Multi-energy coordination control method and system for novel micro-grid

Abstract

The embodiment of the invention provides a multi-energy coordination control method and system for a novel micro-grid, and belongs to the field of multi-energy coordination control for the novel micro-grid. The method comprises the following steps: acquiring micro-grid operation data by using graded acquisition frequency, and preprocessing the acquired micro-grid operation data; dividing the preprocessed micro-grid operation data, and carrying out virtual inertia three-level collaborative support division; establishing a state-space equation, calculating an optimal control variable, and dynamically adjusting parameters of the virtual synchronous generator; and combining the dynamically adjusted virtual synchronous generator parameters with the divided virtual inertia three-stage cooperative support, and performing multi-energy coordination control according to the real-time frequency deviation. Multi-time scale dynamic complementation is realized through a three-stage virtual inertia support architecture; on the basis of VSG and MPC cooperative control, the capacity of the microgrid for coping with new energy fluctuation is remarkably improved, and energy storage loss is reduced while the frequency stability is guaranteed.

Description

Technical Field

[0001] The present invention relates to a multi-energy coordinated control method and system for a novel microgrid. Background Art

[0002] With the significant increase in the penetration rate of new energy (such as photovoltaics and wind power) in microgrids, the traditional frequency regulation mechanism dominated by synchronous generators faces severe challenges. The lack of rotating mass of new energy units has led to a significant decrease in the system's inertia support capacity. The strong random volatility of the output power of new energy units further amplifies this effect, making microgrids prone to frequency instability when encountering power mutations. Especially in scenarios with a high proportion of new energy, sudden power shortages or surpluses can cause the frequency to drop or rise rapidly, and the second-level mechanical response characteristics of traditional synchronous machines cannot match the millisecond-level dynamic requirements, and the system stability faces severe tests. At the same time, although technologies such as virtual inertia control can make up for the inertia gap, they must be at the expense of reactive power regulation costs, which increases the difficulty of multi-objective trade-offs between economy and safety. Summary of the invention

[0003] The purpose of the present invention is to provide a multi-energy coordinated control method and system for a novel microgrid, which combines a virtual synchronous generator (VSG) with a model predictive control (MPC) to dynamically optimize virtual inertia parameters, achieve coordinated scheduling of energy storage equipment with second-level response, flexible load with minute-level regulation, and controllable power supply with hour-level support, and effectively solve the problems of frequency instability in the microgrid and low efficiency of multi-energy coordination.

[0004] In order to achieve the above objectives, the present invention provides a multi-energy coordinated control method and system for a novel microgrid, which can solve the defects of poor low inertia stability, economy and safety of the microgrid in the scenario of high penetration of new energy.

[0005] In the first aspect, in order to achieve the above-mentioned purpose, the present invention provides a multi-energy coordinated control method for a novel microgrid, including using a graded collection frequency to collect microgrid operation data, and preprocessing the collected microgrid operation data; dividing the preprocessed microgrid operation data, and performing a three-level collaborative support division of virtual inertia according to the divided microgrid operation data; establishing a state space equation, calculating the optimal control variable based on the state space equation, and dynamically adjusting the virtual synchronous generator parameters according to the optimal control variable; combining the dynamically adjusted virtual synchronous generator parameters with the divided virtual inertia three-level collaborative support, and performing multi-energy coordinated control according to the real-time frequency deviation.

[0006] Optionally, before collecting microgrid operation data using the graded collection frequency, the multi-energy coordinated control method further includes: grading the collection frequency based on the dynamic response time of the equipment, and grading the collection frequency into high-frequency data, medium-frequency data and low-frequency data.

[0007] Optionally, the dividing the preprocessed microgrid operation data includes: dividing the preprocessed microgrid operation data into energy storage equipment, flexible loads and controllable power supplies.

[0008] Optionally, the three-level collaborative support division of virtual inertia includes primary inertia support, secondary inertia support and third-level backup support, the primary inertia support resource is energy storage equipment, the secondary inertia support resource is a flexible load, and the third-level backup support resource is a controllable power supply.

[0009] Optionally, establishing a state-space equation and calculating the optimal control variables based on the state-space equation include: establishing a state-space equation that describes the dynamic relationship between frequency, inertia, and power variables; based on the state-space equation, solving the objective function with the goal of minimizing frequency deviation and minimizing resource loss to obtain the optimal control variables; dynamically adjusting the frequency deviation weight and power loss weight in the objective function according to the frequency of the real-time operating state.

[0010] Optionally, the dynamic adjustment of the virtual synchronous generator parameters according to the optimal control variable includes: determining the basic inertia value and the adjustment coefficient according to the solved optimal control variable; calculating the deviation between the actual operating frequency and the nominal frequency, and using the deviation as the real-time frequency deviation; based on the real-time frequency deviation, dynamically adjusting the inertia coefficient of the virtual synchronous generator using an adaptive adjustment formula; switching transient damping and steady-state damping according to the frequency oscillation amplitude; and dynamically adjusting the weight of the objective function according to the real-time frequency deviation.

[0011] Optionally, the dynamically adjusted virtual synchronous generator parameters are combined with the divided virtual inertia three-level collaborative support, and multi-energy coordinated control is performed according to the real-time frequency deviation, including: executing the VSG rotor motion equation based on the dynamically adjusted virtual inertia coefficient and damping coefficient; dynamically associating the divided virtual inertia three-level collaborative support with the VSG parameters to form a multi-time scale collaborative control framework; setting the microgrid frequency deviation threshold and response level, which are set as follows: first-level inertia support: microgrid real-time frequency deviation ≤ first threshold, at this time only the energy storage device is activated for discharge / charging; second-level inertia support: first threshold < microgrid real-time frequency deviation ≤ second threshold, at this time the energy storage device and flexible load are activated at the same time; third-level standby support: microgrid real-time frequency deviation > second threshold, at this time the controllable power supply is started.

[0012] Optionally, the dynamically adjusted virtual synchronous generator parameters are combined with the divided three-level collaborative support of virtual inertia, and multi-energy coordinated control is performed according to the real-time frequency deviation, and also includes: associating the energy storage SOC with the releasable inertia value, establishing a nonlinear mapping function to dynamically limit the virtual inertia; deploying edge computing nodes in the microgrid, and using the edge computing nodes to perform high-frequency control; combining the edge computing nodes with MPC, and performing multi-energy coordinated control through multi-layer resource collaboration and parameter adaptation.

[0013] Optionally, the preprocessing includes missing value processing, outlier filtering, format standardization, and high-frequency noise smoothing.

[0014] On the other hand, the present invention provides a multi-energy coordinated control system for a novel microgrid, which is used to implement a multi-energy coordinated control method for a novel microgrid. The system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the multi-energy coordinated control method for the novel microgrid.

[0015] The above technical solution realizes dynamic complementarity at multiple time scales through a three-level virtual inertia support architecture (energy storage equipment responds in seconds, flexible loads adjust in minutes, and controllable power supplies provide a guarantee in hours); dynamically optimizes virtual inertia parameters based on VSG and MPC collaborative control, combines adaptive weight balance frequency deviation suppression and power loss optimization; introduces energy storage SOC association mechanism to prevent overcharging and over-discharging; deploys edge-central collaborative architecture, and realizes high-frequency response and global optimization coordination through hierarchical decision-making and event-triggered communication. This solution can significantly improve the ability of microgrids to cope with fluctuations in new energy sources, reduce energy storage losses while ensuring frequency stability, and provide key technical support for the safe and economical operation of new power systems.

[0016] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention but do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a flow chart of a multi-energy coordinated control method for a novel microgrid.

[0018] Figure 2 It is a flow chart of three-level collaborative support and control of virtual inertia. DETAILED DESCRIPTION

[0019] The following is combined with Figure 1 -Attached Figure 2 The specific implementation of the embodiment of the present invention is described in detail. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0020] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. In the embodiments of this application, some existing solutions in the industry such as certain software, components, and models may be mentioned, which should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0021] The inventors of this application discovered in the process of realizing the present invention that the multi-energy coordinated control of the new microgrid in the prior art usually adopts a fixed acquisition frequency, which has redundant data transmission and high storage costs, and the virtual inertia collaborative control is insufficient, and the prior art fails to make full use of low-cost resources to respond first, resulting in frequent use of high-cost resources and increased system operating costs. In response to frequency mutations, continuous fluctuations and long-term power shortages, there is a lack of multi-level support strategies, and it is difficult to take targeted measures under frequency fluctuations of different severity. The prior art adopts a single VSG adjustment, and the VSG adjustment usually adopts a fixed value, and it is impossible to dynamically adjust the inertia and damping coefficient according to the real-time frequency deviation, resulting in a slow response speed of the system when the frequency mutates, and high energy consumption during steady-state operation; and the lack of effective virtual inertia support leads to a decrease in system frequency stability. In the event of frequency oscillation or sudden drop caused by new energy fluctuations, there is a lack of flexible control strategies, and it is difficult to quickly restore system stability.

[0022] Example 1 Reference Figure 1-Figure 2 , which is the first embodiment of the present invention, and provides a multi-energy coordinated control method for a novel microgrid, comprising: S100: Collecting microgrid operation data using the classified collection frequencies, and preprocessing the collected microgrid operation data.

[0023] Specifically, based on the dynamic response time of the equipment, the collection frequency is divided into high-frequency data, medium-frequency data and low-frequency data; high-frequency data such as grid frequency (50 times / second); medium-frequency data such as diesel generator output (1 time / 10 seconds); low-frequency data such as light intensity (1 time / hour).

[0024] Furthermore, according to the operation requirements of the microgrid, data is collected and preprocessed, and the collected data includes energy storage device data, flexible load data, controllable power data, and environmental and power grid data. Energy storage device data includes energy storage SOC (State of Charge, remaining power), charge and discharge current, voltage, temperature, response delay time (such as lithium battery ≤ 10ms); flexible load data includes air conditioning power, adjustable power range, adjustment delay time, temperature setting value, charging pile real-time power, switch status, etc.; controllable power data includes diesel generator speed, output, fuel consumption rate, start and stop status, minimum start and stop time, etc.; environmental and power grid data includes light intensity, wind speed, power grid frequency, voltage amplitude, etc.

[0025] Furthermore, the collected data are processed for missing values ​​and outliers, and the data format is standardized.

[0026] Preferably, the high-frequency noise data is smoothed, and the window length is set to 5 sampling points.

[0027] Preferably, high-frequency, medium-frequency, and low-frequency data are collected and classified to reduce redundant data transmission and storage costs, covering multi-dimensional data such as energy storage, load, power supply, and environment, and providing comprehensive input for virtual inertia collaborative control.

[0028] S200: dividing the pre-processed microgrid operation data, and performing three-level collaborative support division of virtual inertia according to the divided microgrid operation data.

[0029] Specifically, different resources have different response speeds, capacities, and costs. Based on the preprocessed data, the preprocessed data in the microgrid is divided according to the resource type (energy storage, air conditioning, charging piles, diesel generators, etc.) to cope with different levels of frequency fluctuations.

[0030] Furthermore, the resource microgrid operation data are divided into energy storage equipment, flexible loads and controllable power sources according to resource types; and the three-level collaborative support of virtual inertia is divided.

[0031] (1) Level 1 inertia support (seconds): The data resource type is energy storage equipment (lithium batteries, supercapacitors, etc.); the task is to quickly suppress frequency mutations and provide instantaneous power compensation. For example, if the frequency of the microgrid suddenly drops by 0.2 Hz, the energy storage equipment will discharge to supplement power within 10 ms.

[0032] (2) Secondary inertia support (minute level): The data resource type is flexible load (air conditioners, charging piles, etc.); the task is to respond to continuous fluctuations and absorb or release energy through power regulation. For example, if the frequency of the microgrid is continuously lower than the standard value within 5 minutes, the air conditioner automatically increases the temperature to reduce electricity consumption.

[0033] (3) Level 3 backup support (hourly): The data resource type is a controllable power source such as a diesel generator. The task is to respond to long-term power shortages. For example, when the light intensity or wind speed continues to be lower than the set value, that is, the frequency of the microgrid continues to be abnormal, the diesel generator is started to supplement the power grid.

[0034] Preferably, low-cost resources (flexible loads) respond first to reduce the loss of high-cost resources (energy storage, diesel engines).

[0035] Preferably, according to the response speed, capacity and cost of different resources, energy storage, flexible load and controllable power supply are reasonably divided into data resource types, so that each type of resource can play a role in its most suitable scenario; ensuring that the system can make the most appropriate response to frequency fluctuations in each time scale, improving the stability and economy of the system. A multi-level support strategy is adopted to take targeted measures for frequency fluctuations of different severity, which can not only quickly suppress sudden frequency changes, but also cope with continuous fluctuations.

[0036] S300: Establishing a state space equation, calculating an optimal control variable based on the state space equation, and dynamically adjusting the virtual synchronous generator parameters according to the optimal control variable.

[0037] Specifically, in a high-proportion new energy grid, the inertia support of traditional synchronous generators is weakened, resulting in a decrease in system frequency stability. To this end, a collaborative method of VSG (virtual synchronous generator) and MPC (model predictive control) is adopted to achieve frequency stability control and energy optimization scheduling through dynamic optimization of virtual inertia parameters and multi-time scale resource coordination.

[0038] Furthermore, a state space equation is established to describe the dynamic relationship between variables such as frequency, inertia, and power. The state space equation is as follows:

[0039] in, represents the state vector at time k+1, including variables such as frequency, voltage and resource output power; A is the state transfer matrix, which is used to describe the evolution law of state variables; represents the set of state variables at time k; B represents the input matrix, which is used to describe the influence of control input on state variables; The control inputs at k include VSG key parameters inertia J and damping D, as well as other resource scheduling variables (energy storage power , Load adjustment wait).

[0040] Furthermore, based on the state-space equations, MPC calculations can achieve control instructions that minimize frequency deviation and power loss, and find the optimal control input with the goal of minimizing frequency deviation and resource loss. , the objective function is as follows:

[0041] Among them, u represents the entire optimization control sequence, and the solution is The control inputs at k include VSG key parameters inertia J and damping D, as well as other resource scheduling variables (energy storage power , Load adjustment etc.); N is the rolling optimization step length, indicating how many time steps MPC will optimize in the future; is the frequency deviation, which indicates the difference between the actual operating frequency and the nominal frequency at the current time k of the system; is the power loss, which indicates the energy loss caused by factors such as charging and discharging of energy storage equipment and line loss; Represents the frequency deviation weight, which is used to balance the control objective’s focus on frequency stability; Represents the power loss weight, which is used to balance the control objective with respect to economy.

[0042] Furthermore, the objective function weight is adjusted according to the frequency deviation amplitude of the real-time operating state. and , for example, in the case of large disturbances, the frequency deviation is suppressed first, increasing ; In steady state, focus on optimizing resource loss and increase .

[0043] Preferably, MPC rolling optimization obtains the optimal control variables , including the key parameters of VSG, inertia J and damping D, to ensure optimal operation.

[0044] Preferably, the dynamic evolution of variables such as frequency and power is quantified through state-space equations, providing a mathematical basis for predictive control. The MPC objective function (minimizing frequency deviation and power loss) can balance stability and economy, and adapt to the needs of different scenarios (such as giving priority to frequency stability during large disturbances). Weight adaptation (dynamic adjustment and ) can enhance control flexibility.

[0045] Furthermore, the optimal control variables calculated by MPC are It is used to dynamically adjust the VSG key parameters inertia coefficient J and damping coefficient D to achieve inertia adaptation and damping switching.

[0046] Furthermore, the inertia adaptation is to increase J to enhance the instantaneous support when the frequency changes suddenly, and to reduce the loss by reducing J in steady state.

[0047]

[0048] Where J is the virtual inertia coefficient, Indicates the basic inertia value of VSG, the default value used when there is no disturbance; Indicates the adjustment coefficient, which affects the sensitivity of inertia to frequency deviation changes. The larger it is, the faster the inertia adjustment speed is; Indicates the current frequency deviation, Indicates the maximum frequency deviation that is set, which is used for normalization processing to prevent the parameter from being too large.

[0049] When the frequency change is small, Approaching 0, tanh(0)=0, J≈0 (close to the minimum inertia), at this time it can reduce losses and thus improve economy.

[0050] When the frequency changes suddenly becomes larger, tanh(.) approaches 1, making J≈ (Increased inertia), which can suppress frequency fluctuations and improve stability.

[0051] Furthermore, the damping switching is to switch between transient damping (suppressing oscillation) and steady-state damping (energy-saving operation) according to the oscillation amplitude. The damping switching formula is as follows:

[0052] in, is the transient damping coefficient, which is large (e.g. =20), used to quickly suppress frequency oscillation; is the steady-state damping coefficient, which is small (e.g. =5), used to reduce the loss during steady-state operation; Indicates the current frequency deviation; is the oscillation amplitude threshold (e.g. =0.2Hz), set according to system stability requirements.

[0053] Preferably, transient damping or steady-state damping is selected according to the frequency deviation threshold, so that large damping can quickly suppress oscillation and small damping can reduce operating losses.

[0054] Preferably, according to the optimal control variables calculated by MPC, the key parameters of VSG (virtual inertia J and damping D) are dynamically adjusted so that the system can respond quickly when the frequency changes suddenly and reduce energy consumption during steady-state operation. Through inertia adaptation and damping switching, it can not only achieve instantaneous high-power support, but also ensure that the system maintains efficient and economical regulation effects under different operating conditions. Dynamic adjustment of VSG parameters enables the microgrid to quickly and flexibly adjust the operating state when encountering frequency fluctuations, which not only ensures system stability, but also optimizes energy utilization efficiency and improves the overall response speed.

[0055] S400: Combine the dynamically adjusted virtual synchronous generator parameters with the divided virtual inertia three-level collaborative support, and perform multi-energy coordinated control according to the real-time frequency deviation.

[0056] Furthermore, VSG technology simulates the rotor motion equation of the synchronous generator through the power electronic converter, provides virtual inertia and damping support for the new energy grid-connected system to solve the low inertia problem, and achieves synchronization with the grid through angular velocity and electrical angle. The VSG control equation is as follows:

[0057] Among them, J is the virtual inertia coefficient, which is used to simulate the rotational inertia of the synchronous generator rotor and determines the response speed of the system to frequency changes. D is the damping coefficient, which indicates the viscous resistance of the rotor of the system and determines the attenuation speed of the frequency oscillation. It is the mechanical torque used to simulate the torque provided by the prime mover of the synchronous generator, corresponding to the power output of the energy storage device; It is the electromagnetic torque, which is used to simulate the electromagnetic resistance torque generated inside the synchronous generator, corresponding to the power consumed by the system load. is the rate of change of angular velocity, which indicates the rate of change of the system's rotational angular velocity over time; is the angular velocity corresponding to the frequency of the microgrid; Represents the electrical angle position in the synchronous rotation reference frame.

[0058] Preferably, the VSG affects the frequency stability of the microgrid system by responding to power changes.

[0059] Preferably, VSG can adapt to different frequency disturbance scenarios; when the frequency changes suddenly (such as a sudden drop in the output of new energy), the instantaneous power of the energy storage device is released by increasing J to suppress the frequency drop rate; when the frequency oscillates, the oscillation attenuation is accelerated by adaptively adjusting D to avoid stability problems such as subsynchronous resonance.

[0060] Preferably, in addition to VSG parameter optimization, MPC also optimizes the coordinated scheduling of energy storage, flexible loads, and controllable power sources through multi-time scale coordinated control.

[0061] Furthermore, multi-timescale coordinated control includes triggering different levels of resources according to the frequency deviation amplitude: When the real-time frequency deviation of the microgrid is ≤ the first threshold of 0.2Hz, in order to cope with high-frequency power fluctuations and prevent the VSG from being subjected to excessive instantaneous impact, only the first-level inertia support is activated, and the energy storage device is immediately discharged or charged to slow down the system frequency deviation; When the first threshold 0.2Hz < microgrid real-time frequency deviation ≤ the second threshold 0.5Hz, the first-level inertia support and the second-level inertia support are activated at the same time, and the MPC immediately activates the energy storage device to discharge / charge, and alleviates the power fluctuation on the medium time scale by adjusting the controllable load (such as air conditioning, electric vehicle charging); When the real-time frequency deviation of the microgrid is greater than the second threshold of 0.5Hz, the third-level backup support is activated. When the output of new energy is insufficient, the backup units are gradually dispatched to balance the long-term power shortage and ensure the overall energy supply and demand balance of the system.

[0062] Preferably, for example, in the case of a sudden drop in frequency: large-scale photovoltaic output suddenly drops, resulting in a decrease in system frequency; the control strategy is that MPC predicts a decrease in new energy output and immediately increases the VSG inertia J; starts the energy storage device to discharge to provide instantaneous power support; appropriately adjusts the flexible load (such as reducing the air conditioning power) to reduce load impact; and gradually dispatches the controllable power supply to improve the system power support capability.

[0063] Preferably, for example, fluctuations in renewable energy may cause grid oscillations, affecting stability; the control strategy is to use MPC to calculate the optimal damping coefficient D and dynamically adjust the damping control strategy of the VSG.

[0064] Furthermore, after VSG provides instantaneous inertia support, MPC optimizes subsequent resource allocation to avoid overcharging / over-discharging of energy storage.

[0065] Preferably, the energy storage SOC is associated with the releasable inertia value to establish a nonlinear mapping function.

[0066]

[0067] in, is the virtual inertia value actually output, It is the maximum virtual inertia value that can be provided, determined by the physical characteristics of the energy storage device.

[0068] When the energy storage SOC ≥ 20%, = At this time, the battery is fully charged and the energy storage device is fully involved in the regulation.

[0069] When 10%<storage SOC<20%, = , at this time, it can avoid excessive discharge of the battery and extend the battery life.

[0070] When the energy storage SOC is less than 10%, =0, which can prevent the battery from deep discharge and causing irreversible damage.

[0071] Furthermore, edge computing nodes are deployed in the microgrid, directly connected to energy storage, loads and power supply equipment, to build a two-layer collaborative architecture of "edge fast response-central global optimization". Based on pre-processed data, resource partitioning strategies and dynamic optimization parameters, edge nodes directly execute high-frequency control actions to achieve second-level frequency disturbance suppression; at the same time, MPC optimizes and coordinates long-term scheduling such as diesel engine start and stop and load group control; the two collaborate through event-triggered communication and adaptive resource mapping, the edge layer synchronizes key states such as energy storage SOC to MPC in real time, and the central layer dynamically adjusts the global target weight, which not only ensures high-frequency response accuracy (delay ≤ 10ms), but also avoids resource conflicts, and ultimately achieves the dual goals of microgrid frequency stability and economic operation.

[0072] Preferably, the dynamically adjusted VSG parameters are combined with the three-level collaborative support of virtual inertia to achieve collaborative scheduling of multi-time scale resources and ensure that the system strikes a balance between second-level response and long-cycle scheduling. The coordinated effects of energy storage, flexible loads and controllable power sources are comprehensively utilized to effectively solve the problems of new energy fluctuations and low inertia, and improve frequency stability and energy scheduling efficiency. By associating the energy storage SOC with the virtual inertia value, a nonlinear mapping function is established to avoid over-discharge or overcharging of energy storage equipment, thereby extending battery life and improving system reliability. The edge nodes synchronize key states such as energy storage SOC to the central layer in real time, and the central layer dynamically adjusts the global target weights to ensure that the system can achieve the dual goals of frequency stability and economic operation under different operating scenarios.

[0073] The present invention also provides a multi-energy coordinated control system for a novel microgrid, which is used to implement a multi-energy coordinated control method for a novel microgrid. The system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the multi-energy coordinated control method for the novel microgrid.

[0074] An embodiment of the present invention provides a storage medium having a program stored thereon, which, when executed by a processor, implements the multi-energy coordinated control method for a novel microgrid.

[0075] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes the multi-energy coordinated control method for a new microgrid when running.

[0076] An embodiment of the present invention provides a device, the device includes a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements a multi-energy coordinated control method for a novel microgrid when executing the program. The device in this article may be a server, a PC, a PAD, a mobile phone, etc.

[0077] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the steps of a multi-energy coordinated control method for a new microgrid.

[0078] Those skilled in the art will appreciate that the embodiments of the present application may provide methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0079] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0080] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0081] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0082] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0083] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0084] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0085] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0086] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A multi-energy coordinated control method for a novel microgrid, characterized in that: include: Using the classified collection frequency, the microgrid operation data is collected, and the collected microgrid operation data is preprocessed; The pre-processed microgrid operation data is divided, and the three-level collaborative support division of virtual inertia is performed according to the divided microgrid operation data; Establish state space equations, calculate optimal control variables based on the state space equations, and dynamically adjust the parameters of the virtual synchronous generator according to the optimal control variables; The dynamically adjusted virtual synchronous generator parameters are combined with the divided virtual inertia three-level collaborative support, and multi-energy coordinated control is performed according to the real-time frequency deviation.

2. The multi-energy coordinated control method for a novel microgrid according to claim 1 is characterized in that: Before collecting the microgrid operation data using the classified collection frequency, the multi-energy coordinated control method further includes: The acquisition frequency is classified into high-frequency data, medium-frequency data and low-frequency data based on the dynamic response time of the device.

3. The multi-energy coordinated control method for a novel microgrid according to claim 1 is characterized in that: The division of the pre-processed microgrid operation data includes: The preprocessed microgrid operation data is divided into energy storage equipment, flexible load and controllable power supply.

4. The multi-energy coordinated control method for a novel microgrid according to claim 1 is characterized in that: The virtual inertia three-level collaborative support is divided into a first-level inertia support, a second-level inertia support and a third-level standby support. The first-level inertia support resource is an energy storage device, the second-level inertia support resource is a flexible load, and the third-level backup support resource is a controllable power source.

5. The multi-energy coordinated control method for a novel microgrid according to claim 1 is characterized in that: The step of establishing a state space equation and calculating an optimal control variable based on the state space equation includes: Establish state-space equations that describe the dynamic relationship between frequency, inertia, and power variables; Based on the state space equation, the objective function is solved with the goal of minimizing frequency deviation and minimizing resource loss to obtain the optimal control variables; According to the frequency of the real-time operating status, the frequency deviation weight and power loss weight in the objective function are dynamically adjusted.

6. The multi-energy coordinated control method for a novel microgrid according to claim 1 is characterized in that: The dynamically adjusting the parameters of the virtual synchronous generator according to the optimal control variables comprises: According to the solved optimal control variables, determine the basic inertia value and adjustment coefficient; Calculate the deviation between the actual operating frequency and the nominal frequency, and use the deviation as the real-time frequency deviation; Based on the real-time frequency deviation, dynamically adjusting the inertia coefficient of the virtual synchronous generator using an adaptive adjustment formula; Switch between transient damping and steady-state damping according to the frequency oscillation amplitude; The weight of the objective function is dynamically adjusted according to the real-time frequency deviation.

7. The multi-energy coordinated control method for a novel microgrid according to claim 1 is characterized in that: The method combines the dynamically adjusted virtual synchronous generator parameters with the divided virtual inertia three-level collaborative support, and performs multi-energy coordinated control according to the real-time frequency deviation, including: Based on the dynamically adjusted virtual inertia coefficient and damping coefficient, the VSG rotor motion equation is executed; The divided virtual inertia three-level collaborative support is dynamically associated with the VSG parameters to form a multi-time scale collaborative control framework; Set the microgrid frequency deviation threshold and response level as follows: Level 1 inertia support: When the real-time frequency deviation of the microgrid is less than or equal to the first threshold, only the energy storage device is activated for discharge / charging. Secondary inertia support: the first threshold < microgrid real-time frequency deviation ≤ the second threshold, at this time, the energy storage device and the flexible load are activated at the same time; Level 3 backup support: When the real-time frequency deviation of the microgrid is greater than the second threshold, the controllable power supply is started.

8. The multi-energy coordinated control method for a novel microgrid according to claim 1 is characterized in that: The method combines the dynamically adjusted virtual synchronous generator parameters with the divided virtual inertia three-level collaborative support, and performs multi-energy coordinated control according to the real-time frequency deviation, and further includes: The energy storage SOC is associated with the releasable inertia value, and a nonlinear mapping function is established to dynamically limit the virtual inertia; Deploy edge computing nodes in the microgrid and use the edge computing nodes to perform high-frequency control; The edge computing node is combined with MPC to perform multi-energy coordinated control through multi-layer resource collaboration and parameter adaptation.

9. The multi-energy coordinated control method for a novel microgrid according to claim 2 is characterized in that: The preprocessing includes missing value processing, outlier filtering, format standardization, and high-frequency noise smoothing.

10. A multi-energy coordinated control system for a new microgrid, characterized in that: The system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement a multi-energy coordinated control method for a novel microgrid according to any one of claims 1 to 9.

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