A micro-grid distributed resource transaction model modeling method and device

Abstract

The application discloses a micro-grid distributed resource transaction model modeling method and device, and the scheme of the application collects power data of each micro-grid node in the process of power grid operation, constructs an energy balance relationship formula according to the renewable energy production data, the energy storage state data of the battery energy storage system and the energy consumption data of the power load in the power data, constructs a calculation state supply-demand constraint relationship formula according to the calculation task and the calculation resource of the edge computing load, and constructs a micro-grid operation cost function according to the energy balance relationship formula and the calculation state supply-demand constraint relationship formula, and then obtains the micro-grid distributed resource transaction model. In the construction of the micro-grid distributed resource transaction model, the dynamic power loss brought by the edge device to the power terminal is considered, so that the micro-grid distributed resource transaction is closer to the real situation, and the power supply of the micro-grid can realize the sustainable operation of the MEC system and meet the calculation demand of the micro-grid community users.

Description

Technical Field

[0001] This application relates to the field of microgrid and edge computing technology, and in particular to a modeling method and apparatus for a distributed resource trading model in a microgrid. Background Technology

[0002] Currently, traditional energy sources face problems such as resource depletion and environmental pollution. The emergence of new energy sources provides solutions for various energy acquisition and environmental degradation. Although traditional centralized power generation offers stable long-distance transmission, it also suffers from problems such as high transmission costs and a single power source. Therefore, distributed generation technology has recently seen vigorous development, allowing users with distributed generation equipment to form a multi-microgrid system with other electricity users. The rapid popularization of distributed energy resources has brought significant challenges to centralized power systems with unidirectional power flow. Meanwhile, in the current era of fifth-generation networks, smart services are continuously expanding through Internet of Things (IoT) applications. Multi-access edge computing (MEC) is a revolutionary key technology, bringing application-oriented computing capabilities to the edge of operator networks. In recent years, MEC has faced challenges in low-latency scheduling, scalability, and sustainability. Appropriate energy scheduling can address the sustainability issues of MEC caused by energy failures. To solve this problem, the energy consumption of the MEC network and the power generation of the microgrid need to be strongly coordinated within a certain time frame.

[0003] Existing distributed resource control (DRG) technology for microgrids involves edge devices acquiring real-time data from the microgrid's power terminals via sensors. This data is processed by the edge devices for local computation and simultaneously uploaded to a cloud server. Edge computing nodes utilize local information and information from their communicating neighboring nodes to design an edge information confidence evaluation mechanism. After data analysis by the cloud server, periodic or aperiodic confidence thresholds are issued to evaluate the confidence levels of local observations and received neighboring node information, representing this as a confidence factor. Multiple edge computing nodes employ a distributed cooperative control method, sending control decision commands obtained from secondary control to the droop control of the microgrid's distributed resource generation (DG) to achieve frequency synchronization and stabilization to the reference value. However, in practical applications, existing distributed cooperative control for microgrids still exhibits significant errors, failing to simultaneously ensure the sustainable operation of edge computing and meet the computing needs of microgrid community users. Summary of the Invention

[0004] This application provides a modeling method and apparatus for a distributed resource trading model in microgrids, which addresses the technical problem that existing distributed collaborative control systems for microgrids cannot achieve sustainable operation while taking edge computing into account and meeting the computing needs of microgrid community users.

[0005] To address the aforementioned technical problems, the first aspect of this application provides a modeling method for distributed resource trading in microgrids, comprising:

[0006] The system collects power data from each microgrid node during grid operation. The power data includes: capacity data of renewable energy equipment, energy storage status data of battery energy storage systems, energy consumption data of power loads, and computing status data of edge computing loads. The computing status data includes: computing tasks to be processed and available computing resources.

[0007] Based on the production capacity data, the energy storage status data, and the energy consumption data of the power load, an energy balance relationship is constructed;

[0008] Based on the computing task and the computing resources, construct a supply and demand constraint relationship for the computing state;

[0009] Based on the energy balance equation and the supply and demand constraint equation of the calculation state, a microgrid operation cost function is constructed, and based on the microgrid operation cost function, a microgrid distributed resource trading model is obtained.

[0010] Preferably, the energy consumption data of the power load includes: the static energy consumption of conventional loads, the dynamic energy consumption of flexible loads, and the operating energy consumption of edge computing loads.

[0011] Preferably, the energy balance equation is as follows:

[0012]

[0013] In the formula, i and j represent microgrid nodes, and M represents all microgrid nodes. This represents the amount of electricity purchased by microgrid node i from the P2P market. This represents the amount of electricity purchased by microgrid node i from the main grid. This represents the amount of electricity sold by microgrid node i to the P2P market. This represents the amount of electricity sold by microgrid node i from the main grid. The dynamic energy consumption, The static energy consumption is... The operating energy consumption, The energy output of the renewable energy equipment. This refers to the discharge capacity of the battery energy storage system. The amount of charge applied to the battery energy storage system.

[0014] Preferably, the calculation state supply and demand constraint relationship is specifically as follows:

[0015]

[0016]

[0017] In the formula, This represents the computing resources purchased by microgrid node i from the cloud data center per unit time slot t. This represents the computational capability of microgrid node i per unit time slot t. The computational task assigned to microgrid node i by microgrid node j through a P2P market transaction within a unit time slot t is called the computational task. The computational task initially assigned to microgrid node i in unit time slot t.

[0018] Preferably, the objective function of the microgrid distributed resource trading model is:

[0019]

[0020]

[0021] In the formula, p represents the real-time trading price of electricity, c represents the real-time trading price of computing resources, subscripts i and j represent microgrid nodes, and subscript g represents the main grid. This represents the amount of electricity purchased by microgrid node i from the P2P market. This represents the amount of electricity purchased by microgrid node i from the main grid. This represents the amount of electricity sold by microgrid node i to the P2P market per unit time slot t. This represents the amount of electricity sold by microgrid node i from the main grid per unit time slot t. The computational task assigned to microgrid node i by microgrid node j through a P2P market transaction within a unit time slot t is called the computational task. This represents the computing resources that microgrid node i purchases from the cloud data center per unit time slot t.

[0022] The second aspect of this application provides a modeling device for a distributed resource trading model in a microgrid, comprising:

[0023] The power data acquisition unit is used to collect power data from various microgrid nodes during the operation of the power grid. The power data includes: the production capacity data of renewable energy equipment, the energy storage status data of battery energy storage system, the energy consumption data of power load, and the computing status data of edge computing load. The computing status data includes: the computing tasks to be processed and the available computing resources.

[0024] An energy balance relationship construction unit is used to construct an energy balance relationship based on the production capacity data, the energy storage status data, and the energy consumption data of the power load.

[0025] The computational state supply and demand constraint relationship construction unit is used to construct the computational state supply and demand constraint relationship based on the computational task and the computational resources.

[0026] The microgrid distributed resource trading model construction unit is used to construct a microgrid operating cost function based on the energy balance relationship and the supply and demand constraint relationship of the calculation state, and to obtain a microgrid distributed resource trading model based on the microgrid operating cost function.

[0027] Preferably, the energy consumption data of the power load includes: the static energy consumption of conventional loads, the dynamic energy consumption of flexible loads, and the operating energy consumption of edge computing loads.

[0028] Preferably, the energy balance equation is as follows:

[0029]

[0030] In the formula, i and j represent microgrid nodes, and M represents all microgrid nodes. This represents the amount of electricity purchased by microgrid node i from the P2P market. This represents the amount of electricity purchased by microgrid node i from the main grid. This represents the amount of electricity sold by microgrid node i to the P2P market. This represents the amount of electricity sold by microgrid node i from the main grid. The dynamic energy consumption, The static energy consumption is... The operating energy consumption, The energy output of the renewable energy equipment. This refers to the discharge capacity of the battery energy storage system. The amount of charge applied to the battery energy storage system.

[0031] Preferably, the calculation state supply and demand constraint relationship is specifically as follows:

[0032]

[0033]

[0034] In the formula, This represents the computing resources purchased by microgrid node i from the cloud data center per unit time slot t. This represents the computational capability of microgrid node i per unit time slot t. The computational task assigned to microgrid node i by microgrid node j through a P2P market transaction within a unit time slot t is called the computational task. The computational task initially assigned to microgrid node i in unit time slot t.

[0035] Preferably, the objective function of the microgrid distributed resource trading model is:

[0036]

[0037]

[0038] In the formula, p represents the real-time trading price of electricity, c represents the real-time trading price of computing resources, subscripts i and j represent microgrid nodes, and subscript g represents the main grid. This represents the amount of electricity purchased by microgrid node i from the P2P market. This represents the amount of electricity purchased by microgrid node i from the main grid. This represents the amount of electricity sold by microgrid node i to the P2P market per unit time slot t. This represents the amount of electricity sold by microgrid node i from the main grid per unit time slot t. The computational task assigned to microgrid node i by microgrid node j through a P2P market transaction within a unit time slot t is called the computational task. This represents the computing resources that microgrid node i purchases from the cloud data center per unit time slot t.

[0039] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0040] The microgrid distributed resource trading modeling method provided in this application collects power data from each microgrid node during grid operation. Based on renewable energy capacity data, battery energy storage system energy storage status data, and power load energy consumption data, an energy balance equation is constructed. Furthermore, based on the computing tasks and resources of the edge computing load, a computing state supply and demand constraint equation is constructed. Finally, based on the energy balance equation and the computing state supply and demand constraint equation, a microgrid operating cost function is constructed, thus obtaining the microgrid distributed resource trading model. The solution in this application considers the dynamic power loss brought to the power terminal by edge devices in the obtained microgrid distributed resource trading model, making the microgrid distributed resource trading situation closer to reality. This enables the microgrid's power supply to both meet the challenges of sustainable operation of the MEC system and satisfy the computing needs of microgrid community users. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating an embodiment of a microgrid distributed resource trading modeling method provided in this application.

[0043] Figure 2This is a schematic diagram of an embodiment of a microgrid distributed resource trading modeling device provided in this application. Detailed Implementation

[0044] Existing distributed resource control (DRG) technology for microgrids involves edge devices acquiring real-time data from the microgrid's power terminals via sensors. This data is processed by the edge devices for local computation and simultaneously uploaded to a cloud server. Edge computing nodes utilize local information and information from their communicating neighboring nodes to design an edge information confidence evaluation mechanism. After data analysis by the cloud server, periodic or aperiodic confidence thresholds are issued to evaluate the confidence levels of local observations and received neighboring node information, representing this as a confidence factor. Multiple edge computing nodes employ a distributed cooperative control method, sending control decision commands obtained from secondary control to the droop control of the microgrid's distributed resource generation (DG) to achieve frequency synchronization and stabilization to the reference value. However, in practical applications, existing distributed cooperative control for microgrids still exhibits significant errors, failing to simultaneously ensure the sustainable operation of edge computing and meet the computing needs of microgrid community users.

[0045] In light of the aforementioned technical background, the applicant discovered through research that the cause of the aforementioned technical problems lies in the fact that existing technologies only consider the collaborative control of microgrids and edge computing nodes, without taking into account the dynamic power loss brought to the power terminal by edge devices, resulting in deviations in the distributed resource control of existing microgrids.

[0046] In view of this, the present application provides a modeling method and apparatus for a distributed resource trading model of a microgrid. By establishing a distributed resource trading model of multiple microgrids equipped with an MEC system, the microgrids can trade electrical energy and computing resources. By optimizing the resource trading method, the overall system operating cost is minimized, thereby solving the technical problem that the existing distributed collaborative control of microgrids cannot achieve sustainable operation of edge computing and meet the computing needs of microgrid community users.

[0047] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0048] Please see Figure 1 The first embodiment of this application provides a modeling method for a distributed resource trading model in a microgrid, including:

[0049] Step 101: Collect power data from each microgrid node during grid operation. Power data includes: capacity data of renewable energy equipment, energy storage status data of battery energy storage system, energy consumption data of power load, and computing status data of edge computing load. Computing status data includes: computing tasks to be processed and available computing resources.

[0050] It's important to note that in the multi-microgrid P2P model based on MEC systems, each microgrid is connected to the main power grid's power transmission line, and they are also interconnected by power transmission lines. A microgrid can trade electricity with the main grid or other microgrids, and they can also trade computing resources with each other. When users at a node have a certain demand for mobile computing, meaning the node's computing tasks reach a certain level, the node's MEC system will activate and perform edge computing locally within that microgrid node. In other words, in each time slot, a microgrid node will receive a certain amount of computing tasks requiring local computing resources, which are then completed by the microgrid's MEC system. When the computing tasks are overloaded and the microgrid node's MEC system lacks sufficient computing resources, it can purchase computing resources from a cloud data center.

[0051] In the distributed resource trading system model, in addition to being connected to the main power grid, all microgrids are interconnected, allowing them to initiate electricity trading with each other or with the main grid. This embodiment considers an energy community (i.e., a microgrid) consisting of a set of nodes i,j∈M. A node can represent one or more buildings in the distributed network. Without loss of generality, this embodiment analyzes the operation of microgrids in a multi-microgrid system with a sampling interval of one day and an hourly time slot, for a sample of t∈T={1,......,24}.

[0052] An energy community can possess various distributed energy sources, such as wind power systems (WT), photovoltaic (PV) systems, and energy storage systems (BESS). This paper assumes that the energy of WT and PV at time i is completely known. However, renewable energy is generated by WT and PV during the day and depends on natural environmental factors such as sunlight intensity and wind speed. Therefore, renewable energy has an upper limit, as shown in the following formula:

[0053]

[0054] in This represents the maximum amount of renewable energy that an energy community can generate per unit time slot. This example assumes that the renewable energy output is the same within the same unit time slot.

[0055] With the increasing penetration of DER (Dynamic Energy Storage) technologies, BESS (Power Storage System) is gaining popularity in every microgrid community, similar to Tesla Powerwall and other energy storage products. Microgrid energy can be stored by BESS, including clean energy and purchased energy. Within each microgrid node, the BESS provides power to other internal electrical loads. Therefore, this embodiment allows... This indicates the available battery energy in the current time slot, and the amount of energy used... This represents the amount of electricity stored in the previous time slot. Therefore, the energy storage state expression for BESS is:

[0056]

[0057] Among them, the discharge power and the charging power are respectively used as and express. and These represent the actual efficiency of charging and discharging, respectively. and All have upper and lower bound constraints, as follows:

[0058]

[0059]

[0060]

[0061] In the formula This is the maximum storage capacity of BESS. and These are defined as the battery's maximum discharge and charge power, respectively.

[0062] More specifically, the energy consumption data of the power load mentioned in this embodiment includes: the static energy consumption of conventional loads, the dynamic energy consumption of flexible loads, and the operating energy consumption of edge computing loads.

[0063] Microgrid energy communities support various types of electrical loads, which are categorized into three types in this embodiment: essential loads, resilient loads, and the operating load of MEC servers. In actual microgrid buildings, everyday lighting, electronic devices, etc., are typical essential electrical loads that meet power demand. Therefore, this embodiment assumes that the static energy consumption of essential loads is a constant. This embodiment can schedule the dynamic energy consumption of elastic loads at any time. This refers to various electrical loads, such as disinfection facilities and entertainment equipment. Therefore, the lower and upper bounds of this variable are set as follows:

[0064]

[0065] in, and These are defined as the minimum and maximum values ​​of dynamic energy consumption, respectively. Therefore, this embodiment yields the following constraint expression for 24-hour energy dispatch balance:

[0066]

[0067] In the formula This represents the total dynamic energy consumption demand within a day.

[0068] A MEC system consists of multiple mobile edge computing servers, each with a certain number of CPU cores. The number of CPU cycles determines the CPU's operating frequency, thus affecting the MEC system's energy consumption. Therefore, this embodiment can obtain the total energy consumption of the MEC system per unit time slot. We get the following formula:

[0069]

[0070]

[0071] The total CPU operating frequency of the MEC system is expressed as f. i,t . and This indicates the lower and upper limits of the operating frequency fluctuation range of the MEC. This represents the effective capacitance coefficient of the MEC server.

[0072] In a microgrid energy community, users' computing tasks are required to be completed within each unit time slot to meet computing demands. In other words, the MEC server needs to consume a certain amount of energy to complete the computing task within each unit time slot. Therefore, this embodiment can obtain... This represents the computational task of initial allocation of a microgrid per unit time slot.

[0073]

[0074] in It describes the total amount of computational tasks that multiple microgrids need to complete together in a unit time slot, which is equivalent to meeting the total computational needs of the entire multi-microgrid energy community at any time period. The computing power of a single microgrid node is represented as follows:

[0075]

[0076] This formula represents the local computing resources of the MEC system at a microgrid node.

[0077] Step 102: Based on the production capacity data, energy storage status data, and power load energy consumption data, construct an energy balance relationship.

[0078] It should be noted that when the power consumption of a microgrid node exceeds the sum of the generation from renewable energy sources and the state of local energy storage, the microgrid node may need to purchase its power from the main grid or a peer-to-peer (P2P) trading market, and vice versa. Therefore, the energy balance formula for each unit time slot during microgrid node operation must satisfy the following equation:

[0079]

[0080] In the formula, i and j represent microgrid nodes, and M represents all microgrid nodes. This represents the amount of electricity purchased by microgrid node i from the P2P market. This represents the amount of electricity purchased by microgrid node i from the main grid. This represents the amount of electricity sold by microgrid node i to the P2P market. This represents the amount of electricity sold by microgrid node i from the main grid. The dynamic energy consumption, The static energy consumption is... The operating energy consumption, The energy output of the renewable energy equipment. This refers to the discharge capacity of the battery energy storage system. The amount of charge applied to the battery energy storage system.

[0081] Step 103: Based on the computing task and computing resources, construct the supply and demand constraint relationship of computing status.

[0082] It should be noted that this embodiment studies a distributed peer-to-peer resource trading market model, which involves multiple resources, such as electricity and computing resources. Each node can trade a certain amount of electricity and computing resources with other microgrid nodes. When a node's computing tasks are overloaded and its computing resources are insufficient, the node can purchase computing resources from a cloud data center or from a P2P market. This represents the computing resources that a microgrid node needs to purchase from a cloud data center. Therefore, the balance function between the computing tasks and computing resources of a single node can be derived.

[0083]

[0084]

[0085] in, This represents the computing resources purchased by microgrid node i from the cloud data center per unit time slot t. This represents the computational capability of microgrid node i per unit time slot t. The computational task initially assigned to microgrid node i in unit time slot t. This indicates the computing tasks obtained from other microgrid nodes in the P2P trading market. When this parameter is negative, it indicates that the computing tasks of the current node have been migrated to other microgrid nodes.

[0086] Step 104: Based on the energy balance equation and the supply and demand constraint equation of the calculation state, construct the microgrid operation cost function, and based on the microgrid operation cost function, obtain the microgrid distributed resource trading model.

[0087] It should be noted that in the resource trading model of the entire multi-microgrid system, this embodiment assumes that all energy communities in the P2P trading market are non-strategically rational nodes, and each node needs to solve optimal resource trading decisions with other nodes. The cost function of the microgrid can be considered as the following optimization problem:

[0088]

[0089] Where factors p and c represent the real-time trading prices of electrical energy and computing resources. Taking all factors into account, the optimization objective of this embodiment is the cost function of the entire multi-microgrid system during a day's operation:

[0090]

[0091] A heuristic algorithm is used to solve the problem and optimize the optimal decision variables for resource trading in the model, thereby minimizing the operating cost function of the multi-microgrid system.

[0092] The above is a detailed description of an embodiment of a microgrid distributed resource trading model modeling method provided in this application. The following is a detailed description of an embodiment of a microgrid distributed resource trading model modeling device provided in this application.

[0093] Please see Figure 2 The second aspect of this application provides a microgrid distributed resource trading modeling device, comprising:

[0094] The power data acquisition unit 201 is used to collect power data from each microgrid node during the operation of the power grid. The power data includes: the production capacity data of renewable energy equipment, the energy storage status data of battery energy storage system, the energy consumption data of power load, and the computing status data of edge computing load. The computing status data includes: the computing tasks to be processed and the available computing resources.

[0095] Energy balance relationship construction unit 202 is used to construct energy balance relationship based on production capacity data, energy storage status data and power load energy consumption data;

[0096] The computation state supply and demand constraint relationship construction unit 203 is used to construct the computation state supply and demand constraint relationship based on the computation task and computation resources;

[0097] The microgrid distributed resource trading model construction unit 204 is used to construct the microgrid operating cost function based on the energy balance relationship and the supply and demand constraint relationship of the calculation state, and to obtain the microgrid distributed resource trading model based on the microgrid operating cost function.

[0098] Furthermore, the energy consumption data of the power load includes: the static energy consumption of conventional loads, the dynamic energy consumption of flexible loads, and the operating energy consumption of edge computing loads.

[0099] Furthermore, the energy balance equation is as follows:

[0100]

[0101] In the formula, i and j represent microgrid nodes, and M represents all microgrid nodes. This represents the amount of electricity purchased by microgrid node i from the P2P market. This represents the amount of electricity purchased by microgrid node i from the main grid. This represents the amount of electricity sold by microgrid node i to the P2P market. This represents the amount of electricity sold by microgrid node i from the main grid. The dynamic energy consumption, The static energy consumption is... The operating energy consumption, The energy output of the renewable energy equipment. This refers to the discharge capacity of the battery energy storage system. The amount of charge applied to the battery energy storage system.

[0102] Furthermore, the specific formula for calculating the supply and demand constraint relationship is as follows:

[0103]

[0104]

[0105] In the formula, This represents the computing resources purchased by microgrid node i from the cloud data center per unit time slot t. This represents the computational capability of microgrid node i per unit time slot t. The computational task assigned to microgrid node i by microgrid node j through a P2P market transaction within a unit time slot t is called the computational task. The computational task initially assigned to microgrid node i in unit time slot t.

[0106] Furthermore, the objective function of the microgrid distributed resource trading model is:

[0107]

[0108]

[0109] In the formula, p represents the real-time trading price of electricity, c represents the real-time trading price of computing resources, subscripts i and j represent microgrid nodes, and subscript g represents the main grid. This represents the amount of electricity purchased by microgrid node i from the P2P market. This represents the amount of electricity purchased by microgrid node i from the main grid. This represents the amount of electricity sold by microgrid node i to the P2P market per unit time slot t. This represents the amount of electricity sold by microgrid node i from the main grid per unit time slot t. The computational task assigned to microgrid node i by microgrid node j through a P2P market transaction within a unit time slot t is called the computational task. This represents the computing resources that microgrid node i purchases from the cloud data center per unit time slot t.

[0110] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the terminals, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0112] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application 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 embodiments of the application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, 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.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0115] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0116] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A modeling method for distributed resource trading in microgrids, characterized in that, include: The system collects power data from each microgrid node during grid operation. The power data includes: capacity data of renewable energy equipment, energy storage status data of battery energy storage system, energy consumption data of power load, and computing status data of edge computing load. The computing status data includes: computing tasks to be processed and available computing resources. The energy consumption data of power load includes: static energy consumption of conventional load, dynamic energy consumption of elastic load, and operating energy consumption of edge computing load. Based on the production capacity data, the energy storage status data, and the energy consumption data of the power load, an energy balance relationship is constructed; Based on the computing task and the computing resources, construct a supply and demand constraint relationship for the computing state; Based on the energy balance equation and the supply and demand constraint equation of the calculation state, a microgrid operation cost function is constructed, and based on the microgrid operation cost function, a microgrid distributed resource trading model is obtained. The specific formula for calculating the supply and demand constraint relationship is as follows: In the formula, This represents the computing resources purchased by microgrid node i from the cloud data center per unit time slot t. This represents the computational capability of microgrid node i per unit time slot t. The computational task assigned to microgrid node i by microgrid node j through a P2P market transaction within a unit time slot t is called the computational task. The computational task initially assigned to microgrid node i in a unit time slot t; The objective function of the microgrid distributed resource trading model is: In the formula, p represents the real-time trading price of electricity, c represents the real-time trading price of computing resources, subscripts i and j represent microgrid nodes, and subscript g represents the main grid. This represents the amount of electricity purchased by microgrid node i from the P2P market. This represents the amount of electricity purchased by microgrid node i from the main grid. This represents the amount of electricity sold by microgrid node i to the P2P market per unit time slot t. This represents the amount of electricity sold by microgrid node i from the main grid per unit time slot t. The computational task assigned to microgrid node i by microgrid node j through a P2P market transaction within a unit time slot t is called the computational task. This represents the computing resources that microgrid node i purchases from the cloud data center per unit time slot t.

2. The microgrid distributed resource trading modeling method according to claim 1, characterized in that, The energy balance equation is as follows: In the formula, i and j represent microgrid nodes, and M represents all microgrid nodes. This represents the amount of electricity purchased by microgrid node i from node j through the P2P market. This represents the amount of electricity purchased by microgrid node i from the main grid. This represents the amount of electricity sold by microgrid node i to node j through the P2P market. This represents the amount of electricity sold by microgrid node i from the main grid. The dynamic energy consumption, The static energy consumption is... The operating energy consumption, The energy output of the renewable energy equipment. This refers to the discharge capacity of the battery energy storage system. The amount of charge applied to the battery energy storage system.

3. A modeling device for distributed resource trading in microgrids, characterized in that, include: The power data acquisition unit is used to collect power data from various microgrid nodes during the operation of the power grid. The power data includes: the production capacity data of renewable energy equipment, the energy storage status data of battery energy storage system, the energy consumption data of power load, and the computing status data of edge computing load. The computing status data includes: the computing tasks to be processed and the available computing resources. The energy consumption data of power load includes: the static energy consumption of conventional load, the dynamic energy consumption of elastic load, and the operating energy consumption of edge computing load. An energy balance relationship construction unit is used to construct an energy balance relationship based on the production capacity data, the energy storage status data, and the energy consumption data of the power load. The computational state supply and demand constraint relationship construction unit is used to construct the computational state supply and demand constraint relationship based on the computational task and the computational resources. The microgrid distributed resource trading model construction unit is used to construct a microgrid operating cost function based on the energy balance relationship and the supply and demand constraint relationship of the calculation state, and to obtain a microgrid distributed resource trading model based on the microgrid operating cost function. The specific formula for calculating the supply and demand constraint relationship is as follows: In the formula, This represents the computing resources purchased by microgrid node i from the cloud data center per unit time slot t. This represents the computational capability of microgrid node i per unit time slot t. The computational task assigned to microgrid node i by microgrid node j through a P2P market transaction within a unit time slot t is called the computational task. The computational task initially assigned to microgrid node i in a unit time slot t; The objective function of the microgrid distributed resource trading model is: In the formula, p represents the real-time trading price of electricity, c represents the real-time trading price of computing resources, subscripts i and j represent microgrid nodes, and subscript g represents the main grid. This represents the amount of electricity purchased by microgrid node i from the P2P market. This represents the amount of electricity purchased by microgrid node i from the main grid. This represents the amount of electricity sold by microgrid node i to the P2P market per unit time slot t. This represents the amount of electricity sold by microgrid node i from the main grid per unit time slot t. The computational task assigned to microgrid node i by microgrid node j through a P2P market transaction within a unit time slot t is called the computational task. This represents the computing resources that microgrid node i purchases from the cloud data center per unit time slot t.

4. The microgrid distributed resource trading modeling device according to claim 3, characterized in that, The energy balance equation is as follows: In the formula, i and j represent microgrid nodes, and M represents all microgrid nodes. This represents the amount of electricity purchased by microgrid node i from node j through the P2P market. This represents the amount of electricity purchased by microgrid node i from the main grid. This represents the amount of electricity sold by microgrid node i to node j through the P2P market. This represents the amount of electricity sold by microgrid node i from the main grid. The dynamic energy consumption, The static energy consumption is... The operating energy consumption, The energy output of the renewable energy equipment. This refers to the discharge capacity of the battery energy storage system. The amount of charge applied to the battery energy storage system.

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