Multi-microgrid power distribution network collaborative scheduling method and system based on digital twinning, storage medium and equipment

Abstract

The application discloses a multi-microgrid power distribution network cooperative scheduling method and system based on digital twinning, a storage medium and equipment, and comprises the following steps: S1: a physical layer collects operation state quantities at the current time through a measurement device, and a twin layer executes a state synchronization algorithm according to the collected data; S2: the twin layer receives meteorological environment data; S3: an information control layer constructs a cooperative scheduling optimization model based on a safety accommodation boundary dynamically evaluated in step S2; and S4: before directly issuing a control instruction, out-of-limit evaluation and testing are performed, and the optimal scheduling instruction is issued to the physical layer for execution. Through the three-layer structure of physical to twin to control, the application realizes virtual verification and real-time feedback correction of the scheduling instruction before issuance. Based on a mathematical mapping function of error minimization, the twin layer ensures fast and accurate synchronization of the physical layer topology and operation state. The digital twinning prediction engine realizes real-time reconstruction of a new energy accommodation boundary, and solves the problem of failure of a traditional static boundary under extreme working conditions.

Description

Technical Field

[0001] This invention relates to the field of multi-microgrid distribution network collaborative scheduling technology, specifically to a multi-microgrid distribution network collaborative scheduling method, system, storage medium, and device based on digital twins. Background Technology

[0002] With the global energy transition towards low-carbon energy, the penetration rate of distributed energy in distribution networks is rapidly increasing, prompting the evolution of traditional passive distribution networks into active distribution networks. To improve the absorption capacity of distributed energy and the reliability of local power supply, multi-microgrid systems have become an important organizational form of modern power systems. However, the integration of large-scale microgrids significantly increases the operational complexity and randomness of the distribution network. The close physical-information coupling between the active distribution network and its underlying microgrids poses a severe challenge to the coordinated scheduling of the system.

[0003] Existing collaborative scheduling methods mostly employ centralized or distributed optimization frameworks. While they have achieved some success in steady-state energy management, they still have significant limitations in highly dynamic environments. Traditional scheduling models are mostly based on pre-day or pre-hour forecast data for offline optimization, lacking the ability to capture real-time changes in the physical system and making it difficult to effectively cope with the instantaneous fluctuations of renewable energy. Secondly, as distribution networks transform into cyber-physical systems, communication delays and model mismatches often lead to deviations in the physical execution of scheduling commands, even threatening voltage safety.

[0004] In recent years, digital twin technology has provided new ideas for online optimization of power systems due to its high-fidelity virtual mapping and real-time synchronization capabilities. However, the current application of digital twins in the power field is mostly limited to equipment condition monitoring or fault diagnosis. In the joint coordinated dispatch of active distribution networks and microgrid groups involving multi-entity interactions, there is still a lack of effective closed-loop feedback mechanisms and dynamic boundary assessment methods. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, storage medium, and device for coordinated scheduling of multi-microgrid distribution networks based on digital twins, so as to solve the problems in the prior art mentioned in the background.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-microgrid distribution network collaborative scheduling method based on digital twins, comprising:

[0007] S1: The physical layer collects the current operating state data through measurement devices. The twin layer executes a state synchronization algorithm based on the collected data to achieve a mathematical mapping of the physical system by minimizing the error between the physical observation vector and the virtual state estimation vector.

[0008] S2: The twin layer receives meteorological and environmental data, uses a prediction engine to predict the future state trajectory of the system, and dynamically assesses the safe absorption boundary of renewable energy in the multi-microgrid under the condition of considering physical safety limits.

[0009] S3: Based on the safety absorption boundary dynamically evaluated in step S2, the information control layer constructs a collaborative scheduling optimization model with the goal of minimizing the sum of the operating costs of the active distribution network and all microgrids, and solves to generate a preliminary scheduling strategy.

[0010] S4: Before directly issuing control commands, the information control layer places the preliminary scheduling strategy generated in step S3 in the twin layer for limit evaluation and testing. After verification, the optimal scheduling command is issued to the physical layer for execution.

[0011] Preferably, the mathematical mapping objective function of the state synchronization algorithm in S1 is to solve the problem of minimizing the error; and / or

[0012] The dynamic assessment method for the safe absorption boundary in S2 is as follows:

[0013] Given a weather forecast scenario, for future periods Dynamic safe absorption boundary of internal photovoltaic power generation Perform calculations; and / or

[0014] The objective function of the cooperative scheduling optimization model in S3 is to minimize the joint operation cost.

[0015] The preferred formula for minimizing the error is as follows:

[0016] ;

[0017] In the formula, for State synchronization error at any given moment; For physical systems in The observation vector at time t, which includes node voltage, branch current and injected power; For virtual twin state vectors; A measurement function that connects state variables and observed quantities; The state transition function is determined by the laws of power flow physics; This is the control input vector from the previous time step; To represent the measurement confidence level; This is the weight matrix for model confidence.

[0018] Preferred, future time period Dynamic safe absorption boundary of internal photovoltaic power generation The calculation formula is as follows:

[0019] ;

[0020] In the formula, For the future Solar power output at all times; For digital twin models in a given weather scenario Next prediction node The voltage; For digital twin models in a given weather scenario Predicting branch The current; The physical safety lower limit of node voltage and This represents the physical safety upper limit for node voltage; This is the physical safety limit for branch current.

[0021] The preferred formula for minimizing joint operating costs is as follows:

[0022] ;

[0023] In the formula, To minimize joint operating costs; The operating cost of an active distribution network; For the first The operating cost of an interconnected microgrid; This represents the total number of microgrids.

[0024] Preferably, the operating cost of an active distribution network includes the cost of purchasing electricity from the upstream main grid and the active power loss cost of the distribution network lines, as shown in the following formula:

[0025] ;

[0026] In the formula, The scheduling period; The scheduling time step; for Time-of-use electricity pricing at any given moment; This refers to the active power input from the main grid; For distribution network branch collection; branch road The resistance; For the branch road The current; This is the penalty factor for power loss.

[0027] Preferred, the first The operating costs of a microgrid include:

[0028] The generation cost of dispatchable distributed power sources and the degradation cost of energy storage systems are given by the following formulas:

[0029] ;

[0030] In the formula, For diesel generators or micro gas turbines, it is a quadratic cost function; For schedulable distributed power sources in Contributing effort at all times; This refers to the edge degradation cost coefficient of the energy storage system. and For energy storage systems The charging power and discharging power at any given time.

[0031] Preferably, the collaborative scheduling optimization model in the information control layer considers the power flow and security constraints of the active distribution network; and / or

[0032] The collaborative scheduling optimization model also considers the operational dynamics and logical constraints of the energy storage systems within each microgrid.

[0033] Preferably, the power flow of the distribution network is modeled based on the relaxed DistFlow branch equations of second-order cone programming, and satisfies the following constraints:

[0034] The formula relating voltage magnitude to the second-order cone relaxation constraint is as follows:

[0035] ;

[0036] ;

[0037] Safety Limit Constraints: Subject to the Dynamic Boundary of Digital Twins The guidance should satisfy the following formula:

[0038] ;

[0039] In the formula, branch road The meritorious trend; branch road The unproductive current; branch road The resistance; branch road The reactance; For nodes The square of the voltage amplitude; For nodes The square of the voltage amplitude; branch road The square of the current amplitude; The safe upper limit for the square of the node voltage; This is the lower safety limit for the square of the node voltage; The safe upper limit is the square of the branch current.

[0040] Preferably, the collaborative scheduling optimization model considers the operational dynamics and logical constraints of the energy storage systems within each microgrid, specifically including:

[0041] Dynamic equations of charged state:

[0042] ;

[0043] Charge / discharge power and logic anti-collision constraints:

[0044] ;

[0045] ;

[0046] ;

[0047] In the formula, for The state of charge of the energy storage system at all times; This refers to the rated capacity of the energy storage system. For charging efficiency; For discharge efficiency; This represents the maximum charging power limit. This represents the maximum discharge power extreme value; and It is a binary variable.

[0048] A digital twin-based multi-microgrid distribution network collaborative scheduling system is used to execute the aforementioned digital twin-based multi-microgrid distribution network collaborative scheduling method, including:

[0049] The physical layer is used for the physical assets of the active distribution network and multiple microgrids;

[0050] A twin layer, which carries a dynamic mathematical model and receives physical layer data updates in real time;

[0051] An information control layer is used to perform optimization and decision-making.

[0052] An electronic device, the device comprising:

[0053] At least one processor; and

[0054] A memory communicatively connected to the at least one processor; wherein,

[0055] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to execute the digital twin-based multi-microgrid distribution network collaborative scheduling method.

[0056] A computer-readable storage medium storing computer instructions for causing a processor to execute the aforementioned multi-microgrid distribution network collaborative scheduling method based on digital twins.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] Through a three-layer structure—physical layer, digital twin, and control layer—virtual verification and real-time feedback correction of scheduling commands before issuance are achieved. Furthermore, a mathematical mapping function based on error minimization ensures rapid and accurate synchronization of the digital twin layer with the physical layer's topology and operational status. Simultaneously, a digital twin prediction engine enables real-time reconstruction of the renewable energy consumption boundary, effectively solving the problem of traditional static boundary failure under extreme conditions. A collaborative scheduling model based on real-time feedback from the digital twin is constructed, and global optimal scheduling of distribution network and multi-microgrid resources is achieved through mixed-integer second-order cone programming. Attached Figure Description

[0059] The accompanying drawings, as part of this invention, are provided to further illustrate the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention, but do not constitute an undue limitation thereof. Clearly, the drawings described below are merely some embodiments, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0060] Figure 1 This is a flowchart of the multi-microgrid distribution network collaborative scheduling method based on digital twins according to the present invention;

[0061] Figure 2 The IEEE 123 node distribution network topology diagram used for testing the multi-microgrid distribution network collaborative scheduling method based on digital twins in this invention;

[0062] Figure 3 This is a state synchronization effect diagram of the multi-microgrid distribution network collaborative scheduling method based on digital twins of the present invention;

[0063] Figure 4 This is a dynamic boundary diagram illustrating the multi-microgrid distribution network collaborative scheduling method based on digital twins according to the present invention.

[0064] Figure 5 This is a diagram illustrating the collaborative scheduling effect of the multi-microgrid distribution network collaborative scheduling method based on digital twins according to the present invention.

[0065] Figure 6 This is a spatiotemporal distribution diagram of node voltage in the multi-microgrid distribution network collaborative scheduling method based on digital twins of the present invention;

[0066] Figure 7This is a structural diagram of the multi-microgrid distribution network collaborative dispatching system based on digital twins according to the present invention;

[0067] Figure 8 This is a schematic diagram of the electronic equipment used in the multi-microgrid distribution network collaborative scheduling method based on digital twins according to the present invention. Detailed Implementation

[0068] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention. It should be understood that the various steps described in the method embodiments of the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0069] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0070] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, any variations of the terms "comprising" and "having," etc., are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0071] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0072] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0073] Example 1

[0074] like Figure 1 As shown, the multi-microgrid distribution network collaborative scheduling method based on digital twins includes:

[0075] S1: The physical layer collects the current operating state quantities through measurement devices. The twin layer executes a state synchronization algorithm based on the collected data, and realizes the mathematical mapping of the physical system by minimizing the error between the physical observation vector and the virtual state estimation vector.

[0076] As a further explanation: the mathematical objective function of the state synchronization algorithm is to solve the problem of minimizing the error, and the formula for minimizing the error is as follows:

[0077] ;

[0078] In the formula, for State synchronization error at any given moment; For physical systems in The observation vector at time t, which includes node voltage, branch current and injected power; For virtual twin state vectors; A measurement function that connects state variables and observed quantities; The state transition function is determined by the laws of power flow physics; This is the control input vector from the previous time step; To represent the measurement confidence level; This is the weight matrix for model confidence.

[0079] S2: The twin layer receives meteorological and environmental data, uses a prediction engine to predict the future state trajectory of the system, and dynamically assesses the safe absorption boundary of renewable energy in the multi-microgrid under the condition of considering physical safety limits.

[0080] As further explanation: The dynamic assessment method for the safe absorption boundary is as follows:

[0081] Given a weather forecast scenario, for future periods Dynamic safe absorption boundary of internal photovoltaic power generation The calculation is performed using the following formula:

[0082] ;

[0083] In the formula, For the future Solar power output at all times; For digital twin models in a given weather scenario Next prediction node The voltage; For digital twin models in a given weather scenario Predicting branch The current; The physical safety lower limit of node voltage and This represents the physical safety upper limit for node voltage; This is the physical safety limit for branch current.

[0084] S3: Based on the safety absorption boundary dynamically assessed in step S2, the information control layer constructs a collaborative scheduling optimization model with the goal of minimizing the sum of the operating costs of the active distribution network and all microgrids, and solves to generate a preliminary scheduling strategy.

[0085] As further explanation: The objective function of the cooperative scheduling optimization model in S3 is to minimize the joint operation cost. The formula for minimizing the joint operation cost is as follows:

[0086] ;

[0087] In the formula, To minimize joint operating costs; The operating cost of an active distribution network; For the first The operating cost of an interconnected microgrid; This represents the total number of microgrids.

[0088] In this embodiment: the operating cost of the active distribution network includes the cost of purchasing electricity from the upstream main grid and the active power loss cost of the distribution network lines, as shown in the following formula:

[0089] ;

[0090] In the formula, The scheduling period; The scheduling time step; for Time-of-use electricity pricing at any given moment; This refers to the active power input from the main grid; For distribution network branch collection; branch road The resistance; For the branch road The current; This is the penalty factor for power loss.

[0091] No. The operating costs of a microgrid include:

[0092] The generation cost of dispatchable distributed power sources and the degradation cost of energy storage systems are given by the following formulas:

[0093] ;

[0094] In the formula, For diesel generators or micro gas turbines, it is a quadratic cost function; For schedulable distributed power sources in Contributing effort at all times; This refers to the edge degradation cost coefficient of the energy storage system. and For energy storage systems The charging power and discharging power at any given time.

[0095] S4: Before directly issuing control commands, the information control layer places the preliminary scheduling strategy generated in step S3 in the twin layer for limit evaluation and testing. After verification, the optimal scheduling command is issued to the physical layer for execution.

[0096] As a further explanation: the collaborative scheduling optimization model in the information control layer takes into account the power flow and security constraints of the active distribution network.

[0097] In this embodiment, the power flow of the distribution network is modeled based on the relaxed DistFlow branch equations of second-order cone programming (SOCP) and satisfies the following constraints:

[0098] The formula relating voltage magnitude to the second-order cone relaxation constraint is as follows:

[0099] ;

[0100] ;

[0101] Safety Limit Constraints: Subject to the Dynamic Boundary of Digital Twins The guidance should satisfy the following formula:

[0102] ;

[0103] In the formula, branch road The meritorious trend; branch road The unproductive current; branch road The resistance; branch road The reactance; For nodes The square of the voltage amplitude; For nodes The square of the voltage amplitude; branch road The square of the current amplitude; The safe upper limit for the square of the node voltage; This is the lower safety limit for the square of the node voltage; The safe upper limit is the square of the branch current.

[0104] As a further explanation: the collaborative scheduling optimization model also considers the operational dynamics and logical constraints of the energy storage systems (ESS) within each microgrid.

[0105] In this embodiment, the collaborative scheduling optimization model considers the operational dynamics and logical constraints of the energy storage systems within each microgrid, specifically including:

[0106] Dynamic equations of state of charge (SOC):

[0107] ;

[0108] Charge / discharge power and logic anti-collision constraints:

[0109] ;

[0110] ;

[0111] ;

[0112] In the formula, for The state of charge of the energy storage system at all times; This refers to the rated capacity of the energy storage system. For charging efficiency; For discharge efficiency; This represents the maximum charging power limit. This represents the maximum discharge power extreme value; and It is a binary variable.

[0113] Based on the technical solutions of the above embodiments, the present invention provides specific implementation methods, such as... Figure 1The topology shown is the result of simulation testing in an improved unbalanced 123-node distribution network system. The reference voltage of the test system was set to 4.16 kV. The physical layer structure integrates four independent microgrids (labeled MMG 1 to MMG 4), each connected to different phase nodes (phases A, B, and C). Each microgrid is equipped with a photovoltaic (PV) array, a wind turbine (WT) generator, and a dispatchable diesel generator, with the installed capacity of the distributed renewable energy sources ranging from 200 kW to 500 kW. A centralized energy storage system (ESS) with a rated capacity of 2 MWh and a maximum charge / discharge power of 500 kW is deployed near node 66 to provide system-level buffering capacity. The system's dispatch cycle is set to 24 hours, with a traditional steady-state baseline time resolution of 1 hour and a digital twin-based dynamic correction time resolution of 15 minutes. The purchase cost of electricity from the upstream main grid is calculated using a time-of-use (TOU) pricing mechanism. In addition, the physical power grid is monitored at high density through a heterogeneous sensor network, including SCADA systems, to support the data-driven needs of the twin layer.

[0114] To verify the advantages of this embodiment under extreme conditions, a severe weather anomaly scenario was introduced: a sudden cold wave and thick cloud cover occurred between t=12 and t=16. This weather event caused a surge in local heating load and a sharp 65% drop in photovoltaic power generation in MMG1 and MMG2.

[0115] Compared with the prior art, the present invention has the following significant effects: The actual operation effect of the method in this embodiment was verified by experiments from the following four dimensions:

[0116] Real-time status synchronization accuracy verification: such as Figure 3 As shown, during sudden topology disturbances, the twin layer achieves rapid convergence by iteratively solving the mapping objective function. Test results show that the maximum estimation error of node voltage and branch current decays to less than 10⁻⁴ pu within 200 milliseconds. This proves that the virtual model in this embodiment can maintain a high-fidelity, real-time synchronous mapping of the physical distribution network even under transient conditions.

[0117] Dynamic safety boundary assessment verification: During the abnormal weather period from t=12 to t=16, the physical voltage margin drastically decreased due to system overload. For example... Figure 4 As shown, the digital twin prediction engine in this embodiment accurately predicted the bottleneck state and dynamically tightened the upper limit boundary of the allowable photovoltaic injection, thereby effectively preventing potential overvoltage or undervoltage cascading effects. In contrast, traditional deterministic boundary methods, unable to perceive real-time grid pressure, dangerously overestimate the system's absorption capacity.

[0118] Coordinated power dispatch and energy storage response verification: In the event of an impending extreme weather event, the information control layer in this embodiment performs preventative dispatch adjustments. For example... Figure 5 As shown, to avoid over-reliance on the upstream main grid and thus exacerbate line losses and voltage drops, the digital twin system issues commands to control the energy storage system (ESS) to discharge significantly, rapidly reducing its state of charge (SOC) from 0.8 to 0.3 to support local loads. This autonomous and coordinated scheduling effectively achieves physical isolation and decoupling between internal disturbances in the microgrid and the main distribution network.

[0119] Spatiotemporal voltage elasticity verification: such as Figure 6 As shown, a 3D spatiotemporal comparative analysis of the voltage distribution across the entire distribution network (nodes 1 to 123) reveals that traditional dispatching methods cause severe voltage dips when encountering load surges and photovoltaic downsampling, with the voltage at the feeder end exceeding the safe lower limit of 0.95 pu. Conversely, the auxiliary dispatching method employed in this embodiment, through closed-loop feedback correction and dynamic resource allocation, effectively smooths the overall voltage distribution of the system. The lowest node voltage is safely maintained at 0.965 pu, significantly improving the spatial voltage resilience and robustness of the complex 123-node topology system against unexpected disturbances.

[0120] Example 2

[0121] like Figure 7 As shown, a multi-microgrid distribution network collaborative scheduling system based on digital twins is used to execute the multi-microgrid distribution network collaborative scheduling method based on digital twins in Embodiment 1, including:

[0122] The physical layer is used for the physical assets of active distribution networks and multiple microgrids.

[0123] The twin layer is used to carry the dynamic mathematical model and receive data updates from the physical layer in real time.

[0124] An information control layer is used to perform optimization and decision-making.

[0125] Example 3

[0126] Figure 8A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0127] like Figure 8 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0128] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0129] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the multi-microgrid distribution network collaborative scheduling method based on digital twins.

[0130] In some embodiments, the digital twin-based multi-microgrid distribution network coordinated dispatch method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the digital twin-based multi-microgrid distribution network coordinated dispatch method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the digital twin-based multi-microgrid distribution network coordinated dispatch method by any other suitable means (e.g., by means of firmware).

[0131] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0132] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0133] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0134] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0135] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0136] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0137] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A multi-microgrid distribution network collaborative scheduling method based on digital twins, characterized in that, include: S1: The physical layer collects the current operating state data through measurement devices. The twin layer executes a state synchronization algorithm based on the collected data to achieve a mathematical mapping of the physical system by minimizing the error between the physical observation vector and the virtual state estimation vector. S2: The twin layer receives meteorological and environmental data, uses a prediction engine to predict the future state trajectory of the system, and dynamically assesses the safe absorption boundary of renewable energy in the multi-microgrid under the condition of considering physical safety limits. S3: Based on the safety absorption boundary dynamically evaluated in step S2, the information control layer constructs a collaborative scheduling optimization model with the goal of minimizing the sum of the operating costs of the active distribution network and all microgrids, and solves to generate a preliminary scheduling strategy. S4: Before directly issuing control commands, the information control layer places the preliminary scheduling strategy generated in step S3 in the twin layer for limit evaluation and testing. After verification, the optimal scheduling command is issued to the physical layer for execution.

2. The multi-microgrid distribution network collaborative scheduling method based on digital twins according to claim 1, characterized in that: The mathematical mapping objective function of the state synchronization algorithm in S1 is to solve the problem of minimizing the error; and / or The dynamic assessment method for the safe absorption boundary in S2 is as follows: Given a weather forecast scenario, for future periods Dynamic safe absorption boundary of internal photovoltaic power generation Perform calculations; and / or The objective function of the cooperative scheduling optimization model in S3 is to minimize the joint operation cost.

3. The multi-microgrid distribution network collaborative scheduling method based on digital twins according to claim 2, characterized in that: The formula for minimizing the error is as follows: ； In the formula, for State synchronization error at any given moment; For physical systems in The observation vector at time t, which includes node voltage, branch current and injected power; For virtual twin state vectors; A measurement function that connects state variables and observed quantities; The state transition function is determined by the laws of tidal current physics; This is the control input vector from the previous time step; To represent the measurement confidence level; This is the weight matrix for model confidence.

4. The multi-microgrid distribution network collaborative scheduling method based on digital twins according to claim 2, characterized in that: Future period Dynamic safe absorption boundary of internal photovoltaic power generation The calculation formula is as follows: ； In the formula, For the future Solar power output at all times; For digital twin models in a given weather scenario Next prediction node The voltage; For digital twin models in a given weather scenario Predicting branch The current; The physical safety lower limit of node voltage and This represents the physical safety limit for node voltage; This is the physical safety upper limit for branch current.

5. The multi-microgrid distribution network collaborative scheduling method based on digital twins according to claim 2, characterized in that: The formula for minimizing the joint operation cost is as follows: ； In the formula, To minimize joint operating costs; The operating cost of an active distribution network; For the first The operating costs of an interconnected microgrid; This represents the total number of microgrids.

6. The multi-microgrid distribution network collaborative scheduling method based on digital twins according to claim 5, characterized in that: The operating cost of an active distribution network includes the cost of purchasing electricity from the upstream main grid and the active power loss cost of the distribution network lines, as shown in the following formula: ； In the formula, The scheduling period; The scheduling time step; for Time-of-use electricity pricing at any given moment; This refers to the active power input from the main grid; For distribution network branch collection; branch road The resistance; For the branch road The current; This is the penalty factor for power loss.

7. The multi-microgrid distribution network collaborative scheduling method based on digital twins according to claim 5, characterized in that: No. The operating costs of a microgrid include: The generation cost of dispatchable distributed power sources and the degradation cost of energy storage systems are given by the following formulas: ； In the formula, For diesel generators or micro gas turbines, it is a quadratic cost function; For schedulable distributed power sources in Contributing effort at all times; This refers to the edge degradation cost coefficient of the energy storage system. and For energy storage systems The charging power and discharging power at any given time.

8. The multi-microgrid distribution network collaborative scheduling method based on digital twins according to any one of claims 1-7, characterized in that: The collaborative scheduling optimization model in the information control layer considers the power flow and security constraints of the active distribution network; and / or The collaborative scheduling optimization model also considers the operational dynamics and logical constraints of the energy storage systems within each microgrid.

9. The multi-microgrid distribution network collaborative scheduling method based on digital twins according to claim 8, characterized in that: The power flow of the distribution network is modeled based on the relaxed DistFlow branch equations of second-order cone programming, and satisfies the following constraints: The formula relating voltage magnitude to the second-order cone relaxation constraint is as follows: ； ； Safety Limit Constraints: Subject to the Dynamic Boundary of Digital Twins The guidance should satisfy the following formula: ； In the formula, branch road The meritorious trend; branch road The unproductive current; branch road The resistance; branch road The reactance; For nodes The square of the voltage amplitude; For nodes The square of the voltage amplitude; branch road The square of the current amplitude; The safe upper limit for the square of the node voltage; This is the lower safety limit for the square of the node voltage; The safe upper limit is the square of the branch current.

10. The multi-microgrid distribution network collaborative scheduling method based on digital twins according to claim 8, characterized in that: The collaborative scheduling optimization model considers the operational dynamics and logical constraints of the energy storage systems within each microgrid, specifically including: Dynamic equations of charged state: ； Charge / discharge power and logic anti-collision constraints: ； ； ； In the formula, for The state of charge of the energy storage system at all times; This refers to the rated capacity of the energy storage system. For charging efficiency; For discharge efficiency; This represents the maximum charging power limit. This represents the maximum discharge power extreme value; and It is a binary variable.

11. A multi-microgrid distribution network collaborative dispatching system based on digital twins, characterized in that, The method for implementing the multi-microgrid distribution network collaborative scheduling method based on digital twins as described in any one of claims 1-10 includes: The physical layer is used for the physical assets of the active distribution network and multiple microgrids; A twin layer, which carries a dynamic mathematical model and receives physical layer data updates in real time; An information control layer is used to perform optimization and decision-making.

12. An electronic device, characterized in that, The device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the digital twin-based multi-microgrid distribution network collaborative scheduling method as described in any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the multi-microgrid distribution network collaborative scheduling method based on digital twins as described in any one of claims 1-10.

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