Real-time simulation method and device applied to multi-distributed resource large-scale cluster access power distribution network, computer equipment, storage medium and computer program product

Abstract

The invention relates to a real-time simulation method and device applied to a multi-distributed resource large-scale cluster access power distribution network, computer equipment, a storage medium and a computer program product. The method comprises the following steps: constructing a unit simulation model for each distributed resource; accessing the unit simulation model of each distributed resource into the initial power distribution network simulation model to obtain a novel power distribution network simulation model; performing partition decoupling on the novel power distribution network simulation model by adopting the ideal transformer model based on the dynamic response characteristics to obtain a first sub-simulation system and a second sub-simulation system; and at the simulation moment of the first sub-simulation system, asynchronous communication between the first sub-simulation system and the second sub-simulation system is realized through the multi-rate interface so as to perform simulation data real-time interaction. By adopting the method, real-time simulation can be accurately carried out on the novel power distribution network accessed by various distributed resources.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network, and in particular to a real-time simulation method, apparatus, computer equipment, storage medium and computer program product for accessing a distribution network with a large-scale cluster of multiple distributed resources. Background Art

[0002] With the access of various distributed resources such as new energy power generation, energy storage, and electric vehicle charging stations to the traditional distribution network, the structure and operating characteristics of the distribution network have undergone major changes.

[0003] After a variety of new distributed resources are connected to the distribution network through power electronic device clusters, the problems of complex propagation and evolution of power quality in active distribution networks, weak system overload capacity, and difficulty in accurate frequency control have become increasingly prominent. However, in the current distribution system simulation technology, only the simulation of a certain type of distributed resource is usually considered, or the system-level real-time simulation of "source-grid-load-storage-charge" is not fully considered, resulting in the inability to effectively simulate the steady-state and dynamic processes of new distribution networks with multiple distributed resources connected in real time.

[0004] Therefore, there is a problem in traditional technologies that they cannot accurately simulate new distribution networks that have multiple distributed resources connected in real time. Summary of the invention

[0005] Based on this, it is necessary to provide a real-time simulation method, device, computer equipment, computer-readable storage medium and computer program product for large-scale cluster access to distribution networks of multiple distributed resources, which can accurately simulate a new type of distribution network with access to multiple distributed resources in real time in response to the above-mentioned technical problems.

[0006] A real-time simulation method for large-scale cluster access of multiple distributed resources to a distribution network, the method comprising:

[0007] Build a unit simulation model for each distributed resource;

[0008] Connect the unit simulation model of each distributed resource to the initial distribution network simulation model to obtain a new distribution network simulation model;

[0009] Based on the dynamic response characteristics of the ideal transformer model, the new distribution network simulation model is partitioned and decoupled to obtain the first sub-simulation system and the second sub-simulation system; the first sub-simulation system and the second sub-simulation system use different simulation steps; the simulation step of the first sub-simulation system is an integer multiple of the simulation step of the second sub-simulation system; the first sub-simulation system and the second sub-simulation system exchange data through a multi-rate interface;

[0010] At the simulation time of the first sub-simulation system, asynchronous communication between the first sub-simulation system and the second sub-simulation system is realized through a multi-rate interface to perform real-time interaction of simulation data.

[0011] In one embodiment, at the simulation time of the first sub-simulation system, asynchronous communication between the first sub-simulation system and the second sub-simulation system is implemented through a multi-rate interface to perform simulation data interaction, including:

[0012] At the first simulation moment, the simulation data of the first sub-simulation system at each historical first simulation moment is received through the second sub-simulation system; the first simulation moment is the simulation moment of the first sub-simulation system; the historical first simulation moment is before the first simulation moment;

[0013] The Hermite interpolation method is used to determine the simulation data prediction results of the first sub-simulation system at each historical second simulation moment based on the simulation data of the first sub-simulation system at each historical first simulation moment; the second simulation moment is the simulation moment of the second sub-simulation system; the historical second simulation moment is between the first simulation moment and the historical first simulation moment.

[0014] In one of the embodiments, the unit simulation model of the distributed resource includes a photovoltaic power generation unit simulation model;

[0015] The simulation model of the photovoltaic power generation unit adopts the maximum power point tracking algorithm based on the conductance increment method, and controls the photovoltaic cell terminal voltage through a boost converter to track the maximum power point.

[0016] In one of the embodiments, the unit simulation model of the distributed resource includes a wind power unit simulation model;

[0017] The wind power generation unit simulation model uses a permanent magnet direct-drive wind turbine generator and combines the tip speed ratio tracking technology to optimize the wind speed.

[0018] In one embodiment, the unit simulation model of the distributed resource includes an energy storage unit simulation model; the energy storage unit simulation model is a lithium battery simulation model;

[0019] The lithium battery simulation model is connected to the new distribution network simulation model through a three-level midpoint clamped inverter. The control strategy of the energy storage unit simulation model is a direct power control strategy based on voltage orientation.

[0020] In one of the embodiments, the unit simulation model of the distributed resource includes a unit simulation model of an electric vehicle charging pile;

[0021] The simulation model of the electric vehicle charging pile unit adopts a resonant switching converter; the resonant switching converter supports constant current charging mode and constant voltage charging mode; the resonant switching converter is used to switch the charging mode according to the charge state of the electric vehicle battery to optimize the charging process of the electric vehicle battery.

[0022] A real-time simulation device for large-scale cluster access of multiple distributed resources to a distribution network, the device comprising:

[0023] A construction module, used for constructing a unit simulation model for each distributed resource;

[0024] An access module is used to access the unit simulation model of each distributed resource into the initial distribution network simulation model to obtain a new distribution network simulation model;

[0025] A decoupling module is used to partition and decouple the new distribution network simulation model based on the dynamic response characteristics using an ideal transformer model to obtain a first sub-simulation system and a second sub-simulation system; the first sub-simulation system and the second sub-simulation system use different simulation steps; the simulation step of the first sub-simulation system is an integer multiple of the simulation step of the second sub-simulation system; the first sub-simulation system and the second sub-simulation system exchange data through a multi-rate interface;

[0026] The interaction module is used to realize asynchronous communication between the first sub-simulation system and the second sub-simulation system through a multi-rate interface at the simulation time of the first sub-simulation system, so as to perform real-time interaction of simulation data.

[0027] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0028] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0029] A computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0030] The above-mentioned real-time simulation method, device, computer equipment, storage medium and computer program product applied to the large-scale cluster access of multiple distributed resources to the distribution network, by constructing a unit simulation model for each distributed resource; connecting the unit simulation model of each distributed resource to the initial distribution network simulation model to obtain a new distribution network simulation model; using an ideal transformer model based on dynamic response characteristics, the new distribution network simulation model is partitioned and decoupled to obtain a first sub-simulation system and a second sub-simulation system; the first sub-simulation system and the second sub-simulation system use different simulation step sizes; the simulation step size of the first sub-simulation system is an integer multiple of the simulation step size of the second sub-simulation system; the first sub-simulation system The real system and the second sub-simulation system exchange data through a multi-rate interface. At the simulation moment of the first sub-simulation system, asynchronous communication between the first sub-simulation system and the second sub-simulation system is achieved through the multi-rate interface to interact with simulation data in real time. In this way, the resources of the simulation hardware can be reasonably allocated according to the characteristics of each distributed resource and the simulation requirements, thereby ensuring the accuracy of the simulation results and the efficiency of the simulation calculation. In addition, by adopting a voltage source ideal transformer model to decouple the new distribution network simulation model, no complex control architecture is required, and it can support the simulation requirements of large-scale cluster access of multiple distributed resources, which is convenient for the addition of distributed resource units and the expansion of simulation requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 An application environment diagram of a real-time simulation method for accessing a large-scale cluster of multiple distributed resources to a distribution network in one embodiment;

[0033] Figure 2 A flowchart of a real-time simulation method for accessing a large-scale cluster of multiple distributed resources to a power distribution network in one embodiment;

[0034] Figure 3 A schematic diagram of a new distribution network simulation model including large-scale cluster access of multiple distributed resources in one embodiment;

[0035] Figure 4 is a schematic diagram of a multi-rate parallel simulation principle in one embodiment;

[0036] Figure 5 is a schematic diagram of the principle of a voltage source type ideal transformer model in one embodiment;

[0037] Figure 6 A schematic diagram of segmentation of a simulation model of an electric vehicle charging pile unit in an embodiment;

[0038] Figure 7 A schematic diagram of timing coordination of a large and small step system interface in an embodiment;

[0039] Figure 8 A flowchart of a real-time simulation method for accessing a large-scale cluster of multiple distributed resources to a power distribution network in another embodiment;

[0040] Fig. 9 A structural block diagram of a real-time simulation device applied to large-scale cluster access of multiple distributed resources to a distribution network in one embodiment;

[0041] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0043] Existing simulation technologies are relatively mature for electromagnetic transient analysis at the device and equipment levels. However, when it comes to research and analysis at the grid level, especially when considering the mutual influence of the multi-time-scale dynamic characteristics of distributed resources such as "source-grid-load-storage-charger", existing electromagnetic transient simulation tools face the problems of large computational complexity and inconsistent simulation steps. At the same time, the access of a large number of power electronic devices to the distribution network will cause a sharp increase in the number of converters in the entire system, making system simulation face the problem of "dimensionality curse". In addition, the dynamic response times of different control systems and equipment (such as wind power, photovoltaics, and energy storage systems) vary greatly, making it difficult for the simulation system to simultaneously meet the requirements of high-frequency dynamic response and overall system stability.

[0044] In view of this, in order to improve the authenticity and timeliness of system testing when a variety of new distributed resources are accessed through a cluster of power electronic devices, this application proposes a real-time simulation method for large-scale cluster access of multiple distributed resources based on a CPU-FPGA heterogeneous joint simulation platform, in order to further promote the development and application of new distribution network real-time simulation technology.

[0045] The real-time simulation method for large-scale cluster access of multiple distributed resources to a distribution network provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Construct a unit simulation model for each distributed resource; the server 104 connects the unit simulation model of each distributed resource to the initial distribution network simulation model to obtain a new distribution network simulation model; the server 104 uses an ideal transformer model based on the dynamic response characteristics to partition and decouple the new distribution network simulation model to obtain a first sub-simulation system and a second sub-simulation system; the first sub-simulation system and the second sub-simulation system use different simulation steps; the simulation step of the first sub-simulation system is an integer multiple of the simulation step of the second sub-simulation system; the first sub-simulation system and the second sub-simulation system interact with data through a multi-rate interface; the server 104 realizes asynchronous communication between the first sub-simulation system and the second sub-simulation system through a multi-rate interface at the simulation moment of the first sub-simulation system, so as to interact with simulation data in real time. The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0046] In an exemplary embodiment, Figure 2 As shown in the figure, a real-time simulation method for large-scale cluster access of multiple distributed resources to the distribution network is provided. Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps S202 to S208. Among them:

[0047] Step S202: construct a unit simulation model for each distributed resource.

[0048] The distributed resources may be power resources that can be connected to a distributed distribution network system, for example, photovoltaic power generation units, permanent magnet direct-drive wind power generation units, energy storage units, and electric vehicle charging pile units.

[0049] The unit simulation model of the distributed resources may be a simulation model constructed separately according to the characteristics of the distributed resources.

[0050] Optionally, the server constructs a unit simulation model for each distributed resource.

[0051] For example, unit simulation models are built for photovoltaic power generation units, permanent magnet direct-drive wind power generation units, energy storage units, and electric vehicle charging pile units.

[0052] Step S204: connecting the unit simulation model of each distributed resource to the initial distribution network simulation model to obtain a new distribution network simulation model.

[0053] The initial distribution network simulation model may refer to an IEEE 33-node standard model, which is a standard distribution network widely used in power system analysis and research. The IEEE 33-node standard model has a typical radiation structure and is suitable for load flow analysis, distributed generation optimization, and reliability assessment of power systems.

[0054] Among them, the new distribution network simulation model can be a simulation model formed by connecting the unit simulation models of various distributed resources to the standard distribution network.

[0055] Optionally, the server connects the unit simulation model of each distributed resource to the IEEE 33-node standard model to obtain a new distribution network simulation model, which can be found in Figure 3 .

[0056] In practical applications, each distributed resource is connected to the AC distribution network through a transformer and a converter, and the basic parameters of each distributed resource unit are given, as shown in Table 1, including rated voltage, rated capacity and rated power. The scale of the simulation model can be expanded according to simulation requirements.

[0057] Table 1

[0058]

[0059] Step S206, using an ideal transformer model based on dynamic response characteristics to partition and decouple the new distribution network simulation model to obtain a first sub-simulation system and a second sub-simulation system; the first sub-simulation system and the second sub-simulation system use different simulation steps; the simulation step of the first sub-simulation system is an integer multiple of the simulation step of the second sub-simulation system; the first sub-simulation system and the second sub-simulation system exchange data through a multi-rate interface.

[0060] Among them, the ideal transformer model (ITM) can partition and decouple the new distribution network simulation model. Figure 4As shown, when a new distribution network simulation model including large-scale cluster access of multiple distributed resources adopts a multi-rate parallel simulation method, different simulation steps can be used for distribution network areas with different time constants. The core of the method of this application is to divide the new distribution network simulation system into different sub-simulation systems according to the dynamic response characteristics. Different sub-simulation systems use different simulation steps. A small step size (FPGA) is used to simulate the transient process of power electronic switches, and a large step size (CPU) is used to simulate the AC system. Different sub-simulation systems can be processed in parallel by multi-core CPUs, and then data can be exchanged using corresponding multi-rate interfaces.

[0061] The theoretical basis of the ideal transformer model is the substitution theorem in circuit theory. According to the Thevenin / Norton equivalence, the ideal transformer model can be divided into voltage source type and current source type. This application uses the voltage source ITM to partition and decouple the new distribution network simulation model, such as Figure 5 As shown, Figure 5 An interface system equivalent circuit of the voltage source type ITM is provided. At the partition cutting point between the electrical system and the control system, and the distributed resources and the distribution network busbar connection point, a controlled power source is used to connect the gap: one side is a controlled current source, and the other side is a controlled voltage source. The controlled signal is the current or voltage signal of the interface on the opposite side.

[0062] The voltage source ITM partitions the wind power generation unit, photovoltaic power generation unit, energy storage unit and electric vehicle charging pile unit in the new distribution network. Different devices have different dynamic response characteristics and different requirements for simulation step size. Considering the application scenario that all distributed resources use detailed unit simulation models, the control modules of each distributed resource, such as photovoltaic MPPT control based on the conductance increment method, optimal tip speed ratio control of wind power and grid-connected control of each converter, are assigned to the CPU for execution to handle the slower control links, and the main circuit containing power electronic devices and the PWM model in the control system are assigned to the FPGA to use the high parallel computing power of the FPGA to handle high-frequency dynamic responses.

[0063] The first sub-simulation system may be a sub-simulation system using a simulation step size T, and the second sub-simulation system may be a sub-simulation system using a simulation step size t, where T = mt, and m is a positive integer greater than or equal to 1. The first sub-simulation system may be understood as a large-step simulation system, using a CPU to implement large-step simulation, and the second sub-simulation system may be understood as a small-step simulation system, using an FPGA to implement small compensation simulation.

[0064] The simulation step in the first sub-simulation system refers to the time interval between two adjacent simulation calculations of the first sub-simulation system during the simulation process, and the simulation step in the second sub-simulation system refers to the time interval between two adjacent simulation calculations of the second sub-simulation system during the simulation process.

[0065] Optionally, the server uses an ideal transformer model to partition and decouple the new distribution network simulation model based on dynamic response characteristics to obtain a first sub-simulation system and a second sub-simulation system.

[0066] Step S208, at the simulation time of the first sub-simulation system, asynchronous communication between the first sub-simulation system and the second sub-simulation system is implemented through a multi-rate interface to perform real-time interaction of simulation data.

[0067] The simulation time of the first sub-simulation system may refer to a simulation calculation time point during the simulation process of the first sub-simulation system. In practical applications, it is necessary to record simulation data of the first sub-simulation system at the simulation calculation time point.

[0068] Optionally, during the simulation of the first sub-simulation system, asynchronous communication between the first sub-simulation system and the second sub-simulation system is implemented through a multi-rate interface to perform real-time interaction of simulation data.

[0069] In the above-mentioned real-time simulation method for large-scale cluster access to distribution network of multiple distributed resources, a unit simulation model for each distributed resource is constructed; the unit simulation model of each distributed resource is connected to the initial distribution network simulation model to obtain a new distribution network simulation model; the ideal transformer model is used to partition and decouple the new distribution network simulation model based on dynamic response characteristics to obtain a first sub-simulation system and a second sub-simulation system; the first sub-simulation system and the second sub-simulation system use different simulation steps; the simulation step of the first sub-simulation system is an integer multiple of the simulation step of the second sub-simulation system; the first sub-simulation system and the second sub-simulation system exchange data through a multi-rate interface; at the simulation moment of the first sub-simulation system, asynchronous communication between the first sub-simulation system and the second sub-simulation system is realized through the multi-rate interface to perform real-time interaction of simulation data; in this way, the resources of the simulation hardware can be reasonably allocated according to the characteristics and simulation requirements of each distributed resource, thereby ensuring the accuracy of the simulation results and the efficiency of the simulation calculation, and by using the voltage source ideal transformer model to decouple the new distribution network simulation model, no complex control architecture is required, and the simulation requirements of large-scale cluster access of multiple distributed resources can be supported, which is convenient for the addition of distributed resource units and the expansion of simulation requirements.

[0070] In order to facilitate the understanding of those skilled in the art, the multi-rate real-time simulation strategy of the present application is described below using an electric vehicle charging pile model as an example. The specific simulation architecture and data interaction are as follows: Figure 6 As shown in the figure, the unit simulation model of the electric vehicle charging pile is segmented. The large and small step interface part of the electric vehicle charging pile unit simulation model is used to exchange data to the opposite system for solution. The two systems on both sides of the decoupled partition choose to exchange data at the large step time, and the solution step T of the large step system is set to a positive integer multiple of the solution step t of the small step system, such as Figure 7 As shown, let the simulation step size of the large step size system be T, and the simulation step size of the small step size system be t, and let T = mt, where m is a positive integer greater than or equal to 1.

[0071] Assume that at time Before the moment, all systems have been solved and the synchronization of large and small step data has been completed. The next solution process can be divided into two parallel tasks. For example, if the large system is divided into n subsystems, it can be divided into n parallel tasks. Generally, the process steps of interactive calculation of large and small step data using linear interpolation method are:

[0072] In from Time has come During the simulation at this moment:

[0073] (1) Large step system simulation one step, The current incident on the small step system at each moment, The historical current source on this side can be and the current of the historical port on this side express;

[0074] (2) Small step system simulation one step, The voltage incident on the large step system at any moment can be and Linear interpolation gives .

[0075] However, conventional linear interpolation methods may encounter the following error problems when processing data interactions between two asynchronous subsystems:

[0076] (1) Interpolation error. Linear interpolation assumes that the change of variables is linear. However, in actual power systems, especially in the application scenario of large-scale cluster access to the distribution network for multiple distributed resources targeted by this application, a large number of distributed resources are connected to the distribution network system through power electronic devices, and the system must have nonlinear dynamics. This linear simplification is likely to lead to errors, especially when the step size difference is large.

[0077] (2) Time domain mismatch. Different subsystem step sizes mean different data sampling rates. Linear interpolation may not accurately capture the rapid changes or dynamic characteristics within each subsystem, resulting in mismatch errors in the time domain.

[0078] (3) Frequency response distortion. If the frequency of change of one subsystem is much higher than that of another subsystem, linear interpolation may not accurately reflect the rapidly changing dynamic characteristics, resulting in frequency response distortion.

[0079] (4) Accumulated error. In long-term simulations, the errors caused by linear interpolation may gradually accumulate, especially when the step size difference is large, which may lead to significant deviations in the final results.

[0080] However, optimizing the above mentioned problems by adjusting the simulation step size is obviously contrary to the original intention of the new distribution network multi-rate real-time simulation. Therefore, this application adopts the Hermite interpolation method to process the data interaction between two asynchronous subsystems. The next embodiment provides the implementation process of the Hermite interpolation method in the technical solution of this application.

[0081] In an exemplary embodiment, at the simulation moment of the first sub-simulation system, asynchronous communication between the first sub-simulation system and the second sub-simulation system is implemented through a multi-rate interface to interact with simulation data, including: at the first simulation moment, receiving the simulation data of the first sub-simulation system at each historical first simulation moment through the second sub-simulation system; the first simulation moment is the simulation moment of the first sub-simulation system; the historical first simulation moment is before the first simulation moment; using the Hermite interpolation method, based on the simulation data of the first sub-simulation system at each historical first simulation moment, determining the simulation data prediction results of the first sub-simulation system at each historical second simulation moment; the second simulation moment is the simulation moment of the second sub-simulation system; the historical second simulation moment is between the first simulation moment and the historical first simulation moment.

[0082] Among them, the Hermite interpolation method is the Hermite interpolation method. For each sub-simulation system (sub-simulation system with different step sizes), the Hermite interpolation method uses Hermite interpolation to smoothly transition its data points. The interpolation can be performed on the step size of the sub-simulation system, or the data points can be unified to an intermediate step size and then interpolated. The two-point cubic Hermite interpolation polynomial can be expressed as:

[0083] ,

[0084] in,

[0085] ,

[0086] ,

[0087] ,

[0088] .

[0089] Optionally, the small-step simulation can be implemented on the FPGA, and the large-step simulation can be implemented on the CPU. At the first simulation moment, i.e., the large-step simulation moment, the simulation data of the large-step simulation system at each historical large-step simulation moment is received by the small-step simulation system, and then the Hermite interpolation method is used for interpolation processing, see Figure 7 ,The specific process is: small step simulation system simulation one step, The voltage incident on the simulation system at a large step length at all times can be and Perform two-point cubic Hermite interpolation to obtain:

[0090] .

[0091] It can be seen that the Hermite interpolation method provides higher smoothness than simple linear interpolation or spline interpolation, and provides a continuous transition at the data points and their derivatives of the large-step simulation system, so that the small-step simulation system can effectively eliminate the solution error when receiving the interactive data from the large-step simulation system for solution, thereby improving the system simulation accuracy.

[0092] In this embodiment, at the first simulation moment, the simulation data of the first sub-simulation system at each historical first simulation moment is received through the second sub-simulation system; the first simulation moment is the simulation moment of the first sub-simulation system; the historical first simulation moment is located before the first simulation moment; the Hermite interpolation method is used to determine the simulation data prediction results of the first sub-simulation system at each historical second simulation moment based on the simulation data of the first sub-simulation system at each historical first simulation moment; the second simulation moment is the simulation moment of the second sub-simulation system; the historical second simulation moment is located between the first simulation moment and the historical first simulation moment; in this way, the Hermite interpolation method can be used in the data interaction between the first sub-simulation system and the second sub-simulation system to process the data interaction of the two asynchronous sub-simulation systems, thereby providing a continuous transition at the data points and their derivatives of the first sub-simulation system, i.e., the large-step simulation system, so that the second sub-simulation system, i.e., the small-step simulation system, effectively eliminates the solution error when receiving the interaction data from the large-step simulation system side for solution, thereby improving the overall simulation accuracy.

[0093] It should be noted that the above is a process implemented based on a large-step simulation system and a small-step simulation system. When multiple large- and small-step sub-simulation systems are divided in actual applications, the two-point cubic Hermite interpolation method can also be used in the interaction of asynchronous interface data.

[0094] In an exemplary embodiment, the unit simulation model of the distributed resources includes a photovoltaic power generation unit simulation model; the photovoltaic power generation unit simulation model adopts a maximum power point tracking algorithm based on the conductance increment method, and controls the photovoltaic cell terminal voltage through a boost converter to track the maximum power point.

[0095] The maximum power point tracking algorithm based on the conductance increment method may refer to an MPPT (Maximum PowerPoint Tracking) algorithm.

[0096] The step-up converter may be a Boost converter.

[0097] Optionally, the photovoltaic power generation unit simulation model adopts a maximum power point tracking algorithm based on the conductance increment method to control the photovoltaic cell terminal voltage through a Boost converter to track its maximum power point.

[0098] In this embodiment, the photovoltaic power generation unit adopts a maximum power point tracking algorithm based on the conductance increment method, and controls the photovoltaic cell terminal voltage through a boost converter to track its maximum power point, thereby achieving efficient power output of the photovoltaic power generation unit.

[0099] In an exemplary embodiment, the unit simulation model of the distributed resources includes a wind power generation unit simulation model; the wind power generation unit simulation model uses a permanent magnet direct-drive wind turbine generator and combines the tip speed ratio tracking technology to optimize the wind speed.

[0100] Among them, the tip speed ratio tracking technology can be TSR (Tip Speed ​​Ratio) technology.

[0101] Optionally, the wind power generation unit simulation model uses a permanent magnet direct-drive wind turbine combined with a tip speed ratio (TSR) tracking technology to optimize the wind speed, and achieves real-time tracking of the wind speed by adjusting the blade angle and rotation speed.

[0102] In this embodiment, the wind power generation unit adopts a permanent magnet direct-drive wind turbine combined with tip speed ratio tracking technology to optimize wind speed. By adjusting the blade angle and rotation speed to achieve real-time tracking of wind speed, the energy capture and conversion efficiency of the wind power generation unit can be maximized.

[0103] In an exemplary embodiment, the unit simulation model of distributed resources includes an energy storage unit simulation model; the energy storage unit simulation model is a lithium battery simulation model; the lithium battery simulation model is connected to a new distribution network simulation model through a three-level midpoint clamped inverter, and the control strategy of the energy storage unit simulation model is a voltage-oriented direct power control strategy.

[0104] Among them, the three-level neutral point clamped inverter refers to the NPC (Neutral Point Clamped) inverter.

[0105] Optionally, the energy storage unit simulation model adopts a lithium battery simulation model, which is connected to the distribution network through a three-level neutral point clamped (NPC) inverter, and its control strategy adopts a direct power control strategy based on voltage orientation.

[0106] In this embodiment, the energy storage unit adopts a lithium battery and is connected to the distribution network through a three-level midpoint clamped inverter. Its control strategy adopts a direct power control strategy based on voltage orientation, which can realize flexible charging and discharging control of the energy storage unit and improve the power conversion efficiency.

[0107] In an exemplary embodiment, the unit simulation model of the distributed resources includes an electric vehicle charging pile unit simulation model; the electric vehicle charging pile unit simulation model adopts a resonant switching converter; the resonant switching converter supports a constant current charging mode and a constant voltage charging mode; the resonant switching converter is used to switch the charging mode according to the charge state of the electric vehicle battery to optimize the charging process of the electric vehicle battery.

[0108] The resonant switching converter may be a full-bridge LLC resonant converter.

[0109] Optionally, the electric vehicle charging pile unit simulation model adopts a full-bridge LLC resonant converter design, supports two main charging modes: constant current and constant voltage, and can switch the charging mode to optimize the charging process according to the charge state of the electric vehicle battery.

[0110] In this embodiment, the electric vehicle charging pile adopts a resonant switching converter, which supports two main charging modes: constant current and constant voltage. It can switch the charging mode according to the charge state of the electric vehicle battery to optimize the charging process, thereby accurately simulating the dynamic characteristics of existing electric vehicle charging piles.

[0111] In an exemplary embodiment, when the unit simulation models of photovoltaic units, permanent magnet direct-drive wind power generation units, energy storage units, and electric vehicle charging piles are connected to the initial distribution network simulation model, the unit simulation model of the static VAR compensator is also connected to the initial distribution network simulation model. The unit simulation model of the static VAR compensator uses a thyristor switched capacitor (TSC) and a thyristor switched capacitor (TCR), which can automatically adjust the working state of the TSC and TCR according to the load and voltage changes of the power grid, thereby realizing dynamic compensation of the reactive power of the power grid.

[0112] In another embodiment, Figure 3 As shown in the figure, a real-time simulation method for large-scale cluster access of multiple distributed resources to the distribution network is provided. Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0113] Step S802: construct a unit simulation model for each distributed resource.

[0114] Step S804: Connect the unit simulation model of each distributed resource to the initial distribution network simulation model to obtain a new distribution network simulation model.

[0115] Step S806, using an ideal transformer model based on dynamic response characteristics to partition and decouple the new distribution network simulation model to obtain a first sub-simulation system and a second sub-simulation system; the first sub-simulation system and the second sub-simulation system use different simulation steps; the simulation step of the first sub-simulation system is an integer multiple of the simulation step of the second sub-simulation system; the first sub-simulation system and the second sub-simulation system exchange data through a multi-rate interface.

[0116] Step S808, at the first simulation moment, receiving the simulation data of the first sub-simulation system at each historical first simulation moment through the second sub-simulation system; the first simulation moment is the simulation moment of the first sub-simulation system; the historical first simulation moment is before the first simulation moment.

[0117] Step S810, using the Hermite interpolation method, based on the simulation data of the first sub-simulation system at each historical first simulation moment, determines the simulation data prediction result of the first sub-simulation system at each historical second simulation moment; the second simulation moment is the simulation moment of the second sub-simulation system; the historical second simulation moment is between the first simulation moment and the historical first simulation moment.

[0118] It should be noted that the specific limitations of the above steps can refer to the specific limitations of a real-time simulation method for accessing a large-scale cluster of multiple distributed resources to a distribution network.

[0119] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0120] Based on the same inventive concept, the embodiment of the present application also provides a real-time simulation device for large-scale cluster access to a distribution network of multiple distributed resources for implementing the real-time simulation method for large-scale cluster access to a distribution network involved in the above-mentioned application. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the real-time simulation device for large-scale cluster access to a distribution network of multiple distributed resources provided below can be referred to the limitations of the real-time simulation method for large-scale cluster access to a distribution network of multiple distributed resources in the above text, and will not be repeated here.

[0121] In an exemplary embodiment, Fig. 9 As shown, a real-time simulation device for large-scale cluster access of multiple distributed resources to a distribution network is provided, comprising: a construction module 902, an access module 904, a decoupling module 906 and an interaction module 908, wherein:

[0122] A construction module 902 is used to construct a unit simulation model for each distributed resource;

[0123] An access module 904 is used to access the unit simulation model of the distributed resources into the initial distribution network simulation model to obtain a new distribution network simulation model;

[0124] The decoupling module 906 is used to partition and decouple the new distribution network simulation model based on the dynamic response characteristics using an ideal transformer model to obtain a first sub-simulation system and a second sub-simulation system; the first sub-simulation system and the second sub-simulation system use different simulation steps; the simulation step of the first sub-simulation system is an integer multiple of the simulation step of the second sub-simulation system; the first sub-simulation system and the second sub-simulation system exchange data through a multi-rate interface;

[0125] The interaction module 908 is used to realize asynchronous communication between the first sub-simulation system and the second sub-simulation system through a multi-rate interface during the simulation of the first sub-simulation system, so as to perform real-time interaction of simulation data.

[0126] In an exemplary embodiment, the interaction module 908 is specifically used to receive, at a first simulation moment, the simulation data of the first sub-simulation system at each historical first simulation moment through the second sub-simulation system; the first simulation moment is the simulation moment of the first sub-simulation system; the historical first simulation moment is located before the first simulation moment; using the Hermite interpolation method, based on the simulation data of the first sub-simulation system at each historical first simulation moment, determine the simulation data prediction results of the first sub-simulation system at each historical second simulation moment; the second simulation moment is the simulation moment of the second sub-simulation system; the historical second simulation moment is located between the first simulation moment and the historical first simulation moment.

[0127] In an exemplary embodiment, the unit simulation model of the distributed resources includes a photovoltaic power generation unit simulation model; the photovoltaic power generation unit simulation model adopts a maximum power point tracking algorithm based on the conductance increment method, and controls the photovoltaic cell terminal voltage through a boost converter to track the maximum power point.

[0128] In an exemplary embodiment, the unit simulation model of the distributed resources includes a wind power generation unit simulation model; the wind power generation unit simulation model uses a permanent magnet direct-drive wind turbine generator and combines the tip speed ratio tracking technology to optimize the wind speed.

[0129] In an exemplary embodiment, the unit simulation model of distributed resources includes an energy storage unit simulation model; the energy storage unit simulation model is a lithium battery simulation model; the lithium battery simulation model is connected to a new distribution network simulation model through a three-level midpoint clamped inverter, and the control strategy of the energy storage unit simulation model is a voltage-oriented direct power control strategy.

[0130] In an exemplary embodiment, the unit simulation model of the distributed resources includes an electric vehicle charging pile unit simulation model; the electric vehicle charging pile unit simulation model adopts a resonant switching converter; the resonant switching converter supports a constant current charging mode and a constant voltage charging mode; the resonant switching converter is used to switch the charging mode according to the charge state of the electric vehicle battery to optimize the charging process of the electric vehicle battery.

[0131] Each module in the above-mentioned real-time simulation device for large-scale cluster access to a distribution network of multiple distributed resources can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0132] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store real-time simulation data applied to large-scale clusters of multiple distributed resources accessing the distribution network. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a real-time simulation method applied to large-scale clusters of multiple distributed resources accessing the distribution network is implemented.

[0133] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0134] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned method for real-time simulation of large-scale clusters of multiple distributed resources connected to a distribution network. Here, the steps of the method for real-time simulation of large-scale clusters of multiple distributed resources connected to a distribution network may be the steps of the method for real-time simulation of large-scale clusters of multiple distributed resources connected to a distribution network in each of the above-mentioned embodiments.

[0135] In one embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the processor executes the steps of the above-mentioned method for real-time simulation of large-scale clusters of multiple distributed resources connected to a distribution network. Here, the steps of the method for real-time simulation of large-scale clusters of multiple distributed resources connected to a distribution network can be the steps of the method for real-time simulation of large-scale clusters of multiple distributed resources connected to a distribution network in each of the above-mentioned embodiments.

[0136] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the processor executes the steps of the above-mentioned method for real-time simulation of large-scale clusters of multiple distributed resources connected to a distribution network. Here, the steps of the method for real-time simulation of large-scale clusters of multiple distributed resources connected to a distribution network can be the steps of the method for real-time simulation of large-scale clusters of multiple distributed resources connected to a distribution network in each of the above-mentioned embodiments.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0138] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A real-time simulation method for large-scale cluster access of multiple distributed resources to a distribution network, characterized in that: The method comprises: Build a unit simulation model for each distributed resource; Connecting the unit simulation model of each of the distributed resources to the initial distribution network simulation model to obtain a new distribution network simulation model; The ideal transformer model is used to partition and decouple the novel distribution network simulation model based on dynamic response characteristics to obtain a first sub-simulation system and a second sub-simulation system; the first sub-simulation system and the second sub-simulation system use different simulation steps; the simulation step of the first sub-simulation system is an integer multiple of the simulation step of the second sub-simulation system; the first sub-simulation system and the second sub-simulation system exchange data through a multi-rate interface; At the simulation time of the first sub-simulation system, asynchronous communication between the first sub-simulation system and the second sub-simulation system is implemented through the multi-rate interface to perform real-time interaction of simulation data.

2. The method according to claim 1, characterized in that At the simulation time of the first sub-simulation system, asynchronous communication between the first sub-simulation system and the second sub-simulation system is realized through the multi-rate interface to perform simulation data interaction, including: At a first simulation moment, receiving simulation data of the first sub-simulation system at each historical first simulation moment through the second sub-simulation system; the first simulation moment is the simulation moment of the first sub-simulation system; the historical first simulation moment is before the first simulation moment; The Hermite interpolation method is used to determine the simulation data prediction results of the first sub-simulation system at each historical second simulation moment based on the simulation data of the first sub-simulation system at each historical first simulation moment; the second simulation moment is the simulation moment of the second sub-simulation system; the historical second simulation moment is between the first simulation moment and the historical first simulation moment.

3. The method according to claim 1, characterized in that The unit simulation model of the distributed resources includes a photovoltaic power generation unit simulation model; The photovoltaic power generation unit simulation model adopts a maximum power point tracking algorithm based on the conductance increment method, and controls the photovoltaic cell terminal voltage through a boost converter to track the maximum power point.

4. The method according to claim 1, characterized in that: The unit simulation model of the distributed resources includes a wind power generation unit simulation model; The wind power generation unit simulation model adopts a permanent magnet direct-drive wind turbine generator and combines the tip speed ratio tracking technology to optimize the wind speed.

5. The method according to claim 1, characterized in that The unit simulation model of the distributed resource includes an energy storage unit simulation model; the energy storage unit simulation model is a lithium battery simulation model; The lithium battery simulation model is connected to the novel power distribution network simulation model through a three-level midpoint clamped inverter, and the control strategy of the energy storage unit simulation model is a direct power control strategy based on voltage orientation.

6. The method according to claim 1, characterized in that The unit simulation model of the distributed resources includes a unit simulation model of an electric vehicle charging pile; The electric vehicle charging pile unit simulation model adopts a resonant switching converter; the resonant switching converter supports a constant current charging mode and a constant voltage charging mode; the resonant switching converter is used to switch the charging mode according to the charge state of the electric vehicle battery to optimize the charging process of the electric vehicle battery.

7. A real-time simulation device for large-scale cluster access of multiple distributed resources to a distribution network, characterized in that: The device comprises: A construction module, used for constructing a unit simulation model for each distributed resource; An access module, used to access the unit simulation model of each of the distributed resources into the initial distribution network simulation model to obtain a new distribution network simulation model; A decoupling module is used to partition and decouple the novel distribution network simulation model based on dynamic response characteristics using an ideal transformer model to obtain a first sub-simulation system and a second sub-simulation system; the first sub-simulation system and the second sub-simulation system use different simulation steps; the simulation step of the first sub-simulation system is an integer multiple of the simulation step of the second sub-simulation system; the first sub-simulation system and the second sub-simulation system exchange data through a multi-rate interface; The interaction module is used to realize asynchronous communication between the first sub-simulation system and the second sub-simulation system through the multi-rate interface at the simulation time of the first sub-simulation system, so as to perform real-time interaction of simulation data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images