtained. Among them:

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

[0083] This link represents the physical process and control mechanism of energy form conversion. Specifically, it is the process of converting wind energy, solar energy, chemical energy, potential energy and other energy forms into mechanical energy or electrical energy form, and its control unit is responsible for accurately controlling the energy conversion link to realize the dynamic optimization of conversion efficiency. Since there are differences in energy conversion of wind turbine, photovoltaic and energy storage, different power generation systems need to be established. Energy conversion and control link includes: wind speed model, aerodynamics 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 environmental input is simulated by the wind speed model, the conversion equation of wind energy-mechanical energy is built by the aerodynamic model, and the instruction value of blade speed is provided by the aerodynamic model for the control of impeller speed, and the optional additional pitch angle control model is used to realize the adjustment of the angle of attack of the fan blade.

[0086] 2) Photovoltaic power generation system

[0087] The environmental parameters are obtained based on the irradiation model and the temperature model, and the photovoltaic array model realizes the conversion from light energy to electric energy and controls and adjusts this process.

[0088] 3) Energy storage system

[0089] Only the currently widely used electrochemical energy storage and pumped storage are considered. The primary model of electrochemical energy storage uses a battery model to represent the chemical energy-electric energy conversion, which can maintain the capacitor voltage at the rated value through direct current voltage control; the primary model of pumped storage realizes the dynamic hydraulic characteristics of potential energy-mechanical energy conversion through a water turbine and a water hammer model, and realizes speed regulation through a speed regulator, while the auxiliary model is used to control and simulate the charge and discharge state switching process of the energy storage.

[0090] (2) Energy conversion and control link

[0091] The energy conversion and control link serves as a dynamic buffer layer between the energy conversion and energy output links, and this link realizes the transient balance of energy and the suppression of power fluctuations through physical energy storage elements such as mechanical rotors and capacitors.

[0092] The energy buffer link includes: the mechanical system model of the wind power generation system; the direct current 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 / dual 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, and if pumped storage is used, the energy buffer model is simulated as a rotor.

[0094] (3) Energy output and control link

[0095] The energy output and control link includes: generator / converter model, electrical control model, high / low penetration control model, and protection model.

[0096] The energy output and control link is mainly responsible for adjusting the power and transmitting the converted energy to the grid according to the grid demand. The control unit receives real-time grid state signals to dynamically adjust the active / reactive power output strategy, ensuring that the device output characteristics match the grid operation requirements.

[0097] Specifically, the energy output stage of wind power generation systems and pumped hydro storage systems is the generator-converter, while the energy output stage of photovoltaic and electrochemical energy storage systems is the inverter. They can all use a unified generator / converter model, with parameter configurations representing doubly-fed / direct-drive wind turbines, photovoltaic inverters, and energy storage converters, respectively. The control stage employs a unified electrical control model, high-voltage and low-voltage surge control model, and protection model. The electrical control model and high-voltage and low-voltage surge control model provide active and reactive power reference signals to the converter model to achieve power output control of the converter, while the protection model provides the tripping logic. Therefore, the generator / converter model, electrical control model, high-voltage and low-voltage surge control model, and protection model can serve as universal models for wind, solar, and energy storage systems.

[0098] Through the above steps, a modular modeling system is constructed based on energy form decoupling, breaking the strong coupling of the traditional "black box" model, realizing dynamic reconfiguration modeling of modular components, and improving the flexibility of modeling.

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

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

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

[0102] STEPS (Simulation Toolkit for Electrical Power Systems) is an open-source simulation platform for simulating large-scale AC / DC hybrid power systems. Figure 16 The diagram illustrates the STEPS program architecture. STEPS currently possesses three main analysis functions: power flow, short circuit, and stability analysis. Theoretically, there is no limit to the scale of its software simulations. The STEPS kernel is developed in C++ and provides a wrapped Python interface, allowing for flexible use across different software platforms. Development of the software began around 2008, and it was officially open-sourced under the MIT license in 2018. Users can directly obtain the STEPS source code through the official STEPS hosting websites GitHub and Gitee.

[0103] The hierarchical integration architecture is constructed as follows:

[0104] The hierarchical inheritance architecture based on STEPS realizes modular dynamic reorganization, and divides the model into three layers of device type layer, model type layer, and specific model layer, which are in a successive inheritance and derivation relationship, as shown in Figure 18 The dynamic combination and expansion are realized through the virtual function mechanism in the C++ object-oriented programming features.

[0105] (1) Device type layer: defines the basic attributes of the device and the power grid data acquisition interface, serving as the base class of all models. Abstract interfaces are defined in this class through pure virtual functions, which force the derived classes to override the virtual functions and implement the specific function functions.

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

[0107] (3) Specific model layer: for the differentiated characteristics of wind turbines, photovoltaic devices, and energy storage devices, specific model classes are derived to implement the underlying physical logic. Polymorphism is used to dynamically call the derived class functions through base class pointers, without the need to modify the upper layer logic to switch model types.

[0108] Through the above steps, the hierarchical integration architecture built on the STEPS platform realizes dynamic expansion and cross-module collaboration of the model, improving the ease of extension of modular modeling and the stability of cross-module interaction.

[0109] As shown in Figure 1 , Figure 2 , in step S3, based on the hierarchical integration architecture, the modular models corresponding to wind turbines, photovoltaic devices, and energy storage devices are constructed and dynamically combined to obtain the modeling results; in the modeling process, the specific process is as follows: a wind-solar-storage universal model is constructed; modules are selected for modeling according to the module selection strategy of key wind-solar-storage devices in the power system; and the modeling is performed through user-defined modules.

[0110] S3-1, based on the hierarchical integration architecture, construct a wind-solar-storage universal model

[0111] The universal model includes generator / converter model, electrical control model, high-low penetration control model, and protection model. To realize the sharing of the universal model in wind turbines, photovoltaic devices, and energy storage systems, a universal model that can simultaneously consider the characteristics of wind-solar-storage systems must be designed.

[0112] S3-1-1, construction of universal generator / converter model

[0113] The converter model is an interface model of the network, the model is simplified, the dynamic process of the converter is ignored to some extent, the input is the power, voltage and current instruction value, and the output is the equivalent current injected into the network through a delay and amplitude limiting link. The generator / converter model is mainly divided into two types of grid-connected converter and grid-constructing converter.

[0114] The grid-connected converter measures the voltage phase angle of the grid-connected point through the phase-locked loop and keeps synchronization with the grid, and realizes the decoupling control of active and reactive power through the Park transformation, and forms the equivalent current injected into the network through the inverse Park transformation, so it shows the current source characteristic. The grid-connected converter grid injection current calculation formula is:

[0115]

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

[0117] The current limiting mode of the grid-connected converter is generally divided into active priority and reactive priority, the capacity of the converter is allocated to active power first in active priority, the output of active current is preferentially guaranteed, and the remaining capacity is left to reactive power, while reactive priority is the opposite, the formula is as follows:

[0118]

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

[0120] The grid-constructing type directly controls the output virtual internal potential with the grid through the power ring, and shows the voltage source characteristic. The machine terminal current and voltage equation of the grid-constructing 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 machine terminal voltage.

[0123] The injection current of the grid is coupled with the voltage of the corresponding node according to the formula (4), therefore, some transformation is needed to obtain the injection current of the grid. According to the formula (4), the following formula can be obtained:

[0124]

[0125] The transformation can be understood as converting the equivalent circuit of the voltage source into the equivalent circuit of the current source, i.e. transforming the equivalent impedance of the converter into admittance and adding it to the network admittance matrix. Therefore, the final grid injection current of the grid-forming converter is obtained, and the formula is as follows:

[0126]

[0127] The grid-forming converter has better voltage and frequency support capability, which also means that it has higher overcurrent risk. When simulating the current limit of the grid-forming converter, the actual current limit should be indirectly limited by limiting the virtual internal potential, therefore, the current limiting strategy of the grid-following converter can be applied to the grid-forming converter, and the current limiting strategy is as follows:

[0128] (1) Calculate the grid injection current without considering current limiting by using the formula (6)

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

[0130]

[0131] Wherein, θ is the phase angle of the node voltage.

[0132] (3) Limit the currents I d and I q by using the formulas (2) and (3) to obtain I d ' and I q '.

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

[0134]

[0135] (5) The virtual internal potential after current limiting is obtained by backstepping, and the formula is as follows:

[0136]

[0137] After the above step, through the current limiting strategy, it can be guaranteed that the virtual internal electric potential E is strictly equal to the current limiting model when the current is not over-limit, and the virtual internal electric potential is effectively limited when the current reaches the limit value. This limitation is two aspects, one is the amplitude of the virtual potential, the other is the virtual power angle, which depends on whether the active power or reactive power is prioritized.

[0138] S3-1-2, general electric control model construction

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

[0140] As shown in Figure 4 , wherein ① is the speed control, the speed deviation is output through the speed regulator power reference signal, which is suitable for the speed regulation process of wind power generation system and pumped storage system, wherein K pspeed and K ispeed are the proportional coefficient and integral coefficient of the speed regulator, ω and ω ref are the speed and speed reference value respectively. ② is the speed control with torque control, the torque is adjusted through the speed deviation, and then multiplied by the speed to get the output power reference value, which is suitable for occasions that need to consider torque and speed regulation of wind turbine system and pumped storage system, wherein K PP and K IP are the proportional and integral coefficients of the torque regulator. ③ is power control, which can directly control the converter, which is suitable for wind turbine, photovoltaic and energy storage system to realize the control dominated by frequency modulation, and also used in the direct control of the charging and discharging working condition 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, which is suitable for photovoltaic power generation system and electrochemical energy storage system, V dc and V dcref are the DC voltage and DC voltage reference value respectively. ⑤ is the constant torque control, the torque is fixed at the initial value, and the power set value is determined by the speed, T0 is the initial torque, which is suitable for the constant torque regulation of wind power generation system and pumped storage system. In the charging condition of energy storage, the positive and negative signs of ω ref and ω participate in the calculation.

[0141] In addition, a frequency control module is separately packaged, as shown in Figure 5The shown. It supports primary frequency control, secondary frequency control and virtual inertia control, and its output ΔP can be superimposed at Pset in the electrical control model to make the device have frequency modulation capability. In energy storage, ΔP also serves as the active scheduling instruction value of the upper layer control of the energy storage, controlling the charge and discharge operating state of the energy storage. Figure 5 K fint is the secondary frequency control link proportional coefficient, K vi is the virtual inertia link proportional coefficient, T vi is the virtual inertia link time constant, K droop is the primary frequency control link proportional coefficient, T droop is the primary frequency control link time constant.

[0142] The reactive power control supports constant voltage control, constant power factor control, constant reactive power control, as shown in Figure 6 . It also supports controlling the voltage of another bus by detecting the voltage of any remote bus using superimposed line voltage drop compensation. The parameters K pv and K VI in the figure are the voltage control link proportional coefficient and integral coefficient, P G is the device output active power, PF ref is the power factor reference value, Q ref is the reactive power reference value.

[0143] S3-1-3, general high-low voltage ride-through control model construction

[0144] The current wind, light and energy storage devices are required to have high and low voltage ride-through capability. During low voltage ride-through, it is required to provide certain reactive power to the system to help voltage recovery. During high voltage ride-through, it is required to absorb certain reactive power. The normal electrical control model of the device is bypassed during voltage ride-through, and the control signal of the converter is determined by the high and low voltage ride-through control strategy. The control strategies of different types of devices are different, and the response characteristics differ greatly. Therefore, a three-stage voltage ride-through control strategy with good operability and universality is adopted, and seven high and low voltage ride-through states are defined, as shown in Figure 7 .

[0145] The specific high and low voltage ride-through control strategy switching logic is:

[0146] (1) In the normal state, if the terminal voltage is lower than the low voltage ride-through threshold value V lvrt_th , enter 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, the low voltage ride through recovery start time relay starts timing, and after T lvrt_delay = 0, it means that there is no low voltage ride through recovery start state, and it will directly enter the low voltage ride through recovery state.

[0148] (3) In the low voltage ride through recovery start state, if the terminal voltage is lower than the low voltage ride through threshold value V lvrt_th , it will return to the low voltage ride through state. In the low voltage ride through recovery start state, the low voltage ride through recovery start time relay starts timing, and after T lvrt_delay , it will enter the low voltage ride through 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 , it will return to the low voltage ride through state. When the active current command I pcmd recovers to the active current command before entering the low voltage ride through state and the reactive current command I qcmd recovers to the reactive current command before entering the low voltage ride through state, it will enter the normal state, at this time the electrical control model cancels the bypass and normally integrates, and the signal input of the wind power generator model will be switched to the electrical control model.

[0150] (5) In the normal state, if the terminal voltage is higher than the high voltage ride through threshold value V hvrt_th , it will enter the high voltage ride through state, at this time the electrical control model is bypassed and its integral element is frozen, and the signal input of the wind power generator model is switched to the high-low voltage 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 , it will enter the high voltage ride through recovery start state. If the delay time T hvrt_delay of the high voltage ride through recovery start time relay is 0, it means that there is no low voltage ride through recovery start state, and it will directly enter the high voltage ride through recovery state.

[0152] (7) In the high voltage ride through recovery start state, if the terminal voltage is higher than the high voltage ride through threshold value V hvrt_th , it will return to the high voltage ride through state. In the high voltage ride through recovery start state, the high voltage ride through recovery start time relay starts timing, and after T lvrt_delay , it will enter the high voltage ride through 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 , it will return to the high voltage ride through state. When the active current command I pcmd recovers to the active current command before entering the high voltage ride through state and the reactive current command I qcmdIf the reactive current command before the high penetration state is restored, the system enters the normal state, in which the electrical control model cancels the bypass and normally integrates, and the signal input of the wind turbine model is switched to the electrical control model.

[0154] S3-1-4, general protection model construction

[0155] Some grid code rules stipulate that the device cannot be offline if the system fault is not serious enough to exceed the boundary limit and the tolerance time limit, but the boundary condition limit and the tolerance time limit will vary with different engineering projects, so the user can customize the boundary curve, such as Figure 8 as shown.

[0156] The threshold value for entering the low voltage ride through state is generally taken as the maximum value of the low voltage boundary protection curve, and in order to cooperate with the high-low penetration control model, the maximum value (minimum value) of the device low voltage (high voltage) boundary curve can be the low voltage ride through threshold value (high voltage ride through threshold value) of the high-low penetration control model. When the voltage is lower than the threshold value, the 0 time of the low voltage boundary protection curve will automatically align with the time when the voltage is lower than the threshold value, if the voltage touches the boundary protection curve, the generator protection relay will start timing, and will officially trip after a certain delay time, once the generator protection relay starts timing, it cannot return. Proportional tripping can be supported, if the tripping ratio is 1, all units will be tripped, if the tripping ratio is 0, it is equivalent to not acting.

[0157] S3-2, wind power system model construction

[0158] The types of wind turbines mainly include constant speed, double-fed and direct drive, although constant speed wind turbines have been in operation for a long time, their efficiency is low and their power regulation ability is poor, so 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 wind turbine converting wind energy into mechanical energy, it can not only calculate the mechanical power drawn by the wind turbine impeller, but also provide a speed reference value ω ref according to the control strategy. The aerodynamic model is also responsible for calculating the initial values of the pitch angle and the wind wheel speed, so that the mechanical power provided by the wind turbine is equal to the generated power obtained by the power flow calculation. The aerodynamic model supports linear model and detailed model.

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

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

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

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

[0165]

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

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

[0168]

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

[0170] It supports operation in three active power reserve modes: maximum power point tracking, underspeed, and overspeed. For example... Figure 9 As shown. The maximum power point is solved using the derivative increment method, but this only provides a rough range of operating points. Therefore, a bisection method can be further used to find a more accurate solution, ensuring a calculation accuracy within 10 to the power of -10. The specific process is as follows. Figure 9 As shown. Therefore, the simulation of the detailed model is very accurate, but solving it is a very time-consuming process.

[0171] The three active power reserve modes are described below:

[0172] (1) Maximum Power Point Tracking Mode

[0173] Based on the current wind speed V w C corresponding to the pitch angle β p The impeller speed at the point of maximum power on the curve is used as the reference speed value ω. ref As shown in the following formula:

[0174]

[0175] (2) Underspeed operation mode

[0176] According to wind speed V w C corresponding to the pitch angle β p Curve, in C p Find an impeller speed P within the range to the left of the maximum power point on the curve. m The mechanical power P that the wind turbine generator draws from the wind. m With rotor braking power P mech The impeller speed is equal to the impeller speed, and this impeller speed is used as the reference value ω. ref If the mechanical power absorbed at the maximum power point is less than the rotor braking power, the fan switches to maximum power point tracking mode, using the speed corresponding to the maximum power point as the speed reference value ω. ref The system returns to underspeed operation mode only when the mechanical power absorbed at the maximum power point exceeds the rotor braking power. This is illustrated in the following formula:

[0177]

[0178] Rotational speed ω ref Subject to the maximum impeller speed ω max Or minimum impeller speed ω min The limitations are as shown in the following formula:

[0179]

[0180] (3) Overspeed operation mode

[0181] The dynamic processes of overspeed operation mode and underspeed operation mode are similar, the difference being that ω operates at C. p The maximum power point ω of the curve MPPT The right side will not be discussed further 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 rotational speed and reference signals, as well as the power and reference value signals. This signal is then input into the aerodynamic model to achieve mechanical power control of the wind turbine. When the pitch angle control model is unavailable, 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 block model, which simulates the wind turbine blades and generator rotor as an inertial body, taking into account the flexibility and damping of the transmission shaft.

[0186] S3-2-4 Wind Speed ​​Model Construction

[0187] Wind speed model generates wind speed signal and inputs aerodynamics model, which is realized by reading wind speed file, in which wind speed value at different time points can be set, so that any wind type can be simulated in theory.

[0188] S3-3, photovoltaic power generation system model construction

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

[0190] S3-3-1, photovoltaic array model construction

[0191] Photovoltaic array model is used to simulate the characteristics of converting light energy into electrical energy by photovoltaic panel, which supports two types of simple model and complex model.

[0192] Simple model uses general engineering calculation formula to calculate the output power P of photovoltaic panel pv , and realizes power backup according to given power backup coefficient K rp , without considering the effect of temperature. The formula is as follows:

[0193]

[0194] Where S is the light intensity, S ref is the reference value of light intensity, P msta is the maximum power under standard test environment, and b is the constant related to battery material.

[0195] Detailed model simulates the volt-ampere characteristics of photovoltaic panel. It can not only calculate the output power of photovoltaic panel, but also provide direct current voltage reference value U dcref according to control strategy. In the initialization stage, photovoltaic array model is responsible for calculating the initial direct current voltage, and can back-calculate the initial light intensity according to the photovoltaic flow result.

[0196] The photovoltaic characteristics of this model need to specify four technical parameters of short-circuit current I sc , open-circuit voltage V oc , maximum power point load current I m and load voltage V m when describing, and then be converted at actual temperature T and light intensity S. The formula is:

[0197]

[0198] The output current of photovoltaic panel is:

[0199]

[0200] Similar to the detailed model in the aerodynamic model, the photovoltaic 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 of the wind turbine's aerodynamic model; simply replace the Cp function of the wind turbine with the volt-ampere characteristic function of the photovoltaic panel, and replace the independent variables—pitch angle and tip speed ratio—with light intensity and DC voltage, respectively. Further details are omitted here.

[0201] S3-3-2, Capacitor Model Construction

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

[0203]

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

[0205] S3-3-3 Construction of Illumination and Temperature Models

[0206] To simulate various environmental conditions, both illumination and temperature are obtained by reading files. When the model does not exist, the initial values ​​of illumination and temperature are kept unchanged.

[0207] S3-4, Energy Storage System Model Construction

[0208] Only the two most widely used energy storage methods, electrochemical energy storage and pumped hydro storage, are considered. The system supports both charging and discharging operations. In the discharging mode, all physical quantities, such as rotational speed and power, are positive, while in the charging mode, they are negative.

[0209] S3-4-1, First Model Construction

[0210] The primary model of electrochemical energy storage mainly consists of an electrochemical bulk model and DC voltage control. For example... Figure 11 The model shown, where E oc R is the internal potential of the battery, close to the resting voltage; b R is the ohmic resistance of the battery. p C p These are the polarization resistance and capacitance of the battery, used to describe the overall polarization characteristics. They can simulate single-stage / two-stage electrochemical energy storage control, and both the presence and absence of DC / DC circuits, such as... Figure 12 As shown. When the DC / DC circuit is present, the DC capacitor voltage will be controlled by the DC / DC circuit to the rated value V. dcN When the DC / DC circuit is absent, the DC capacitor voltage responds to system changes and is not a constant value.

[0211] like Figure 13As shown, the primary model of pumped storage consists of a regulator, a servo mechanism, and a prime mover. The regulator typically uses a PI controller, which has multiple control modes, including power control, speed control, and opening control. It can generate corresponding opening signals according to system requirements and transmit them to the servo mechanism. The servo mechanism can be simulated using a first-order inertial element, receiving the opening signal and driving the guide vanes to make the turbine generate the specified mechanical energy.

[0212] S3-4-2, Energy Buffer Model Construction

[0213] like Figure 14 As shown, if electrochemical energy storage is used in the energy storage system, the energy buffer model is simulated as a capacitor; if pumped hydro storage is used, the energy buffer model is simulated as a rotor. When simulated as a capacitor, it is as shown in formula (19). When simulated as a rotor, it is the rotor motion equation. In the figure, H is the moment of inertia of the rotor.

[0214] In actual simulations, the capacitor voltage remains constant at a positive value, while the rotational speed fluctuates between positive and negative due to changes in the charging and discharging states of the energy storage system. When the rotational speed is near 0, it may lead to inaccurate simulations because it is in the denominator. Therefore, when the rotational speed decreases to a certain value (which can be specified as 0.001 pu in the program), the rotor will be directly locked, at which point the rotational speed will be 0 and no integration operation will be performed. When the rotor starts, it increases directly from 0.001 pu during discharging and from -0.001 pu during charging. The D coefficient is used to simulate the effect of braking torque. During shutdown, the braking torque will only take effect if the mechanical power and output power reach 0 before the rotational speed, effectively ensuring that the rotational speed eventually decays to 0.

[0215] S3-4-3, Construction of Energy State Model

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

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

[0218] Among them, M base The base capacity of the energy storage system is expressed in MVA or E. total This represents the total energy that the energy storage system can store, expressed in kJ.

[0219] S3-4-4, Auxiliary Model Construction

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

[0221] (1) Operating condition switching logic.

[0222] This embodiment proposes seven energy storage charge / discharge switching conditions and supports automatic or manual switching. For example... Figure 16 As shown. The switching process is mainly controlled by the active power dispatch command value and energy status of the power grid. 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 given through a specified interface. When the power generated or absorbed by the unit is less than the minimum value of the normal operating range, the unit will choose not to start based on efficiency. When the energy status value is too low or too high, the energy storage system will be automatically controlled to enter the no-load condition.

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

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

[0225] S3-5. Based on the module selection strategy or user-defined module for key wind, solar and energy storage equipment in the power system, construct modular sub-models for wind turbines, photovoltaics and energy storage equipment respectively, and dynamically combine them to obtain the modeling results.

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

[0227] Therefore, the user can freely select a specific model for use under each model type, thereby achieving flexible combination of modules. When expanding the model, only the specific model with specific functions needs to be developed under a certain model type, avoiding the re-development of a complete set of models. In the general model part, the developer only needs to care about the input and output of the general model, without repeatedly processing the complex logic inside the model, and if the model logic needs to be adjusted, only the shared model needs to be modified, which can achieve simultaneous modification of the wind-solar-storage device model, thereby significantly saving model development resources and time.

[0228] S3-5-1, module selection strategy based on key wind-solar-storage devices of the power system, selecting modules for modeling.

[0229] For power system simulation, detailed models can generally obtain more accurate simulation results, but introducing too many detailed models for simulation calculation is large in amount and time-consuming. On the contrary, using simple models can reduce the amount of simulation calculation, but the accuracy of simulation is sacrificed. In order to balance the efficiency and accuracy of power system simulation, it is crucial to select a model with appropriate complexity. The module selection strategy proposed in the present application realizes the accurate identification of key wind-solar-storage devices in the power system by improving the PageRank algorithm through power distribution analysis model, solves the contradiction between "low efficiency of detailed modeling of the whole system" and "insufficient accuracy of simplified modeling" in traditional modeling, and realizes the modular selection and adaptation of wind-solar-storage models. The strategy not only improves the simulation efficiency, but also ensures the simulation accuracy by dynamically matching the model complexity.

[0230] The PageRank algorithm can solve the sorting problem of important nodes in the network and has been widely applied in various fields. The traditional PageRank algorithm only allocates link weights based on the average out-degree of nodes, without considering the power flow characteristics of the power system, and cannot reflect the actual contribution of the device to the power flow distribution. The algorithm represents the entire network as a directed graph, and the edges associated with the nodes represent the links between the nodes. In the algorithm, the importance of a node is measured by the PR value, which is determined by the PR values of the incoming nodes and its own PR value. In the PageRank algorithm, the calculation of the PR value is an iterative process. The formula is:

[0231]

[0232] Where, j e i is the set of i node; L(j) is the chain out of the node j number; n is the number of all nodes in the network; sigma is the damping coefficient of the algorithm, to ensure the convergence of the algorithm.

[0233] As Figure 3 shown, the module selection strategy of the key wind light energy storage equipment is specifically:

[0234] (1) Through power system flow calculation, the system power flow distribution is obtained.

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

[0236] For a network of n nodes, each node has an inherent importance of (1-σ) / n, the node j linked to i contributes PR(j) importance, which is shared by all the chain out of j nodes. After iteration calculation, the PR value of each node tends to converge, and the larger the PR value of the node, the more important the node in the network. However, the PageRank algorithm evenly distributes the link weight of the model in the iteration process according to the node out-degree, without considering the importance of each node itself. For power system, the distribution of node link weight should be determined by the actual line flow, so the contribution of wind light storage and other power generation equipment to the system line flow should be considered. Since the difference between the detailed model and the simple model in the wind light storage model is mainly related to the active power, only the active power flow is designed here.

[0237] The specific process is:

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

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

[0240]

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

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

[0243] K = (k ij ) n×n = ET (P GG ) -1 A -1 (23)

[0244] where E is the identity matrix; is the active power output of the power source at node k,

[0245] 2) Based on the distribution coefficient matrix, the power distribution of the power source flowing to the line and the link weight between each power source node are calculated.

[0246] The power distribution of the power source G k flowing to the line L(i,j) is calculated and the link weight ω L (i,j) between each node, specifically:

[0247] The power distribution of the power source flowing to the line is calculated, and the formula is:

[0248]

[0249] where, is the power distribution of the power source G k flowing to the line L(i,j).

[0250] According to the importance of the power output at the node, the importance weight of the power source is defined , and the formula is:

[0251]

[0252] where ∑P G is the total power generation of the system.

[0253] According to the importance weight of the equipment and the power flow distribution, the link strength ω L (i,j) between the nodes of the system is defined to represent the link weight between each node, and the formula is:

[0254]

[0255] 3) Based on the link weight between each power source node, the PR value of each power source node is iteratively calculated.

[0256] Since the line with a larger link weight is more likely to come from a more important power source, the iterative calculation formula for calculating the PR value of each power source node using this algorithm is:

[0257]

[0258] where S(j) is the sum of all chain-out weights of j node,

[0259] The node PR value obtained after the iteration 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 influence degree of power equipment on the system.

[0260] (3) According to the PR value of each power node, a detailed model or a simplified model is selected for modeling.

[0261] Through the above steps, the key wind, light and storage equipment in the power system is accurately identified, the efficiency and accuracy of power system simulation are considered, and the modular selection and adaptation of wind, light and storage model are realized.

[0262] S3-5-2, user-defined modeling

[0263] In addition to the selection of specific models by users under a certain model type, user-defined modular modeling is also supported, so as to realize the flexible expansion of modules.

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

[0265] (1) According to the transfer function block diagram of the user-defined model, select the required basic elements. The basic elements include first-order inertia element, integral element, proportional-integral element, proportional-integral-derivative element, saturation element, etc.

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

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

[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 modular modeling framework has been implemented in STEPS, and specific models have been integrated into the dynamic model database of STEPS. For each type of model, users can choose one of the specific models according to the simulation requirements, and input the specific data format to the program to complete the model creation, or customize the modeling under the established modular modeling framework according to the above process.

[0270] Through the above steps, the rapid development of personalized models based on basic links is realized, and the openness of the modular modeling platform and the user's self-expanding ability are significantly improved.

[0271] S3-6, verifying the constructed model

[0272] To improve the reliability and ease of use 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, it includes:

[0273] (1) Model integrity check

[0274] Model integrity check is used to identify missing or redundant models. Model missing will cause inaccurate simulation results, and model redundancy, although it does not affect the simulation results, will undoubtedly waste a large amount of memory resources in large-scale system simulation scenarios. For example, when the primary model of the energy storage system is an electrochemical energy storage, the auxiliary model does not need to exist, and when the photovoltaic array model is a simple model, the capacitor model does not need to exist.

[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 the capacitor model; when simulating pumped storage systems, the energy buffer model must use the rotor model.

[0277] (3) Control mode matching check

[0278] Control mode matching check is used to ensure that the control logic matches the operating characteristics of the device. For example, the speed control in the electrical control model can only be used for wind power systems and pumped storage systems; DC voltage control is suitable for photovoltaic power systems and electrochemical energy storage systems; when the photovoltaic array model in the photovoltaic power system uses a simple model, the electrical control model must use power control, and when a complex model is used, the electrical control model must use DC voltage control; the auxiliary model of pumped storage requires the presence of speed control and power control in the primary model and the electrical control model, otherwise the switching operation cannot be completed smoothly.

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

[0280] After modeling, the wind-solar-storage modular modeling method in the embodiment can be applied to 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 electromechanical transient characteristic research of new power systems. By modularly combining different new energy models, the additional regulation capacity under a certain penetration rate can be evaluated. A million-level electric vehicle aggregation model can be established to realize vehicle interaction. It can be applied to virtual power plants to improve the income from participating in the peak shaving market. The current modeling method has been difficult to adapt to the development needs of new power systems.

[0281] Embodiment two

[0282] The purpose of the embodiment is to provide a wind-solar-storage modular modeling system for electromechanical transient of a power system, comprising:

[0283] A modular decomposition and reconstruction module is configured to modularly decompose and reconstruct the wind-solar-storage dynamic model based on energy form decoupling, and decompose it into three links of energy conversion and control, energy buffering, and energy output and control.

[0284] A hierarchical integrated architecture construction module is configured to construct a hierarchical integrated architecture based on the three links.

[0285] A modeling module is configured to construct modular models corresponding to wind turbines, photovoltaic devices, and energy storage devices based on the hierarchical integrated architecture, and perform dynamic combination to obtain modeling results. In the modeling, the specific process is as follows: a wind-solar-storage general model is constructed; modules are selected for modeling according to a module selection strategy of key wind-solar-storage equipment of the power system; and modeling is performed through user-defined modules.

[0286] Based on the wind-solar-storage modular modeling system for electromechanical transient of a power system, the method steps in embodiment one are implemented.

[0287] Embodiment three

[0288] The object of this embodiment is to provide a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the program.

[0289] Embodiment four

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

[0291] A computer readable storage medium having stored thereon a computer program, the program being executable by a processor to perform the steps of the above method.

[0292] Embodiment five

[0293] The object of this embodiment is to provide a computer program product comprising instructions which, when executed on a computer, cause the computer to perform the method and functions involved in any of the above embodiments.

[0294] The steps and methods involved in the above embodiments correspond to embodiment one, and the detailed description can be found in the relevant description part of embodiment one. The term "computer readable storage medium" should be understood as including a single medium or multiple media of one or more instruction sets; it should also be understood as including any medium capable of storing, encoding or carrying instruction sets for execution by a processor and causing the processor to perform any of the methods in the present application.

[0295] Those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computer device, alternatively, they can be implemented by program codes executable by a computing device, so that they can be stored in a storage device for execution by a computing device, or they can be made into individual integrated circuit modules, or a plurality of modules or steps among them can be made into a single integrated circuit module. The present application is not limited to any specific combination of hardware and software.

[0296] The above describes the specific embodiments of the present application in conjunction with the accompanying drawings, but is not a limitation on the scope of protection of the present application, those skilled in the art should understand that various modifications or variations made by those skilled in the art on the basis of the technical solutions of the present application without creative labor are still within the scope of protection of the present application.

Claims

1. A modular modeling method for wind, solar, and energy storage systems oriented towards electromechanical transients in power systems, characterized in that, include: Based on energy form decoupling, the dynamic model of wind, solar and energy storage is modularly decomposed and reconstructed into three links: energy conversion and control, energy buffering, and energy output and control. The energy conversion and control process includes: 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; Energy buffering mechanisms include: Mechanical system model of a wind power generation system; DC capacitor module of photovoltaic power generation system; Capacitor model or mechanical rotor model of energy storage system; The energy output and control components include: generator / converter model, electrical control model, high and low voltage power supply control model, and protection model; Based on the three stages, a hierarchical integration architecture is constructed; Based on a hierarchical integrated architecture, modular models corresponding to wind turbines, photovoltaics, and energy storage devices are constructed respectively, and dynamically combined to obtain the modeling results; In the modeling process, the specific steps are as follows: constructing a general model for wind, solar and energy storage; Based on the module selection strategy for key wind, solar, and energy storage equipment in the power system, modules are selected for modeling; the specific module selection strategy for key wind, solar, and energy storage equipment is as follows: The power flow distribution of the system is obtained through power system power flow calculation; Based on the system power flow distribution, the PR value of each power source node is calculated using an improved PageRank algorithm; Choose between a detailed model or a simplified model for modeling based on the PR value of each power node; Modeling is performed using user-defined modules.

2. The modular modeling method for wind, solar, and energy storage oriented towards electromechanical transients of power systems as described in claim 1, characterized in that, To construct a hierarchical integrated architecture, specifically, in the STEPS simulation platform of the power system simulation toolkit, a hierarchical integrated architecture is established, consisting of an equipment type layer, a model type layer, and a specific model layer.

3. The modular modeling method for wind, solar, and energy storage oriented towards electromechanical transients of power systems as described in claim 1, characterized in that, The PageRank value of each power node is calculated using an 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 power flow to the line and the link weights between each power node are calculated. The PR value of each power node is calculated iteratively based on the link weights between each power node.

4. A modular modeling system for wind, solar, and energy storage oriented towards electromechanical transients in power systems, characterized in that: include: The modular decomposition and reconstruction module is used to perform modular decomposition and reconstruction of the dynamic model of wind, solar and energy storage based on energy form decoupling, decomposing it into three links: energy conversion and control, energy buffering, and energy output and control; the energy conversion and control link includes: 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; Energy buffering mechanisms include: Mechanical system model of a wind power generation system; DC capacitor module of photovoltaic power generation system; Capacitor model or mechanical rotor model of energy storage system; The energy output and control components include: generator / converter model, electrical control model, high and low voltage power supply control model, and protection model; The hierarchical integration architecture building module is used to build a hierarchical integration architecture based on three stages; The modeling module is used to construct modular models corresponding to wind turbines, photovoltaics, and energy storage devices based on a hierarchical integrated architecture, and dynamically combine them to obtain the modeling results. The specific modeling process includes: constructing a general wind-solar-storage model; selecting modules for modeling according to the module selection strategy for key wind, solar, and energy storage devices in the power system; and modeling using user-defined modules. The module selection strategy for key wind, solar, and energy storage devices is as follows: The power flow distribution of the system is obtained through power system power flow calculation; Based on the system power flow distribution, the PR value of each power source node is calculated using an improved PageRank algorithm; Choose between a detailed model or a simplified model based on the PR value of each power node.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it performs the steps of the method described in any one of claims 1-3 above.

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