Multi-energy coordinated control method and system for new micro-grid

Abstract

The embodiment of the application provides a kind of multi-energy coordination control method and system for new micro-grid, belong to the multi-energy coordination control field for new micro-grid;The method comprises using the collection frequency after grading, collects micro-grid operation data, and the micro-grid operation data collected is preprocessed;The micro-grid operation data after preprocessing is divided, and is divided into three levels of virtual inertia collaborative support;Establish state space equation, calculate optimal control variable, and dynamically adjust virtual synchronous generator parameter;The virtual synchronous generator parameter after dynamic adjustment is combined with the virtual inertia three-level collaborative support after division, and according to real-time frequency deviation, multi-energy coordination control is carried out, and the application is realized by three levels of virtual inertia support structure Multi-time scale dynamic complementation;Based on VSG and MPC collaborative control, the ability of micro-grid to deal with new energy fluctuation is significantly improved, while ensuring frequency stability, energy storage loss is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to a multi-energy coordinated control for a new micro-grid, in particular to a multi-energy coordinated control method and system for a new micro-grid. BACKGROUND

[0002] With the significant increase of new energy (such as photovoltaic and wind power) penetration in micro-grid, the traditional frequency regulation mechanism dominated by synchronous generators faces severe challenges. New energy units lack rotational inertia, resulting in a significant decrease in system inertia support capability. The strong random fluctuation of new energy unit output power further amplifies this effect, making the micro-grid prone to frequency instability when encountering power surges. Especially in the high proportion of new energy scenarios, sudden power shortage or surplus will cause the frequency to drop or climb rapidly, and the second-level mechanical response characteristics of traditional synchronous machines cannot match the millisecond-level dynamic demand, and the system stability is severely tested. At the same time, although virtual inertia control technology can make up for the inertia gap, it needs to pay the cost of reactive power regulation, which exacerbates the difficulty of multi-objective trade-off between economy and safety. SUMMARY

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

[0004] In order to achieve the above purpose, the present application provides a multi-energy coordinated control method and system for a new micro-grid, which can solve the problems of low inertia stability, poor economy and safety in the high proportion of new energy penetration scenarios in micro-grid.

[0005] In the first aspect, in order to achieve the above purpose, the present application provides a multi-energy coordinated control method for a new micro-grid, which comprises collecting micro-grid operation data by using the collected frequency after grading, and preprocessing the collected micro-grid operation data; dividing the preprocessed micro-grid operation data, and dividing the virtual inertia three-level collaborative support according to the divided micro-grid 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 the micro-grid operation data is collected at the classified collection frequency, the multi-energy coordinated control method further comprises: classifying the collection frequency based on the dynamic response time of the equipment, and classifying the collection frequency into high-frequency data, medium-frequency data and low-frequency data.

[0007] Optionally, the pre-processed micro-grid operation data is divided into energy storage equipment, flexible load and controllable power supply.

[0008] Optionally, the virtual inertia three-level collaborative support division includes a first inertia support, a second inertia support and a third standby support, the first inertia support resource is an energy storage equipment, the second inertia support resource is a flexible load, and the third standby support resource is a controllable power supply.

[0009] Optionally, the state space equation is established, and the optimal control variable is calculated based on the state space equation, which comprises: establishing a state space equation describing the dynamic relationship between frequency, inertia and power variables; based on the state space equation, solving a target function with the minimum frequency deviation and the minimum resource loss as the target to obtain the optimal control variable; and dynamically adjusting the frequency deviation weight and the power loss weight in the target function according to the real-time operating frequency.

[0010] Optionally, the virtual synchronous generator parameters are dynamically adjusted according to the optimal control variable, which comprises: determining a basic inertia value and an adjustment coefficient according to the solved optimal control variable; calculating the deviation between the actual operating frequency and the nominal frequency, and taking 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 the transient damping and the steady-state damping according to the frequency oscillation amplitude; and dynamically adjusting the weight of the target 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 the multi-energy coordinated control is performed according to the real-time frequency deviation, which comprises: executing a VSG rotor motion equation based on the dynamically adjusted virtual inertia coefficient and the 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 a micro-grid frequency deviation threshold and a response level, and setting as follows: first inertia support: micro-grid real-time frequency deviation ≤ first threshold, at this time only the energy storage equipment is activated for discharging / charging; second inertia support: first threshold < micro-grid real-time frequency deviation ≤ second threshold, at this time the energy storage equipment and the flexible load are activated simultaneously; third standby support: micro-grid real-time frequency deviation > second threshold, at this time the controllable power supply is started.

[0012] Optionally, the dynamically adjusted virtual synchronous generator parameter is combined with the divided virtual inertia three-level cooperative support, and multi-energy coordinated control is performed according to the real-time frequency deviation, and the method further comprises: associating the energy storage SOC with the releasable inertia value, establishing a nonlinear mapping function to dynamically limit the virtual inertia; deploying an edge computing node in the microgrid, and using the edge computing node to perform high-frequency control; combining the edge computing node with the MPC, and performing multi-energy coordinated control through multi-layer resource cooperation and parameter self-adaptation.

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

[0014] In another aspect, the application provides a multi-energy coordinated control system for a new microgrid, for implementing the multi-energy coordinated control method for a new microgrid, the system comprising a control module, the control module comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the multi-energy coordinated control method for a new microgrid.

[0015] The above technical solution realizes multi-time scale dynamic complementation through a three-level virtual inertia support architecture (energy storage device responds at a second level, flexible load adjusts at a minute level, and controllable power source underpins at an hour level); based on VSG and MPC cooperative control, dynamically optimizes virtual inertia parameters, and balances frequency deviation suppression and power loss optimization through adaptive weight; introduces an energy storage SOC association mechanism to prevent overcharging and overdischarging; deploys an edge-central cooperative architecture to realize high-frequency response and overall optimization through hierarchical decision-making and event-triggered communication. The scheme can significantly improve the ability of the microgrid to respond to new energy fluctuations, while ensuring frequency stability and reducing energy storage loss, thereby providing key technical support for safe and economic operation of a new power system.

[0016] Other features and advantages of the application will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, and are used together with the following specific embodiments to explain the application, but do not constitute a limitation on the application. In the drawings:

[0018] Figure 1 is a flowchart of a multi-energy coordinated control method for a new microgrid.

[0019] Figure 2 is a virtual inertia three-level cooperative support regulation flowchart. DETAILED DESCRIPTION

[0020] The following will be described in conjunction with the drawingsFigure 1 -Appendix Figure 2 The specific embodiments of the present application are described in detail. It should be understood that the specific embodiments described herein are merely intended to illustrate and explain the embodiments of the present application, and are not intended to limit the embodiments of the present application.

[0021] It should be noted that the acquisition, transmission, storage, use, processing and the like of data in the technical scheme of the present application comply with the relevant provisions of national laws and regulations. In the embodiments of the present application, some existing industry solutions such as software, components, models and the like may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the implementation of the technical scheme of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0022] The present inventors found in the process of realizing the present application that the multi-energy coordinated control of the new micro-grid in the prior art usually adopts a fixed collection frequency, which has redundant data transmission and high storage cost, and the virtual inertia collaborative control is insufficient, and the prior art fails to fully utilize low-cost resources to respond preferentially, resulting in frequent use of high-cost resources and increased system operation cost. 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 different severity of frequency fluctuations. The prior art uses a single VSG adjustment, and the VSG adjustment usually uses a fixed value, which cannot dynamically adjust the inertia and damping coefficient according to the real-time frequency deviation, resulting in slow response speed of the system in the case of frequency mutation, high energy consumption in steady-state operation; and lack of effective virtual inertia support, resulting in a decrease in system frequency stability. In the case 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.

[0023] Embodiment 1

[0024] Reference Figures 1-2 As a first embodiment of the present application, the embodiment provides a multi-energy coordinated control method for a new micro-grid, comprising:

[0025] S100: Collect micro-grid operation data using a hierarchical collection frequency, and pre-process the collected micro-grid operation data.

[0026] 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; the high-frequency data is, for example, the grid frequency (50 times / second); the medium-frequency data is, for example, the diesel generator output (1 time / 10 seconds); and the low-frequency data is, for example, the light intensity (1 time / hour).

[0027] Further, according to the micro-grid operation requirements, data is collected and pre-processed, including energy storage device data, flexible load data, controllable power supply data, and environmental and grid data. The energy storage device data includes the state of charge (SOC), charging and discharging current, voltage, temperature, response delay time (such as lithium battery ≤10ms); flexible load data includes air conditioner power, adjustable power range, adjustment delay time, temperature set value, real-time power of charging pile, switch state, etc.; controllable power supply data includes diesel generator speed, output, fuel consumption rate, start-stop state, minimum start-stop time, etc.; environmental and grid data includes light intensity, wind speed, grid frequency, voltage amplitude, etc.

[0028] Further, the collected data is processed for missing values and filtered for outliers, and the data is standardized for format.

[0029] Preferably, high-frequency noise data is smoothed, with a window length of 5 sampling points.

[0030] Preferably, high-frequency, medium-frequency, and low-frequency data are classified and collected to reduce redundant data transmission and storage costs, covering multi-dimensional data such as energy storage, load, power supply, and environment, providing comprehensive input for virtual inertia collaborative control.

[0031] S200: The pre-processed micro-grid operation data is divided, and virtual inertia three-level collaborative support is divided according to the divided micro-grid operation data.

[0032] Specifically, different resources have different response speeds, capacities, and costs. According to the pre-processed data, the pre-processed data in the micro-grid is divided according to resource types (energy storage, air conditioner, charging pile, diesel generator, etc.) to respond to different levels of frequency fluctuations.

[0033] Further, the resource micro-grid operation data is divided into energy storage devices, flexible loads, and controllable power supplies according to resource types; and virtual inertia three-level collaborative support is divided.

[0034] (1) Primary inertia support (second level): data resource type is energy storage device (lithium battery, super capacitor, etc.); task is to quickly suppress frequency mutation and provide instantaneous power compensation, such as micro-grid frequency suddenly dropping 0.2Hz, energy storage device discharging power within 10ms.

[0035] (2) Secondary inertia support (minute level): data resource type is flexible load (air conditioner, charging pile, etc.); task is to respond to continuous fluctuations by absorbing or releasing energy through power regulation, such as micro-grid frequency continuously below standard value within 5 minutes, air conditioner automatically adjusts temperature to reduce power consumption.

[0036] (3) Three-level backup support (hour level): The data resource type is controllable power supply such as diesel generator; the task is to cope with long-term power shortage, such as when the light intensity or wind speed is continuously lower than the set value, that is, the micro-grid frequency is continuously abnormal, and the diesel generator is started to supplement the power grid.

[0037] Preferably, low-cost resources (flexible load) are preferentially responded, and the loss of high-cost resources (energy storage, diesel engine) is reduced.

[0038] Preferably, according to the response speed, capacity and cost of different resources, the energy storage, flexible load and controllable power supply are reasonably divided into data resource types, so that various resources can play a role in their most suitable scenarios; ensure that the system can respond to frequency fluctuations in various time scales, and improve the stability and economy of the system. Adopting multi-level support strategy, targeted measures are taken for different severity of frequency fluctuations, which can quickly suppress sudden frequency changes and cope with continuous fluctuations.

[0039] S300: Establish a state space equation, calculate the optimal control variable based on the state space equation, and dynamically adjust the virtual synchronous generator parameters according to the optimal control variable.

[0040] Specifically, in a high-proportion new energy power grid, the inertia support of traditional synchronous generators is weakened, resulting in a decrease in system frequency stability. Therefore, a method of VSG (virtual synchronous generator) and MPC (model predictive control) cooperation is adopted, and through dynamic optimization of virtual inertia parameters and multi-time scale resource coordination, frequency stability control and energy optimization scheduling are realized.

[0041] Further, a state space equation is established to describe the dynamic relationship between frequency, inertia, power and other variables, and the state space equation is as follows:

[0042]

[0043] wherein, represents the state vector at k+1 time, including frequency, voltage and resource output power variables; A is a state transition matrix, which is used to describe the evolution law of state variables; represents the state variable set at k time; B represents the input matrix, which is used to describe the influence of control input on state variables; is the control input at k time, including VSG key parameters inertia J and damping D, and other resource scheduling variables (energy storage power , load adjustment , etc.).

[0044] Further, based on the state space equation, the MPC calculates the control instruction that can realize the minimization of frequency deviation and power loss, so as to minimize the frequency deviation and resource loss as the target, and find the optimal control input The objective function is as follows:

[0045]

[0046] where u represents the entire optimization control sequence, and the solution of is the control input at time k, including the 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, indicating how many time steps the MPC optimizes in the future; is the frequency deviation, indicating the difference between the actual operating frequency at the current time k and the nominal frequency; is the power loss, indicating the energy loss due to factors such as energy storage device charging and discharging, line loss, etc.; represents the frequency deviation weight, used to balance the attention of the control target to frequency stability; represents the power loss weight, used to balance the attention of the control target to economy.

[0047] Further, the objective function weights and are adjusted according to the frequency deviation amplitude of the real-time running state, for example, in the case of a large disturbance, the frequency deviation is prioritized to be suppressed, and is increased; in the steady state, resource loss optimization is emphasized, and is increased.

[0048] Preferably, the MPC rolling optimization obtains the optimal control variable , which includes the VSG key parameters inertia J and damping D, to ensure optimal operation.

[0049] Preferably, the state space equation is used to quantify the dynamic evolution law of frequency, power, etc., 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 different scene requirements (such as prioritizing frequency stability in the case of a large disturbance). The weight self-adaptation (dynamically adjusting and ) can enhance control flexibility.

[0050] Further, the optimal control variable calculated by the MPC is used to dynamically adjust the VSG key parameters inertia coefficient J and damping coefficient D, realizing inertia self-adaptation and damping switching.

[0051] Further, inertia self-adaptation is used to increase J to enhance instantaneous support when the frequency suddenly changes, and to reduce J to reduce loss in the steady state.

[0052]

[0053] wherein, J is the virtual inertia coefficient, represents the basic inertia value of VSG, the default value used when there is no disturbance; represents the adjustment coefficient, which affects the sensitivity of the inertia change with the frequency deviation, The greater, the faster the inertia adjustment speed; represents the current frequency deviation, represents the set maximum frequency deviation, used for normalization processing to prevent the parameter from being too large.

[0054] When the frequency change is small, tends to 0, tanh(0)=0, J≈0 (close to the minimum inertia), which can reduce the loss and improve the economy at this time.

[0055] When the frequency changes suddenly becomes large, tanh(.) approaches 1, so that J≈ (increased inertia), which can suppress the frequency fluctuation and improve the stability at this time.

[0056] Further, the damping is switched to, according to the oscillation amplitude, the transient damping (suppressing oscillation) and the steady-state damping (energy-saving operation) are switched. The formula of damping switching is as follows:

[0057]

[0058] wherein, is the transient damping coefficient, the value is large (such as =20), which is used to quickly suppress frequency oscillation; is the steady-state damping coefficient, the value is small (such as =5), which is used to reduce the loss in steady-state operation; represents the current frequency deviation; is the oscillation amplitude threshold (such as =0.2Hz), which is set according to the system stability requirement.

[0059] Preferably, according to the frequency deviation threshold, the transient damping or the steady-state damping is selected, which realizes large damping to quickly suppress oscillation and small damping to reduce operating loss.

[0060] Preferably, the optimal control variables calculated according to the MPC are used to dynamically adjust the key parameters (virtual inertia J and damping D) of the VSG, so that the system can quickly respond when the frequency suddenly changes, and reduce energy consumption when the system is in steady state. Through inertia adaptation and damping switching, both instantaneous high-power support and high-efficiency and economical regulation under different operating conditions can be achieved. Dynamic adjustment of the VSG parameters can enable the microgrid to quickly and flexibly adjust the operating state when the frequency fluctuates, ensuring system stability, optimizing energy utilization efficiency, and improving overall response speed.

[0061] S400: Combine the dynamically adjusted virtual synchronous generator parameters with the divided virtual inertia three-level cooperative support, and perform multi-energy coordinated control according to the real-time frequency deviation.

[0062] Further, the 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 realizes synchronization with the power grid through angular velocity and electrical angle. The VSG control equation is as follows:

[0063]

[0064] wherein J is a virtual inertia coefficient used to simulate the rotational inertia of the rotor of the synchronous generator, determines the response speed of the system to frequency changes, and D is a damping coefficient representing the viscous resistance of the rotor of the system, determines the decay speed of frequency oscillation; is a 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; is an electromagnetic torque used to simulate the electromagnetic resistance torque generated inside the synchronous generator, corresponding to the power consumed by the system load. is an angular velocity change rate representing the change rate of the rotational angular velocity of the system over time; is an angular velocity corresponding to the frequency of the microgrid; represents the electrical angle position in the synchronous rotating reference frame.

[0065] Preferably, the VSG affects the frequency stability of the microgrid system by responding to power changes.

[0066] Preferably, the VSG can adapt to different frequency disturbance scenarios; when the frequency suddenly changes (such as sudden drop in new energy output), the instantaneous power of the energy storage device is released by increasing J to suppress the frequency drop rate; when the frequency oscillates, D is adaptively adjusted to accelerate the oscillation decay, avoiding stability problems such as subsynchronous resonance.

[0067] Preferably, in addition to the optimization of VSG parameters, the MPC also optimizes the cooperative scheduling of energy storage, flexible load and controllable power supply through multi-time scale coordinated control.

[0068] Further, the multi-time scale coordinated control includes triggering different levels of resources according to the frequency deviation amplitude:

[0069] When the real-time frequency deviation of the microgrid is less than or equal to a first threshold value 0.2 Hz, in order to cope with high-frequency power fluctuations and prevent the VSG from bearing 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;

[0070] When the first threshold value 0.2 Hz is less than the real-time frequency deviation of the microgrid and less than or equal to a second threshold value 0.5 Hz, the first-level inertia support and the second-level inertia support are activated at the same time, the MPC immediately activates the energy storage device to discharge / charge, and adjusts the controllable load (such as air conditioner, electric vehicle charging) to alleviate the medium-time scale power fluctuation;

[0071] When the real-time frequency deviation of the microgrid is greater than the second threshold value 0.5 Hz, the third-level standby support is activated, and when the new energy output is insufficient, the standby unit is gradually dispatched to balance the long-time power shortage and ensure the overall energy supply and demand balance of the system.

[0072] Preferably, for example, in the case of frequency drop: the output of large-scale photovoltaic power suddenly decreases, causing the system frequency to decrease; the control strategy is that the MPC predicts the decrease of new energy output, immediately increases the inertia J of the VSG; starts the energy storage device to discharge to provide instantaneous power support; appropriately adjusts the flexible load (such as reduces the air conditioner power) to reduce the load impact; gradually dispatches the controllable power supply to improve the system power support capability.

[0073] Preferably, for example, in the case of new energy fluctuation causing grid oscillation and affecting stability; the control strategy is that the MPC calculates the optimal damping coefficient D and dynamically adjusts the damping control strategy of the VSG.

[0074] Further, after the VSG provides instantaneous inertia support, the MPC optimizes the subsequent resource allocation to avoid overcharging / overdischarging of the energy storage.

[0075] Preferably, the SOC of the energy storage is associated with the releasable inertia value to establish a nonlinear mapping function.

[0076]

[0077] wherein, is the actual output virtual inertia value, is the maximum virtual inertia value that can be provided, which is determined by the physical characteristics of the energy storage device.

[0078] When the SOC of the energy storage is greater than or equal to 20%, = At this time, the battery power is sufficient, and the energy storage device participates in regulation at full capacity.

[0079] When 10% < energy storage SOC < 20%, = At this time, the battery over-discharge can be avoided, and the battery service life is prolonged.

[0080] When energy storage SOC < 10%, = 0, at this time, the battery deep discharge leading to irreversible damage can be prevented.

[0081] Further, the edge computing node is deployed in the micro-grid, is directly connected with the energy storage, the load and the power supply equipment, and a double-layer collaborative architecture of "edge fast response-central global optimization" is constructed. The edge node directly executes high-frequency control actions based on preprocessed data, resource division strategies and dynamic optimization parameters, realizes second-level frequency disturbance suppression, meanwhile, the MPC optimization coordinates diesel engine start-stop, load group control and other long-term scheduling, and the double-layer collaborative architecture is coordinated through event-triggered communication and adaptive resource mapping. The edge layer synchronizes key states such as energy storage SOC to the MPC in real time, and the central layer dynamically adjusts global target weights, which guarantees high-frequency response accuracy (delay ≤ 10 ms) and avoids resource conflicts, and finally realizes the dual goals of micro-grid frequency stability and economic operation.

[0082] Preferably, the dynamically adjusted VSG parameters are combined with the virtual inertia three-level collaborative support, multi-time scale resource collaborative scheduling is realized, and balance between second-level response and long-period scheduling is ensured. The coordinated action of energy storage, flexible load and controllable power supply is comprehensively utilized, the new energy fluctuation and low inertia problem are effectively solved, the frequency stability and energy scheduling efficiency are improved. By associating the energy storage SOC with the virtual inertia value, a nonlinear mapping function is established, the over-discharge or over-charge of the energy storage device is avoided, the battery life is prolonged, and the system reliability is improved. The edge node synchronizes key states such as energy storage SOC to the central layer in real time, the central layer dynamically adjusts global target weights, and the dual goals of frequency stability and economic operation are realized under different operation scenarios.

[0083] The application also provides a multi-energy coordination control system for a new type of micro-grid, which is used for realizing the multi-energy coordination control method for the new type of micro-grid.

[0084] The application provides a storage medium, which has a program stored thereon, and the program is executed by a processor to realize the multi-energy coordination control method for the new type of micro-grid.

[0085] The embodiment of the present application provides a processor used for running a program, wherein the program performs the method for coordinating control of multiple energies of a new micro-grid.

[0086] The embodiment of the present application provides a device including a processor, a memory, and a program stored in the memory and capable of running on the processor, and the processor performs the method for coordinating control of multiple energies of a new micro-grid when running the program. The device herein can be a server, a PC, a PAD, a mobile phone, and the like.

[0087] The present application also provides a computer program product suitable for performing the program initialized with the steps of the method for coordinating control of multiple energies of a new micro-grid when executed on a data processing device.

[0088] Those skilled in the art should understand that the embodiments of the present application can provide a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a form of a computer program product implemented on one or more computer usable storage media including computer usable program code (including but not limited to disk storage, CD-ROM, optical storage, and the like).

[0089] The present application is described with reference to flowcharts and / or block diagrams according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices generate a device that realizes the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The devices that realize the functions specified in one or more flows and / or blocks.

[0090] These computer program instructions can also be stored in a computer readable memory capable of guiding a computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including instruction devices that realize the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The devices that realize the functions specified in one or more flows and / or blocks.

[0091] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0092] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0093] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, or other memory technologies, about which the processor can execute instructions. The memory is an example of computer readable media.

[0094] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as 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 programmable read only memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to computing devices. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0095] It should also be noted that the terms "comprising," "including," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0096] ​​The above merely provides an example of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the scope of claims of the present application.

Claims

1. A multi-energy coordinated control method for novel microgrids, characterized in that, include: Based on the device's dynamic response time, the acquisition frequency is divided into high-frequency data, medium-frequency data, and low-frequency data; Using the graded acquisition frequency, microgrid operation data is collected, and the collected microgrid operation data is preprocessed. The preprocessed microgrid operation data is divided into segments, and a three-level collaborative support segmentation of virtual inertia is performed based on the segmented microgrid operation data. The three-level collaborative support segmentation of virtual inertia includes primary inertia support, secondary inertia support, and tertiary backup support. The primary inertia support resource is energy storage equipment, the secondary inertia support resource is flexible load, and the tertiary backup support resource is controllable power source. Establish state-space equations and calculate optimal control variables based on these equations, including: establishing state-space equations describing the dynamic relationship between frequency, inertia, and power variables. in, A represents the state vector at time k+1; A is the state transition matrix, used to describe the evolution of the state variables. B represents the set of state variables at time k; B represents the input matrix, used to describe the influence of control inputs on the state variables. The control inputs for k include the key parameters of the virtual synchronous generator: inertia J and damping D. Based on the state-space equations, the objective function is solved to minimize frequency deviation and resource loss, thus obtaining the optimal control variables. ; The objective function is as follows: Where u represents the entire optimized control sequence, and the solution is... The control input at time k includes the key parameters of the virtual synchronous generator, inertia J and damping D; N is the rolling optimization step size, indicating how many time steps the model predictive control will optimize in the future. Frequency deviation represents the difference between the actual operating frequency and the nominal frequency at the current time k of the system. For power loss; This represents the frequency deviation weight, used to balance the control objective's focus on frequency stability; This represents the power loss weight, used to balance the control objective's focus on economic efficiency. Based on the frequency of real-time operating status, dynamically adjust the frequency deviation weight and power loss weight in the objective function; and dynamically adjust the virtual synchronous generator parameters based on the optimal control variables. Model predictive control obtains the optimal control variables through rolling optimization, including the parameters of the virtual synchronous generator, inertia and damping. The optimal control variables calculated by model predictive control are used to dynamically adjust the virtual inertia coefficient J and damping coefficient D of the virtual synchronous generator. The formula for damping switching is as follows: in, This is the transient damping coefficient, used to quickly suppress frequency oscillations; This is the steady-state damping coefficient, used to reduce losses during steady-state operation; Indicates the current frequency deviation; The oscillation amplitude threshold is set according to the system stability requirements. Calculate the deviation between the actual operating frequency and the nominal frequency, and use this deviation as the frequency deviation of the real-time operating state; based on the frequency deviation of the real-time operating state, dynamically adjust the virtual inertia coefficient using an adaptive adjustment formula; switch between transient damping and steady-state damping according to the frequency deviation amplitude; and dynamically adjust the weight of the objective function according to the frequency deviation amplitude of the real-time operating state. The adaptive adjustment formula is as follows: Where J is the virtual inertia coefficient. This represents the basic inertia value of the virtual synchronous generator, the default value used when there is no disturbance. Indicates the adjustment factor; This indicates the current frequency deviation, that is, the frequency deviation in real-time operation. This indicates the maximum set frequency deviation; 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 based on the frequency deviation of the real-time operating status, i.e., the real-time frequency deviation.

2. The multi-energy coordinated control method for novel microgrids according to claim 1, characterized in that, The process of dividing the preprocessed microgrid operation data includes: The preprocessed microgrid operation data is divided into energy storage devices, flexible loads, and controllable power sources.

3. The multi-energy coordinated control method for novel microgrids according to claim 1, characterized in that, The process of combining dynamically adjusted virtual synchronous generator parameters with the divided virtual inertia three-level coordinated support, and performing multi-energy coordinated control based on the frequency deviation of the real-time operating status, includes: Based on the dynamically adjusted virtual inertia coefficient and damping coefficient, the rotor motion equation of the virtual synchronous generator is executed. The three-level collaborative support of the divided virtual inertia is dynamically correlated with the parameters of the virtual synchronous generator to form a multi-timescale collaborative control framework. Set the real-time operating status frequency deviation threshold (i.e., the microgrid frequency deviation threshold) and response level as follows: Level 1 Inertia Support: When the real-time frequency deviation of the microgrid is ≤ the first threshold, only the energy storage device is activated for discharging / charging. Secondary inertia support: First threshold < microgrid real-time frequency deviation ≤ second threshold, at which point energy storage devices and flexible loads are activated simultaneously; Level 3 backup support: When the real-time frequency deviation of the microgrid exceeds the second threshold, the controllable power supply is activated.

4. The multi-energy coordinated control method for novel microgrids according to claim 1, characterized in that, The method of combining the dynamically adjusted virtual synchronous generator parameters with the divided virtual inertia three-level coordinated support, and performing multi-energy coordinated control based on the frequency deviation of the real-time operating status, also includes: By associating the remaining energy storage capacity with the releasable inertia value, a nonlinear mapping function is established to dynamically limit the virtual inertia. Deploy edge computing nodes in a microgrid and use the edge computing nodes to perform high-frequency control; By combining the edge computing nodes with model predictive control, multi-energy coordinated control is performed through multi-layer resource collaboration and parameter adaptation.

5. The multi-energy coordinated control method for novel microgrids according to claim 1, characterized in that, The preprocessing includes missing value handling, outlier filtering, format normalization, and high-frequency noise smoothing.

6. A multi-energy coordinated control system for novel microgrids, 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 the multi-energy coordinated control method for novel microgrids according to any one of claims 1-5.

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