Micro-grid group distributed energy management method, system and equipment based on event driving and medium

Abstract

The invention belongs to the technical field of intelligent power grid control, and discloses a micro-grid group distributed energy management method, system and device based on event driving, and a medium, so as to solve the problem that the micro-grid group distributed energy management method cannot give consideration to real-time performance and economical efficiency. The method comprises the following steps: predefining a driving event; establishing a micro-grid lower-layer optimization model by taking the minimum total operation cost of a single micro-grid as an optimization target; establishing a micro-grid group upper-layer optimization model by taking the minimum total cost of the target micro-grid group as an optimization target; and monitoring the running state of the micro-grid group in real time through the coroutine monitoring thread, triggering the micro-grid lower-layer optimization model and the micro-grid group upper-layer optimization model to execute isolation response when a driving event is detected, and outputting an optimal energy scheduling scheme of the micro-grid group. Through the event-driven layered optimization architecture, the overall operation cost of the microgrid group is reduced, the energy utilization efficiency is improved, and the adaptability and response speed of the system to different operation conditions are enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart grid control, and particularly relates to an event-driven distributed energy management method, system, device and medium for a microgrid group. Background Art

[0002] With the continuous development of microgrid technology towards the direction of high-proportion renewable energy access, a single microgrid is limited by the load characteristics and the spatio-temporal mismatch problem of new energy generation, and it is difficult to achieve complete local consumption of renewable energy. This makes the energy complementarity and coordinated scheduling among microgrid groups a key problem that needs to be urgently solved. To address this issue, realizing cross-regional energy sharing by constructing interconnected microgrid groups can effectively improve the system operation efficiency and reduce the standby capacity configuration cost. However, traditional centralized control methods have two major bottlenecks: on the one hand, as the system scale expands, the computational complexity increases exponentially, making it difficult to meet the real-time requirements; on the other hand, the fixed-period communication mechanism leads to low network bandwidth utilization, less than 40%. In summary, the existing distributed energy management methods for microgrid groups cannot balance real-time performance and economy, resulting in low energy utilization efficiency and poor stability of microgrids. Summary of the Invention

[0003] Based on the above-mentioned disadvantages and deficiencies existing in the prior art, one of the objectives of the present invention is to at least solve one or more of the above problems existing in the prior art. In other words, one of the objectives of the present invention is to provide an event-driven distributed energy management method, system, device and medium for a microgrid group that meet one or more of the foregoing requirements, so as to achieve the purpose of reducing the overall operation cost of the microgrid group, improving the energy utilization efficiency, and enhancing the adaptability and response speed of the system to different operating conditions.

[0004] To achieve the above-mentioned invention objectives, the present invention adopts the following technical solutions: In the first aspect, the present invention provides an event-driven distributed energy management method for a microgrid group, including the steps of: S1. Predetermine driving events, where the driving events include power over-limit events, energy storage capacity events, and electricity price adjustment events; S2. Establish a lower-layer optimization model for a microgrid with the minimum total operating cost of a single microgrid as the optimization objective; S3. Establish an upper-layer optimization model for a microgrid group with the minimum total cost of the target microgrid group as the optimization objective; S4. Real-time monitor the operating status of the microgrid group through a coroutine monitoring thread, and when the driving event is detected, trigger the lower-layer optimization model of the microgrid and the upper-layer optimization model of the microgrid group to execute isolation response: If the driving event is a local event, trigger the calculation of the lower-layer optimization model of the affected microgrid and output the optimal energy scheduling plan for the microgrid group; If the driving event is a global event, trigger the calculation of the lower-level microgrid optimization model of the affected microgrid, and notify the upper-level optimization model of the microgrid group to start collaborative optimization through the event bus, and output the optimal energy scheduling scheme of the microgrid group.

[0005] As a preferred solution, when the driving event is a global event, the output of the optimal energy scheduling scheme of the microgrid group includes: Perform Lagrangian transformation on the lower-level microgrid optimization model triggered for optimization, solve its KKT conditions and complementary slack conditions, and perform iterative calculation in combination with the upper-level optimization model of the microgrid group. Terminate the iteration when the change amount of the optimization variable is less than the preset error accuracy, and output the optimal energy scheduling scheme of the microgrid group.

[0006] As a preferred solution, the total operating cost of a single microgrid includes the cost of traditional generators, the cost of new energy generation, the operating cost of the energy storage system, and the operating cost of microgrid control; the lower-level microgrid optimization model satisfies power constraints, traditional generator ramp constraints, and energy storage capacity constraints.

[0007] As a preferred solution, the expression of the lower-level microgrid optimization model is: , where is the total operating cost of the microgrid , , , and are the operating costs of the traditional generator, new energy, energy storage system, and control system of the microgrid at time respectively; The constraints of the lower-level microgrid optimization model include: , , , , , , , , , , where is the power of the traditional generator of the microgrid at time respectively. is the upper power limit of the traditional generator of the microgrid at moment; is the power of the traditional generator of the microgrid at moment; is the upper limit of the ramping power of the traditional generator of the microgrid; is the power of the new energy of the microgrid at moment; is the upper power limit of the new energy of the microgrid at moment; is the purchased power of the microgrid at moment; is the upper limit of the purchased power of the microgrid at moment; is the sold power of the microgrid at moment; is the upper limit of the sold power of the microgrid at moment; is the capacity of the energy storage system of the microgrid at moment; and are respectively the upper and lower limits of the capacity of the energy storage system of the microgrid at moment; is the capacity of the energy storage system of the microgrid at moment; is the time interval between moment and is the purchased power of the control system of the microgrid at moment; is the sold power of the control system of the microgrid at moment; is the total power of the microgrid at moment.

[0008] As a preferred solution, the upper-layer optimization model of the microgrid group satisfies the internal energy trading price constraint, power balance constraint, and cost balance constraint; the expression of the upper-layer optimization model of the microgrid group is: , In the formula, is the total number of microgrids in the microgrid cluster, is the serial number of the microgrid; The constraints of the upper-layer optimization model of the microgrid cluster include: , , , , In the formula, is the price at which the microgrid cluster buys electricity from the main grid at time , is the price at which the microgrid cluster sells electricity to the main grid at time , is the price at which electricity is bought within the microgrid cluster at time , is the price at which electricity is sold within the microgrid cluster at time , is the electricity bought by the main grid at time , is the electricity sold by the main grid at time , is the electricity bought within the microgrid cluster at time , is the electricity sold within the microgrid cluster at time .

[0009] As a preferred solution, the KKT conditions of the lower-layer optimization model of the microgrid include: , , , , , , , In the formula, , , , , , , , , , , , and are dual variables.

[0010] As a preferred solution, the complementary slackness conditions of the lower-layer optimization model of the microgrid include: , , , , , , , , , , , , , The above formula represents .

[0011] In a second aspect, the present invention provides an event-driven distributed energy management system for a microgrid group, which is used to implement the distributed energy management method for a microgrid group as described in the first aspect; The system includes an event-driven module and a two-layer model building module; The event-driven module includes an event definition unit, a state monitoring unit, and an isolation response unit; The event definition unit is used to pre-define driving events, and the driving events include power limit events, energy storage capacity events, and electricity price adjustment events; The two-layer model building module includes a lower-layer optimization model building unit for the microgrid and an upper-layer optimization model building unit for the microgrid group; The lower-layer optimization model building unit for the microgrid is used to establish a lower-layer optimization model for the microgrid with the minimum total operating cost of a single microgrid as the optimization goal. The total operating cost of a single microgrid includes the cost of traditional generators, the cost of new energy generation, the operating cost of the energy storage system, and the operating cost of microgrid control. The lower-layer optimization model of the microgrid satisfies power constraints, traditional generator ramp constraints, and energy storage capacity constraints; The upper-layer optimization model building unit for the microgrid group is used to establish an upper-layer optimization model for the microgrid group with the minimum total cost of the target microgrid group as the optimization goal. The upper-layer optimization model of the microgrid group satisfies internal energy trading price constraints, power balance constraints, and cost balance constraints; The state monitoring unit is used to monitor the operating state of the microgrid group in real time through a coroutine monitoring thread; The isolation response unit is used to trigger the execution of the isolation response by the lower-layer optimization model of the microgrid and the upper-layer optimization model of the microgrid group when the status monitoring unit monitors the driving event.

[0012] As a preferred solution, the execution isolation response unit includes a local response subunit and a global response subunit; the local response subunit is used to trigger the calculation of the lower-layer optimization model of the affected microgrid when a local event is triggered, and output the optimal energy scheduling plan of the microgrid group; the global response subunit is used to trigger the calculation of the lower-layer optimization model of the affected microgrid when a global event is triggered, and notify the upper-layer optimization model of the microgrid group to start collaborative optimization through the event bus, and output the optimal energy scheduling plan of the microgrid group.

[0013] As a preferred solution, the execution isolation response unit further includes an optimal energy calculation subunit; the optimal energy calculation subunit is used to perform Lagrangian transformation on the lower-layer optimization model of the triggered optimization when a global event is triggered, solve its KKT conditions and complementary slack conditions, and perform iterative calculation in combination with the upper-layer optimization model of the microgrid group. When the change amount of the optimization variable is less than the preset error accuracy, the iteration is terminated, and the optimal energy scheduling plan of the microgrid group is output.

[0014] In a third aspect, the present invention provides an electronic device, where the computer device includes a memory, a processor, and a computer program, and when the computer program is executed by the processor, it implements the distributed energy management method for a microgrid group as described in the first aspect.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by the processor, it implements the distributed energy management method for a microgrid group as described in the first aspect.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention realizes the double improvement of energy consumption and system stability, the minimization of the collaborative operation cost of multiple microgrids, the rapid response to sudden anomalies and the enhancement of resilience, the economic optimization under dynamic electricity prices, and the adaptive support for the new power system through an event-driven hierarchical optimization architecture, providing a complete technical solution for the efficient, safe, and economic operation of the microgrid group.

[0017] Further or more detailed beneficial effects will be described in combination with specific embodiments in the specific implementation manner. Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] Figure 1 It is a schematic flowchart of the distributed energy management method for the microgrid group in the first embodiment of the present invention.

[0020] Figure 2 It is a schematic structural diagram of the distributed energy management system for the microgrid group in the second embodiment of the present invention.

[0021] Figure 3 It is a structural diagram of the electronic device provided in the third embodiment of the present invention.

[0022] Figure 4 It is a schematic diagram of the basic operating parameters of the system in the simulation experiment in the fifth embodiment of the present invention.

[0023] Figure 5 It is a schematic diagram of the electricity trading situation among the microgrids in the simulation experiment in the fifth embodiment of the present invention.

[0024] Reference numerals in the drawings: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed implementation manners

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention.

[0026] In the following introduction, multiple embodiments of the present invention are provided. Different embodiments can be replaced or combined. Therefore, the present invention can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present invention should also be considered to include embodiments containing all other possible combinations of A, B, C, and D, even though such embodiments may not be explicitly described in the following content.

[0027] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the elements described without departing from the scope of the present disclosure. Various processes or components may be appropriately omitted, substituted, or added to each example. For example, the methods described may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with respect to some examples may be combined into other examples.

[0028] To facilitate a better understanding of the embodiments of the present invention, before explaining the specific embodiments of the present invention in detail, the application scenarios thereof will be described first.

[0029] The distributed energy management method for a microgrid group described in the embodiments of this specification is applied to the operation process of a power system, covering typical scenarios such as high penetration of renewable energy, regulation of distributed power fluctuations, collaborative trading among multiple stakeholders, response to sudden abnormal power consumption, and adaptation to dynamic electricity price mechanisms. In these scenarios, the application of the distributed energy management method for the microgrid group aims to cope with the high volatility of renewable energy and improve energy consumption and stability.

[0030] Embodiment 1: As Figure 1 shown, this embodiment provides a distributed energy management method for a microgrid group based on event-driven, including the steps of: S1. Pre-define driving events, where the driving events include power over-limit events, energy storage capacity events, and electricity price adjustment events; S2. Establish a lower-layer optimization model for a microgrid with the minimum total operating cost of a single microgrid as the optimization goal; S3. Establish an upper-layer optimization model for the microgrid group with the minimum total cost of the target microgrid group as the optimization goal; S4. Real-time monitor the operating status of the microgrid group through a coroutine monitoring thread, and when the driving event is detected, trigger the lower-layer optimization model of the microgrid and the upper-layer optimization model of the microgrid group to execute isolation response: If the driving event is a local event, trigger the calculation of the lower-layer optimization model of the affected microgrid and output the optimal energy scheduling plan for the microgrid group; If the driving event is a global event, trigger the calculation of the lower-layer optimization model of the affected microgrid and notify the upper-layer optimization model of the microgrid group to start collaborative optimization through an event bus, and output the optimal energy scheduling plan for the microgrid group.

[0031] It is understandable that the prior art usually adopts a fixed - period polling mechanism or a full - volume data synchronization processing mode, resulting in the processor continuously consuming computing power for ineffective calculations even when there is no event trigger. In this embodiment, an event - driven mechanism is established to achieve independent triggering and isolated execution of control strategies. Among them, predefined events are detected in real - time by a dedicated coroutine monitoring thread, and the policy actions after triggering are physically isolated and run in an independent process space. The specific process includes three stages: policy definition, event modeling, and policy parsing. By converting the control logic into a Boolean logic expression and performing formal verification, a task - scheduling topology based on a directed acyclic graph is finally constructed.

[0032] Specifically, this embodiment provides a preferred implementation manner. When the driving event is a global event, the output of the optimal energy scheduling scheme for the micro - grid group includes: performing a Lagrangian transformation on the lower - layer optimization model of the micro - grid triggered for optimization, solving its KKT conditions and complementary slack conditions, and performing iterative calculations in combination with the upper - layer optimization model of the micro - grid group. When the change amount of the optimization variable is less than the preset error accuracy, the iteration is terminated, and the optimal energy scheduling scheme for the micro - grid group is output.

[0033] Specifically, this embodiment provides a preferred implementation manner. The total operating cost of a single micro - grid includes the cost of traditional generators, the cost of new - energy power generation, the operating cost of the energy - storage system, and the operating cost of micro - grid control; the lower - layer optimization model of the micro - grid satisfies power constraints, traditional generator ramp - up constraints, and energy - storage capacity constraints.

[0034] Specifically, this embodiment provides a preferred implementation manner. The expression of the lower - layer optimization model of the micro - grid is: , In the formula, is the total operating cost of the micro - grid , , , and are the operating costs of the traditional generator, new - energy, energy - storage system, and control system of the micro - grid at the moment, respectively; The constraints of the lower - layer optimization model of the micro - grid include: , , , , , , , , , , wherein, is the power of the traditional generator of the microgrid at time , is the upper limit of the power of the traditional generator of the microgrid at time , is the power of the traditional generator of the microgrid at time , is the upper limit of the ramping power of the traditional generator of the microgrid , is the power of the new energy of the microgrid at time , is the upper limit of the power of the new energy of the microgrid at time , is the purchased power of the microgrid at time , is the upper limit of the purchased power of the microgrid at time , is the sold power of the microgrid at time , is the upper limit of the sold power of the microgrid at time , is the capacity of the energy storage system of the microgrid at time , and are respectively the upper and lower limits of the capacity of the energy storage system of the microgrid at time , is the capacity of the energy storage system of the microgrid at time , is the time interval between time and time is the purchased power of the control system of the microgrid at time , is the sold power of the control system of the microgrid at time , is the sold power of the microgrid at time Total power at a moment.

[0035] Specifically, this embodiment provides a preferred implementation manner. The upper-layer optimization model of the microgrid group satisfies the internal energy trading price constraint, power balance constraint, and cost balance constraint. The expression of the upper-layer optimization model of the microgrid group is: , In the formula, is the total number of microgrids in the microgrid group, is the serial number of the microgrid; The constraints of the upper-layer optimization model of the microgrid group include: , , , , In the formula, is the price at which the microgrid group buys electricity from the large power grid at moment, is the price at which the microgrid group sells electricity to the large power grid at moment, is the price at which the microgrid group buys electricity internally at moment, is the price at which the microgrid group sells electricity internally at moment, is the electricity bought by the large power grid at moment, is the electricity sold by the large power grid at moment, is the electricity bought by the microgrid group internally at moment, is the electricity sold by the microgrid group internally at moment.

[0036] Specifically, this embodiment provides a preferred implementation manner. The KKT conditions of the lower-layer optimization model of the microgrid include: , , , , , , , In the formula, , , , , , , , , , , , and are dual variables.

[0037] Specifically, this embodiment provides a preferred implementation manner. The complementary slackness conditions of the lower-layer optimization model of the microgrid include: , , , , , , , , , , , , , The above formula represents .

[0038] Embodiment 2: As Figure 2As shown in the figure, this embodiment provides an event-driven distributed energy management system for a microgrid cluster, which is used to implement the distributed energy management method for a microgrid cluster as described in Embodiment 1. The system includes an event-driven module and a two-layer model building module. The event-driven module includes an event definition unit, a state monitoring unit, and an isolation response unit. The event definition unit is used to pre-define driving events, and the driving events include power limit violation events, energy storage capacity events, and electricity price adjustment events. The two-layer model building module includes a lower-layer optimization model building unit for a microgrid and an upper-layer optimization model building unit for a microgrid cluster. The lower-layer optimization model building unit for a microgrid is used to establish a lower-layer optimization model for a microgrid with the goal of minimizing the total operating cost of a single microgrid. The total operating cost of a single microgrid includes the cost of a traditional generator, the cost of new energy generation, the operating cost of an energy storage system, and the operating cost of microgrid control. The lower-layer optimization model for a microgrid satisfies power constraints, traditional generator ramp constraints, and energy storage capacity constraints. The upper-layer optimization model building unit for a microgrid cluster is used to establish an upper-layer optimization model for a microgrid cluster with the goal of minimizing the total cost of the target microgrid cluster. The upper-layer optimization model for a microgrid cluster satisfies internal energy trading price constraints, power balance constraints, and cost balance constraints. The state monitoring unit is used to monitor the operating state of the microgrid cluster in real time through a coroutine monitoring thread. The isolation response unit is used to trigger the lower-layer optimization model for a microgrid and the upper-layer optimization model for a microgrid cluster to execute isolation responses when the state monitoring unit monitors the driving events.

[0039] This embodiment optimizes the use of a hierarchical architecture design. The scheduling results of its microgrid cluster should first satisfy the lower-layer model and then the upper-layer model. This two-layer structure ensures a Pareto optimal balance between collective economic efficiency and local autonomy, strictly protecting the interests of each stakeholder while achieving global resource coordination. More specifically: the upper-layer optimization model represents the decision-making process of the microgrid cluster. It sets the internal energy trading price to minimize the total operating cost of the microgrid cluster. The lower-layer optimization model represents the individual decision-making process of the local microgrid, where each microgrid optimizes its scheduling plan based on the provided price and its own energy state to ensure optimal cost operation.

[0040] Specifically, this embodiment provides a preferred implementation manner. The execution isolation response unit includes a local response subunit and a global response subunit. The local response subunit is configured to trigger the calculation of the lower-layer microgrid optimization model of the affected microgrid when a local event is triggered, and output the optimal energy scheduling scheme for the microgrid group. The global response subunit is configured to trigger the calculation of the lower-layer microgrid optimization model of the affected microgrid when a global event is triggered, and notify the upper-layer optimization model of the microgrid group to start collaborative optimization through the event bus, and output the optimal energy scheduling scheme for the microgrid group. This embodiment avoids the influence of local events on other microgrids, realizes the rapid processing of local problems, and improves the stability and response speed of the system. When a global event is detected, the upper-layer optimization model is notified to start collaborative optimization through the event bus, enabling each microgrid in the microgrid group to perform coordinated optimization according to the global information, realizing the global reasonable allocation of resources, and improving the overall operation performance of the microgrid group.

[0041] Specifically, this embodiment provides a preferred implementation manner. The execution isolation response unit further includes an optimal energy calculation subunit. The optimal energy calculation subunit is configured to perform Lagrangian transformation on the lower-layer microgrid optimization model triggered for optimization when a global event is triggered, solve its KKT conditions and complementary slack conditions, and perform iterative calculation in combination with the upper-layer optimization model of the microgrid group. When the change amount of the optimization variable is less than the preset error precision, the iteration is terminated, and the optimal energy scheduling scheme for the microgrid group is output. This embodiment introduces decoupling technology to perform distributed decoupling on the global energy management optimization model of the microgrid group, making the solution process more efficient. At the same time, event-driven is introduced to solve the distributed decoupled global energy management optimization model, improving the accuracy and speed of the solution.

[0042] Embodiment Three: As Figure 3 shown, this embodiment provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0043] Among them, the communication bus can be used to realize the connection and communication of the above-mentioned various components.

[0044] Among them, the user interface may include buttons, and the optional user interface may further include a standard wired interface and a wireless interface.

[0045] Among them, the network interface may but is not limited to including a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0046] Among them, the processor may include one or more processing cores. The processor uses various interfaces and circuits to connect various parts within the entire electronic device, and by running or executing instructions, programs, code sets, or instruction sets stored in the memory, and by calling the data stored in the memory, it executes various functions of the electronic device and processes data. Optionally, the processor may be implemented in at least one hardware form of DSP, FPGA, or PLA. The processor may integrate a combination of one or more of CPU, GPU, and modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor and may be implemented separately through a single chip.

[0047] Among them, the memory may include RAM and may also include ROM. Optionally, the memory includes a non-transitory computer-readable medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory may also be at least one storage device located far from the aforementioned processor. As a computer storage medium, the memory may include an operating system, a network communication module, a user interface module, and an energy management application program. The processor can be used to call the energy management application program stored in the memory and execute the steps of energy management mentioned in the foregoing embodiments.

[0048] Embodiment 4: This embodiment provides a computer-readable storage medium, in which instructions are stored. When they run on a computer or a processor, they cause the computer or the processor to execute one or more of the steps in the above-mentioned Figure 1 illustrated embodiments. If the various component modules of the above-mentioned electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0049] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this specification are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a Digital Versatile Disc (DVD)), or a semiconductor medium (for example, a Solid State Disk (SSD)), etc.

[0050] Those of ordinary skill in the art can understand that all or part of the processes in implementing the method in the above Embodiment 1 can be completed by instructing relevant hardware through a computer program. This program can be stored in a computer-readable storage medium. When this program is executed, it can include the processes of the embodiments of the above methods. The foregoing storage medium includes various media that can store program codes, such as ROM, RAM, magnetic disks, or optical discs. Without conflict, the technical features in this embodiment and the implementation solutions can be combined arbitrarily.

[0051] Embodiment Five: To verify the effectiveness of the method and system for distributed energy management of a microgrid group based on event-driven described in this specification, in this embodiment, based on the actual application scenario of the distributed energy management system of the microgrid group, a microgrid group formed by connecting 3 microgrids is built, and its basic operating parameters are as Figure 4 shown. In this embodiment, a control group and an experimental group are set up to conduct a comparative experiment. Among them, the control group does not adopt the method and system described in this specification, while the experimental group does. The experiment sets the time step to 1 hour. Under this condition, the control group and the experimental group respectively carry out global energy management work for the microgrid group to clearly evaluate the actual effects of the method and system through the comparison results. As Figure 5As shown, it is the electricity trading situation among microgrids. By adopting the distributed energy management method and system for microgrid clusters described in this specification, active electricity exchange occurs among microgrids, achieving the optimization of global energy management for microgrid clusters. Specifically: As shown in Table 1, by adopting the distributed energy management method and system for microgrid clusters described in this specification, the global operation cost of the microgrid cluster is effectively saved by about 11.54%; As shown in Table 2, by adopting the distributed energy management method and system for microgrid clusters described in this specification, the new energy consumption rate is effectively increased by about 12.43%.

[0052] Table 1 Operating Costs and Relative Profits of Microgrid Clusters (Unit: Yuan)

[0053] Table 2 New Energy Consumption Rates and Relative Improvements of Microgrid Clusters (Unit: %)

[0054] Based on the above, this embodiment verifies the effectiveness of a distributed energy management method and system for microgrid clusters based on event-driven described in this specification.

[0055] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be carried out in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0056] In the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0057] The above are only exemplary embodiments of the present invention, and the scope of the present invention cannot be limited thereby. That is, all equivalent changes and modifications made according to the teachings of the present invention still fall within the scope covered by the present invention. Those skilled in the art will easily think of other implementation schemes of the present invention after considering the specification and practicing the disclosure here. The present invention aims to cover any variations, uses, or adaptive changes of the present invention, and these variations, uses, or adaptive changes follow the general principles of the present invention and include the common general knowledge or conventional technical means in the technical field not recorded in the present invention. The specification and embodiments are only regarded as exemplary, and the scope and spirit of the present invention are defined by the claims.

Claims

1. A distributed energy management method for a microgrid group based on event-driven, characterized in that Including the steps: S1. Pre-define driving events, where the driving events include power over-limit events, energy storage capacity events, and electricity price adjustment events; S2. Establish a lower-layer optimization model of the microgrid with the minimum total operating cost of a single microgrid as the optimization objective; S3. Establish an upper-layer optimization model of the microgrid group with the minimum total cost of the target microgrid group as the optimization objective; S4. Monitor the operating status of the microgrid group in real time through a coroutine monitoring thread. When the driving event is detected, trigger the lower-layer optimization model of the microgrid and the upper-layer optimization model of the microgrid group to execute isolation response: If the driving event is a local event, trigger the calculation of the lower-layer optimization model of the affected microgrid and output the optimal energy scheduling plan of the microgrid group; If the driving event is a global event, trigger the calculation of the lower-layer optimization model of the affected microgrid, and notify the upper-layer optimization model of the microgrid group to start collaborative optimization through the event bus, and output the optimal energy scheduling plan of the microgrid group.

2. The distributed energy management method for a microgrid group based on event-driven according to claim 1, characterized in that When the driving event is a global event, the output of the optimal energy scheduling plan of the microgrid group includes: Perform Lagrangian transformation on the lower-layer optimization model of the microgrid triggered for optimization, solve its KKT conditions and complementary slack conditions, and perform iterative calculation in combination with the upper-layer optimization model of the microgrid group. When the change amount of the optimization variable is less than the preset error accuracy, terminate the iteration and output the optimal energy scheduling plan of the microgrid group.

3. A distributed energy management method for a microgrid group based on event-driven according to claim 1, characterized in that: The total operating cost of a single microgrid includes the cost of traditional generators, the cost of new energy generation, the operating cost of the energy storage system, and the operating cost of microgrid control; The lower-layer optimization model of the microgrid satisfies power constraints, traditional generator ramp constraints, and energy storage capacity constraints.

4. An event-driven distributed energy management method for a microgrid cluster according to claim 3, characterized in that The expression of the lower-layer optimization model of the microgrid is: ， In the formula, is the total operating cost of the microgrid, , , and are the operating costs of the traditional generator, new energy, energy storage system, and control system of the microgrid at the moment, respectively.

5. A distributed energy management method for a microgrid group based on event-driven according to claim 1, characterized in that: The upper-layer optimization model of the microgrid group satisfies internal energy trading price constraints, power balance constraints, and cost balance constraints; The expression of the upper-layer optimization model of the microgrid group is: ， In the formula, is the total number of microgrids in the microgrid group, is the serial number of the microgrid.

6. A distributed energy management system for a microgrid group based on event-driven, characterized in that, For implementing the distributed energy management method for a microgrid group according to any one of claims 1 to 5; The system includes an event-driven module and a two-layer model building module; The event-driven module includes an event definition unit, a status monitoring unit, and an isolation response unit; The event definition unit is used to pre-define driving events, where the driving events include power over-limit events, energy storage capacity events, and electricity price adjustment events; The two-layer model building module includes a lower-layer optimization model building unit of the microgrid and an upper-layer optimization model building unit of the microgrid group; The lower-layer optimization model building unit of the microgrid is used to establish a lower-layer optimization model of the microgrid with the minimum total operating cost of a single microgrid as the optimization objective. The total operating cost of a single microgrid includes the cost of traditional generators, the cost of new energy generation, the operating cost of the energy storage system, and the operating cost of microgrid control. The lower-layer optimization model of the microgrid satisfies power constraints, traditional generator ramp constraints, and energy storage capacity constraints; The upper-layer optimization model construction unit of the microgrid group is used to establish an upper-layer optimization model of the microgrid group with the minimum total cost of the target microgrid group as the optimization goal. The upper-layer optimization model of the microgrid group satisfies internal energy trading price constraints, power balance constraints, and cost balance constraints; The state monitoring unit is used to monitor the operating state of the microgrid group in real time through a coroutine monitoring thread; The isolation response unit is used to trigger the lower-layer optimization model of the microgrid and the upper-layer optimization model of the microgrid group to execute isolation responses when the state monitoring unit monitors the driving event.

7. The distributed energy management system of a microgrid group based on event-driven according to claim 6, characterized in that: The execution isolation response unit includes a local response subunit and a global response subunit; The local response subunit is used to trigger the calculation of the lower-layer optimization model of the affected microgrid when a local event is triggered, and output the optimal energy scheduling plan of the microgrid group; The global response subunit is used to trigger the calculation of the lower-layer optimization model of the affected microgrid when a global event is triggered, and notify the upper-layer optimization model of the microgrid group to start collaborative optimization through an event bus, and output the optimal energy scheduling plan of the microgrid group.

8. The distributed energy management system of a microgrid group based on event-driven according to claim 7, characterized in that: The execution isolation response unit further includes an optimal energy calculation subunit; The optimal energy calculation subunit is used to perform Lagrangian transformation on the lower-layer optimization model of the triggered optimization when a global event is triggered, solve its KKT conditions and complementary slack conditions, and perform iterative calculation in combination with the upper-layer optimization model of the microgrid group. When the change amount of the optimization variable is less than the preset error accuracy, the iteration is terminated, and the optimal energy scheduling plan of the microgrid group is output.

9. A computer device, the computer device comprising a memory, a processor, and a computer program, characterized in that, The computer program, when executed by a processor, implements the distributed energy management method of the microgrid group according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, implements the distributed energy management method of the microgrid group according to any one of claims 1 to 5.

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