Energy consumption analysis method and system based on physical-digital space model of microgrid resources

Abstract

The present invention discloses an energy consumption analysis method and system based on a physical digital space model of microgrid resources. The method obtains the first monitoring data of distributed energy devices in the microgrid and the second monitoring data of power-consuming devices, and fuses these data to construct a physical digital space model of microgrid resources; constructs a blockchain-based distributed energy trading platform based on this model; and integrates an energy consumption traceability function in this trading platform to construct an energy consumption traceability tree model, and then calculates the energy consumption contribution degree of each distributed energy device and the energy consumption responsibility sharing ratio of each power-consuming device, generates an energy consumption traceability report and stores it on the chain for evidence. By constructing an energy consumption traceability tree model, the present invention can achieve refined tracing of microgrid energy flow and responsibility sharing, provide accurate data support for energy consumption analysis and management, and significantly improve the transparency and fairness of microgrid energy management.

Description

Technical Field

[0001] The present invention relates to the technical field of power energy consumption analysis, and more specifically, to an energy consumption analysis method and system based on a physical-digital space model of microgrid resources. Background Art

[0002] With the advancement of the construction of a new power system, the microgrid, as an autonomous power supply and consumption system integrating distributed renewable energy, energy storage systems, and flexible loads, has an increasingly complex internal energy flow, posing higher requirements for refined energy consumption management and fair trading mechanisms.

[0003] Chinese Patent with the authorization announcement number CN205231853U discloses a power energy consumption analysis system based on the Internet of Things. The system consists of a power energy consumption analyzer and a power energy consumption monitor. The power energy consumption analyzer includes a wireless communication module, an energy consumption analyzer, an energy-saving potential analyzer, and an economic analyzer. The power energy consumption monitor includes an energy-consuming equipment monitor, a power quality monitor, a wireless communication module, a microprocessor, and a power supply. This system can monitor energy-consuming equipment and power quality in real time and perform energy consumption analysis, energy-saving potential analysis, and economic analysis. However, this technical solution mainly focuses on enterprise-level power energy consumption analysis, targets traditional centralized power systems, lacks consideration of the complex relationships between distributed energy sources and devices within the microgrid, and is difficult to achieve refined management of each device within the microgrid. In addition, this technical solution does not utilize digital twin technology, cannot achieve real-time mapping and interaction between the physical world and the digital world, is difficult to perform effective predictive maintenance and optimization control, and cannot reflect the correlation between independent devices within the microgrid. Therefore, it cannot support energy trading based on the physical-digital space model of microgrid resources.

[0004] A Chinese patent with the publication number CN119010335A discloses a distributed microgrid control method, system, device, and storage medium. The method includes: performing a topology analysis on the physical structure and energy flow path of the distributed microgrid to establish a microgrid architecture including a control center and multiple control nodes; deploying acquisition and transmission devices for the microgrid architecture, establishing a communication network, and introducing a security mechanism; the control center distributes the data collected by the acquisition and transmission devices to the target control nodes based on the communication network and the security mechanism, monitors the energy consumption of the microgrid architecture in real time, and dynamically adjusts and controls the working states of various parts of the microgrid architecture. The system includes a demand analysis module, an architecture design module, a communication system design module, and an energy management and optimization algorithm design module. This technical solution has the characteristics of strong controllability and comprehensive analysis. However, this method mainly focuses on the control problem of the microgrid, lacks consideration of the energy trading link, and does not even involve the problem of energy consumption traceability in the trading process, and cannot stimulate the enthusiasm of all participants in the microgrid. In addition, this technical solution relies on a centralized control center, has a single point of failure risk, and it is difficult to ensure the security and transparency of data.

[0005] In summary, the prior art cannot achieve refined management and traceability of the internal energy trading of the microgrid. In particular, it is impossible to accurately calculate the energy consumption contribution degree of each distributed energy device and the energy consumption responsibility sharing ratio of each power-consuming device, and it is difficult to reflect fairness; the prior art lacks consideration of the complex relationships between the distributed energy and devices inside the microgrid, and it is difficult to achieve refined management of each device inside the microgrid. Summary of the Invention

[0006] To overcome the above defects of the prior art, the present invention provides an energy consumption analysis method and system based on a physical digital space model of microgrid resources. First, a digital twin model of the microgrid is constructed, and then a blockchain distributed energy trading platform is built based on this model, and an energy consumption traceability function is integrated. By constructing an energy consumption traceability tree model, refined traceability of the energy trading process is realized, and the energy consumption contribution degree and responsibility sharing ratio of each participant are calculated. Finally, an energy consumption traceability report is generated and stored on the chain for evidence, thereby improving the transparency, fairness, and traceability of microgrid energy management.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An energy consumption analysis method based on a physical digital space model of microgrid resources, including:

[0009] Obtain the first monitoring data of distributed energy devices in the microgrid and the second monitoring data of power-consuming devices, fuse the first monitoring data and the second monitoring data, and construct a physical digital space model of microgrid resources;

[0010] Based on the physical-digital space model of microgrid resources, construct a blockchain-based distributed energy trading platform;

[0011] Integrate the energy consumption traceability function in the distributed energy trading platform to construct an energy consumption traceability tree model; according to the energy consumption traceability tree model, calculate the energy consumption contribution degree of each distributed energy device and the energy consumption responsibility sharing ratio of each electricity-consuming device, generate an energy consumption traceability report, and store it on the blockchain for evidence.

[0012] Further, the first monitoring data includes the energy device ID and the energy device location information;

[0013] The second monitoring data includes the electricity-consuming device ID and the electricity-consuming device location information;

[0014] The construction of the physical-digital space model of microgrid resources includes:

[0015] Match the first monitoring data and the second monitoring data according to the energy device ID and the electricity-consuming device ID to form device panoramic data;

[0016] Establish the association relationship between device nodes according to the energy device location information and the electricity-consuming device location information to form device association data;

[0017] Based on the device panoramic data and the device association data, construct a physical-digital space model of microgrid resources, and the distributed energy device and the electricity-consuming device are device nodes in the physical-digital space model of microgrid resources.

[0018] Further, the construction of a blockchain-based distributed energy trading platform based on the physical-digital space model of microgrid resources includes:

[0019] Upload the first monitoring data and the second monitoring data in the physical-digital space model of microgrid resources to the blockchain;

[0020] Design a blockchain-based energy trading smart contract and deploy it to the blockchain network;

[0021] According to the first monitoring data and the second monitoring data, determine whether to trigger the energy trading smart contract. If triggered, carry out energy trading based on the physical-digital space model of microgrid resources according to the deployed energy trading smart contract; if not triggered, continue to monitor the operation status of the microgrid and wait for the satisfaction of the triggering condition.

[0022] Further, the design of the blockchain-based energy trading smart contract includes:

[0023] Design an energy trading algorithm and convert the energy trading algorithm into smart contract code; the energy trading algorithm includes a trading matching algorithm, a pricing algorithm, and a settlement algorithm;

[0024] Set the triggering conditions for the trading contract and deploy the smart contract code to the blockchain network.

[0025] Furthermore, the design method of the energy trading algorithm includes:

[0026] Determine the electricity sales demand information based on the first monitoring data; determine the electricity purchase demand information based on the second monitoring data;

[0027] Calculate the supply-demand matching degree based on the electricity sales demand information and the electricity purchase demand information;

[0028] Generate a supply-demand matching queue based on the supply-demand matching degree and the device association data, and automatically match and sort the supply and demand parties with higher rankings.

[0029] Furthermore, the calculation of the supply-demand matching degree based on the electricity sales demand information and the electricity purchase demand information includes: calculate the energy type matching degree, electricity quantity matching degree, price matching degree, and time window matching degree based on the electricity sales demand information and the electricity purchase demand information; calculate the supply-demand matching degree based on the energy type matching degree, electricity quantity matching degree, price matching degree, and time window matching degree;

[0030] The setting of the triggering conditions for the trading contract includes: setting the triggering conditions based on the trading time window, setting the triggering conditions based on the energy balance state, setting the triggering conditions based on the market electricity price fluctuations, and setting the triggering conditions based on major events.

[0031] Furthermore, the implementation of the energy trading based on the physical-digital space model of the microgrid resources includes:

[0032] Distributed energy devices transfer the electricity sales demand information to the energy trading smart contract through their device nodes in the physical-digital space model of the microgrid resources;

[0033] Electricity-consuming devices transfer the electricity purchase demand information to the energy trading smart contract through their device nodes in the physical-digital space model of the microgrid resources;

[0034] The energy trading smart contract deployed in the blockchain network automatically matches the received electricity sales demand information and electricity purchase demand information according to the preset energy trading algorithm to obtain the matching result;

[0035] The energy trading smart contract automatically executes the energy trading according to the matching result, conducts settlement, records each energy transaction, and generates a blockchain record.

[0036] Furthermore, the integration of the energy consumption traceability function in the distributed energy trading platform and the construction of the energy consumption traceability tree model include:

[0037] Extract the first energy consumption data of each energy transaction according to the blockchain record;

[0038] Access the physical-digital space model of the microgrid resources and extract the second energy consumption data related to each transaction;

[0039] Perform fusion analysis on the first energy consumption data and the second energy consumption data to construct an energy consumption traceability tree model.

[0040] Further, the construction of the energy consumption traceability tree model includes:

[0041] Based on the first energy consumption data in the blockchain record, construct a directed graph of energy transactions;

[0042] Take the second energy consumption data as attribute data and attach it to the corresponding nodes of the directed graph of energy transactions;

[0043] Obtain time series data, embed the time series data in the directed graph of energy transactions to form a spatio-temporal energy consumption traceability graph;

[0044] On the basis of the spatio-temporal energy consumption traceability graph, extract typical transaction patterns and construct an energy consumption traceability tree model.

[0045] An energy consumption analysis system based on the physical-digital space model of microgrid resources, which is used to implement the above-mentioned energy consumption analysis method based on the physical-digital space model of microgrid resources. The system includes:

[0046] Digital model construction module: used to obtain the first monitoring data of distributed energy equipment and the second monitoring data of electrical equipment in the microgrid, fuse the first monitoring data and the second monitoring data, and construct a physical-digital space model of microgrid resources;

[0047] Energy trading platform construction module: construct a blockchain-based distributed energy trading platform based on the physical-digital space model of microgrid resources;

[0048] Energy consumption analysis module: used to integrate the energy consumption traceability function in the distributed energy trading platform, construct an energy consumption traceability tree model; according to the energy consumption traceability tree model, calculate the energy consumption contribution degree of each distributed energy equipment and the energy consumption responsibility sharing ratio of each electrical equipment, generate an energy consumption traceability report, and store it on the chain for certification.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] By constructing a physical-digital space model of microgrid resources, comprehensive and real-time monitoring and digital modeling of distributed energy devices and electrical equipment within the microgrid are achieved. Such a digital twin model can intuitively display the operating status of the microgrid, energy flow, and interactions between devices, thus greatly improving the transparency and observability of microgrid operation and laying a foundation for refined management. The distributed energy trading platform based on blockchain uses smart contracts to automatically execute transaction matching, settlement, and recording, improving the efficiency of energy trading and reducing transaction costs. At the same time, the decentralization, tamper-proofing, and transparency of blockchain technology ensure the fairness and justice of the trading process and enhance the trust between trading parties. By integrating the energy consumption traceability function and constructing an energy consumption traceability tree model, the ins and outs of each energy transaction can be traced, and the energy consumption contribution of each distributed energy device and the proportion of energy consumption responsibility sharing of each electrical equipment can be accurately calculated. This refined energy consumption analysis method provides a scientific basis for optimizing energy allocation, formulating reasonable energy prices and incentive mechanisms, and promoting fair and reasonable energy responsibility sharing. Through the energy trading platform and energy consumption traceability mechanism, distributed energy, especially clean energy production and use, can be incentivized. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the 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.

[0052] Figure 1 It is the principle flow chart of the energy consumption analysis method based on the physical-digital space model of microgrid resources in the present invention;

[0053] Figure 2 It is the method flow chart of constructing the physical-digital space model of microgrid resources in the energy consumption analysis method based on the physical-digital space model of microgrid resources of the present invention;

[0054] Figure 3 It is the method flow chart of designing an energy trading smart contract based on blockchain in the energy consumption analysis method based on the physical-digital space model of microgrid resources of the present invention;

[0055] Figure 4 It is the method flow chart of designing a transaction matching algorithm in the energy consumption analysis method based on the physical-digital space model of microgrid resources of the present invention;

[0056] Figure 5 It is the method flow chart of setting transaction contract trigger conditions in the energy consumption analysis method based on the physical-digital space model of microgrid resources of the present invention;

[0057] Figure 6 This is the flowchart of the method for determining whether to trigger an energy trading smart contract in the energy consumption analysis method based on the physical-digital space model of microgrid resources of the present invention;

[0058] Figure 7 This is the flowchart of the method for conducting energy trading based on the physical-digital space model of microgrid resources in the energy consumption analysis method based on the physical-digital space model of microgrid resources of the present invention;

[0059] Figure 8 This is the flowchart of the method for constructing an energy consumption traceability tree model in the energy consumption analysis method based on the physical-digital space model of microgrid resources of the present invention;

[0060] Figure 9 This is the functional module diagram of the energy consumption analysis system based on the physical-digital space model of microgrid resources in the present invention. Detailed implementation manners

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] Application scenario of the present invention: The energy consumption analysis method based on the physical-digital space model of microgrid resources described in the present invention can be applied to intelligent microgrid systems including various distributed energy devices (such as photovoltaic, wind power, energy storage, etc.) and various types of electrical loads, such as industrial parks, commercial buildings, communities / residences, etc.

[0063] Embodiment 1

[0064] Please refer to Figure 1 As shown, this embodiment provides an energy consumption analysis method based on the physical-digital space model of microgrid resources, including:

[0065] Step S1000, obtaining first monitoring data of distributed energy devices in the microgrid and second monitoring data of electrical devices, and fusing the first monitoring data and the second monitoring data to construct a physical-digital space model of microgrid resources;

[0066] Further, step S1000 includes:

[0067] Step S1100, performing real-time monitoring on distributed energy devices in the microgrid to obtain first monitoring data; the first monitoring data includes energy device ID, device operation parameters, energy device energy consumption data, energy device environmental parameters, and energy device location information;

[0068] Specifically, the purpose of step S1100 is to comprehensively and real-time grasp the operating status and energy consumption of distributed energy devices in the microgrid, laying a data foundation for constructing a physical digital space model of microgrid resources. Distributed energy devices usually refer to small-scale power generation units located on the user side, such as photovoltaic power generation systems, small wind turbines, micro gas turbines, fuel cells, etc. These devices are important components for constructing an intelligent microgrid due to their proximity to the load, cleanliness, and environmental friendliness. Step S1100 conducts multi-dimensional and multi-granularity real-time monitoring of each distributed energy device.

[0069] The energy device ID refers to the unique identifier of each distributed energy device, such as the device number, serial number, etc., used to distinguish different devices. The device operating parameters refer to various parameters during the device operation, such as power generation power, rotational speed, voltage, current, etc., which reflect the real-time working status of the device. Install intelligent sensors on each distributed energy device to collect device operating parameters. For example, for a photovoltaic power generation system, sensors for measuring the output voltage, current, and power of the photovoltaic modules, as well as sensors for measuring the surface temperature, inclination angle, and azimuth angle of the photovoltaic panels, need to be installed. If the photovoltaic system has a maximum power point tracking (MPPT) function, the input and output voltage, current, and power of the MPPT also need to be monitored. For a small wind turbine, sensors for measuring the rotational speed, output voltage, current, and power of the wind turbine, as well as sensors for measuring the wind direction and wind speed, need to be installed. If the wind turbine has a yaw system, parameters such as the yaw angle also need to be monitored.

[0070] The energy device energy consumption data refers to the power generation, self-consumption, and grid-connected power in a period of time of the device, which reflect the energy production and consumption of the device. Install intelligent electricity meters at the grid connection points of each distributed energy device to collect the power generation, self-consumption, and grid-connected power. The power generation refers to the total electric energy produced by the distributed energy device in a period of time. The self-consumption refers to the part of the electric energy produced by the distributed energy device directly consumed by the local load. The grid-connected power refers to the part of the electric energy produced by the distributed energy device transmitted to the grid, and this part of the electric energy can participate in energy trading. The energy device environmental parameters refer to the temperature, humidity, wind speed, radiation intensity, etc. of the environment where the device is located, which will affect the operation efficiency and power generation of the device. Install environmental monitoring devices near each distributed energy device to collect the environmental parameters of the environment where the device is located, including temperature, humidity, wind speed, wind direction, radiation intensity, etc. The environmental temperature and humidity will affect the operation efficiency of the distributed energy device. For example, high temperature will reduce the power generation efficiency of the photovoltaic module, and high humidity may affect the insulation performance of electrical equipment. For a wind turbine, the wind speed and wind direction are the main factors affecting its power generation.

[0071] The location information of energy equipment refers to the geographical location information of the equipment, such as longitude, latitude, altitude, etc. These information are crucial for analyzing the spatial relationship between equipment and the energy flow path. Through GPS or other positioning technologies, the geographical location information of each distributed energy equipment, including longitude, latitude, altitude, etc., is obtained. These information are crucial for constructing a physical digital space model of microgrid resources and can be used to analyze the spatial relationship between equipment and the energy flow path. The collected data is transmitted to the data center for storage and processing in real time through wireless or wired communication methods.

[0072] By conducting multi-dimensional and multi-granularity real-time monitoring of distributed energy equipment, the operating status, energy consumption, and environmental parameters of each equipment can be comprehensively grasped, providing data support for subsequent analysis and optimization. The detailed monitoring data lays a solid foundation for constructing an accurate physical digital space model of microgrid resources, enabling the digital space model to more realistically reflect the microgrid status in the physical world. The combination of operating parameters and environmental parameters can be used for fault diagnosis and predictive maintenance, reducing downtime and improving the reliability of the system. For example, by monitoring the temperature and output power of photovoltaic panels, it is possible to determine whether there is dust occlusion or hot spot effect, and perform cleaning or maintenance in a timely manner.

[0073] Step S1200: Conduct real-time monitoring of the electrical equipment in the microgrid to obtain second monitoring data; the second monitoring data includes the electrical equipment ID, equipment operating status data, electrical equipment energy consumption data, equipment load data, electrical equipment location information, and electrical equipment environmental parameters.

[0074] Specifically, install intelligent sensors on the electrical equipment to collect equipment operating status data, including on / off status, working mode, load rate, etc. An intelligent sensor is a new type of sensor that integrates functions such as information collection, data processing, and wireless communication. It can real-time sense the operating status of the equipment and transmit the collected data to the monitoring center through a wireless network. By monitoring the on / off status of the equipment, the usage period and frequency of the equipment can be grasped; by monitoring the working mode, the operating conditions and energy efficiency level of the equipment can be understood; by monitoring the load rate, the utilization efficiency and energy-saving space of the equipment can be evaluated.

[0075] Collect the energy consumption data of the electrical equipment through an intelligent electricity meter, including power consumption, peak-valley electricity, power factor, etc. An intelligent electricity meter is a new type of electricity meter that integrates metering, monitoring, and two-way communication functions. It can record in detail the electrical energy consumption of the electrical equipment and support remote meter reading and demand-side management. The power consumption data reflects the total power consumption of the equipment over a period of time; the peak-valley electricity data reflects the electricity usage characteristics of the equipment at different times, which helps to formulate a peak-shaving electricity strategy; the power factor data reflects the reactive power situation of the equipment and is an important indicator for evaluating the power supply quality and equipment health level.

[0076] Collect the load data of electrical equipment through the demand response management system, including load prediction values, load scheduling plans, etc. Demand response is a demand-side management method that guides users to change their electricity consumption behaviors through price signals or incentives, which can alleviate the contradiction between supply and demand during peak grid hours and improve energy utilization efficiency. Load prediction is the basis of demand response. Through big data analysis and machine learning algorithms, the load change trend of electrical equipment in the future period can be accurately predicted; load scheduling is the core of demand response. According to the load prediction results and grid scheduling requirements, an optimal load control strategy is formulated to guide users to consume electricity during low-price periods and reduce unnecessary electricity consumption during peak periods.

[0077] By monitoring the operating status, energy consumption, and load of electrical equipment in real time, the electricity consumption demand and energy-saving potential on the user side can be accurately grasped, providing a decision-making basis for optimizing energy trading and energy consumption management. For example, an industrial park installs intelligent sensors and intelligent electricity meters on major production equipment to collect detailed operation and energy consumption data of the equipment; then, through the demand response management system, analyze the overall load characteristics of the park and predict the next-day load curve; according to the load prediction results, adjust the production plan to avoid peak grid hours, and through peak-shifting production and equipment scheduling, the park realizes "peak shaving and valley filling", saving more than 20% of the electricity bill expenditure every year. It can be seen that real-time monitoring of the operation data of electrical equipment is a key link in realizing refined energy management and demand response, which is of great significance for reducing energy consumption costs and improving grid efficiency.

[0078] Step S1300, fuse the first monitoring data and the second monitoring data to construct a cyber-physical space model of microgrid resources.

[0079] The cyber-physical space model (Cyber-Physical System, CPS) is an intelligent system that closely combines the physical world and the digital world. Through a large number of sensors and controllers, it realizes real-time perception, dynamic control, and deep integration of physical objects. In the microgrid scenario, distributed energy equipment and electrical equipment are physical components of the CPS system, while the first monitoring data and the second monitoring data are digital descriptions of these physical components. By fusing monitoring data from different sources and different dimensions, a CPS model that comprehensively reflects the actual operating status of the microgrid can be constructed in the digital space, that is, the cyber-physical space model of microgrid resources.

[0080] Furthermore, as Figure 2 shown, step S1300 includes:

[0081] Step S1310, match the first monitoring data and the second monitoring data according to the energy equipment ID and the electrical equipment ID to form equipment panoramic data;

[0082] Step S1320, establishing association relationships between device nodes based on energy device location information and power device location information to form device association data;

[0083] Step S1330, based on the equipment panoramic data and equipment association data, a physical digital space model of microgrid resources is constructed, and distributed energy equipment and power consumption equipment are device nodes in the physical digital space model of microgrid resources.

[0084] Specifically, equipment panoramic data refers to the aggregation of different monitoring data of the same equipment to form a complete, multi-dimensional equipment portrait. Since the first monitoring data and the second monitoring data come from the energy side and the user side respectively, they need to be matched and fused according to the unique equipment identifier (such as equipment ID). By cleaning, denoising and standardizing the monitoring data, the data quality is improved to ensure the consistency and reliability of the data. On the one hand, equipment panoramic data includes the static attributes of the equipment, such as equipment type, rated parameters, manufacturer, etc.; on the other hand, it includes the dynamic attributes of the equipment, such as the real-time operating status of the equipment, historical energy consumption data, health level, etc. Based on equipment panoramic data, the operating laws and energy efficiency characteristics of the equipment can be deeply analyzed, and data support can be provided for applications such as intelligent scheduling and predictive maintenance.

[0085] Device-associated data reflects the direction of energy flow and the properties of electrical connections in a microgrid. A microgrid is a small power system composed of distributed energy, energy storage devices, loads, and related control and protection equipment. Its physical structure has obvious network characteristics. By analyzing the location information of energy equipment and power-consuming equipment (such as longitude and latitude coordinates, substations, etc.), the topological relationship between device nodes can be established in the digital space to form a virtual "microgrid". In this virtual grid, distributed energy nodes serve as power sources, power-consuming equipment nodes serve as loads, and electric energy flows from source nodes to load nodes. At the same time, the physical connections between different devices, such as transformers and lines, will also be abstracted as logical links between nodes. The value of device-associated data lies in that it provides a networked perspective for subsequent energy optimization, fault location, and other analyses, making the overall operating status of the microgrid clear at a glance. For example, when a branch fails, it can quickly determine which device nodes will be affected and take emergency measures; in another example, in an energy trading scenario, the optimal path for electric energy from the source to the end point can be calculated and used as a reference for transaction pricing.

[0086] The physical-digital space model of microgrid resources realizes the deep integration of microgrid physical resources and digital resources through the digital twin of physical devices. This model uses the panoramic data of devices as nodes and the associated data of devices as edges, abstracts the key physical devices of the microgrid into individual digital twins, and then organizes these twins into a digital microgrid isomorphic to the physical microgrid. Each digital twin is a highly realistic mapping of a physical device. It receives the monitoring data transmitted back by the physical device in real time and dynamically simulates the behavioral characteristics of the device according to the embedded mechanism model and data model. The digital twins interact through data streams and energy flows to jointly form a "living" microgrid system. The advantage of the physical-digital space model of microgrid resources is that it provides a digital means for realizing the full-life cycle management of the microgrid. Before operation, the microgrid can be comprehensively simulated and verified in the digital space to optimize the design scheme; during operation, based on the real-time monitoring data, the safety and stability level of the microgrid can be dynamically evaluated to assist in energy scheduling decisions; after operation, the health degradation process of the device can be traced through the digital twin to formulate a device maintenance plan.

[0087] In summary, the physical-digital space model of microgrid resources constructs a dynamic mapping relationship between the physical entity and the digital virtual entity of the microgrid by integrating multi-source heterogeneous device monitoring data and introducing cutting-edge technologies such as digital twin and graph model, enabling the perception, analysis, decision-making, and control processes of the microgrid to be completed in the digital space, thereby maximizing the flexible, efficient, and intelligent advantages of the microgrid. This model can accurately depict the flow trajectory of energy in the microgrid and optimize the coordination of source-network-load-storage.

[0088] Step S2000: Based on the physical-digital space model of microgrid resources, construct a blockchain-based distributed energy trading platform;

[0089] Further, step S2000 includes:

[0090] Step S2100: Upload the first monitoring data and the second monitoring data in the physical-digital space model of microgrid resources to the blockchain;

[0091] Further, step S2100 includes:

[0092] Step S2110: Select a consortium blockchain as the underlying architecture of the blockchain, set each entity within the microgrid as a consortium member, and allocate nodes to the consortium members;

[0093] Step S2120: Format the first monitoring data and the second monitoring data into transaction events on the blockchain and upload them to the blockchain nodes;

[0094] Step S2130: Each blockchain node verifies and confirms the transaction events through a consensus mechanism.

[0095] Specifically, the process of uploading the monitoring data in step S2100 refers to using blockchain technology to store the real-time operation data of distributed energy devices and power consumption devices in a blockchain network in an encrypted and tamper-proof manner, so as to achieve secure data sharing and trusted exchange. Blockchain is a decentralized distributed ledger technology. Through cryptographic principles and consensus mechanisms, data consistency is achieved among multiple participating nodes, ensuring the authenticity and immutability of data. Uploading the first monitoring data (energy device ID, device operation parameters, energy device energy consumption data, energy device environmental parameters, and energy device location information) and the second monitoring data (power consumption device ID, device operation status data, power consumption device energy consumption data, device load data, power consumption device location information, and power consumption device environmental parameters) in the physical digital space model of the microgrid resources to the blockchain can provide a trusted data basis for subsequent energy trading and energy consumption analysis.

[0096] A consortium blockchain is a semi-decentralized form of blockchain, lying between a fully open public blockchain and a fully closed private blockchain. In a consortium blockchain, the joining of nodes requires the permission and authorization of consortium members, so the consortium blockchain takes into account both decentralization and controllability. Choosing a consortium blockchain as the underlying architecture of the energy blockchain can achieve mutual trust and collaboration among different stakeholders while ensuring data privacy. In the microgrid scenario, microgrid operators, distributed energy owners, electricity retailers, grid companies, etc. can be regarded as consortium members, and each member is assigned a blockchain node. A node is the basic unit of the consortium blockchain network, with functions such as ledger storage, smart contract execution, and transaction verification. By reasonably setting node permissions and consensus rules, an efficient, reliable, and scalable energy blockchain network can be formed. Adopting the consortium blockchain architecture can achieve trusted data sharing and transaction matching while meeting the privacy protection needs of all parties in the microgrid. For example, distributed energy owners and users can authorize microgrid operators to view some of their data, but not disclose it to grid companies and electricity retailers; while grid companies can obtain the aggregated data provided by microgrid operators, but cannot obtain the privacy data of individual users. By flexibly configuring node permissions, a multi-level and differentiated data sharing mechanism can be built on the basis of a trusted consortium blockchain, effectively balancing the relationship between data utilization and privacy protection.

[0097] The basic data unit of the blockchain is the "transaction", and each transaction will be recorded in a block. Multiple blocks are concatenated into a chain in chronological order, so it is called the "blockchain". Formatting the monitoring data in the physical digital space model of microgrid resources into blockchain transactions means encoding and encapsulating this data in the data manner of the blockchain network to generate standardized transaction events. For example, a set of monitoring data of energy devices can be packaged into a single transaction, and information such as the transaction timestamp, transaction content, and transaction ID can be written into the transaction body; or the cumulative data over a period of time can be used as a single transaction to reduce the frequency of data being uploaded to the blockchain. The generated transaction events are broadcast to each node through the P2P network of the blockchain and wait for the nodes in the network to verify and confirm them.

[0098] The consensus mechanism is one of the core technologies of the blockchain and is used to ensure that all nodes in a distributed network reach an agreement on the verification results of transactions. Through the consensus mechanism, malicious nodes in the network can be resisted, and security issues such as data tampering and double spending can be prevented. In the energy blockchain, consensus algorithms suitable for consortium blockchains, such as PBFT (Practical Byzantine Fault Tolerance), RAFT, etc., can be adopted. These consensus algorithms achieve fast, efficient, and secure transaction confirmation through multiple rounds of voting and message passing. Each blockchain node verifies the received transaction events by executing the consensus protocol, checking the legality, integrity, and consistency of the transactions. The transactions that pass the verification are packaged into a new block and linked to the existing blockchain, forming an immutable and traceable data chain. The transactions that do not pass the verification are discarded and not written into the blockchain.

[0099] Adopting blockchain technology for data uploading to the blockchain and consensus verification can significantly improve the security and reliability of microgrid energy data. In the physical space, the geographical locations of distributed energy devices and power-consuming devices are relatively dispersed, and the collection, transmission, and storage of data face risks such as network failures, equipment failures, and data loss. By uploading data to the blockchain, the advantages of distributed storage of the blockchain can be utilized to store data copies on multiple nodes to avoid data loss caused by single-point failures; the anti-tampering mechanism of the blockchain can be used to ensure the authenticity and integrity of the data after it is uploaded to the blockchain, providing reliable data support for energy transactions; the traceability of the blockchain can be used to record the whole process of data generation, transfer, and transaction, realizing the auditability and traceability of energy consumption data. The blockchain effectively integrates and protects the scattered physical space data in the digital space, laying a solid data foundation for the energy Internet.

[0100] Step S2200, design an energy trading smart contract based on the blockchain and deploy it to the blockchain network; the energy trading smart contract includes a power purchase contract and a power sales contract;

[0101] Further, as Figure 3 shown, step S2200 includes:

[0102] Step S2210, designing an energy trading algorithm and converting the energy trading algorithm into smart contract code; the energy trading algorithm includes a transaction matching algorithm, a pricing algorithm, and a settlement algorithm;

[0103] Further, step S2210 includes:

[0104] Step S2211, designing a transaction matching algorithm;

[0105] Specifically, transaction matching is a key link in energy trading. The purpose is to automatically match the optimal trading partners between energy supply and demand sides, and achieve a rapid match of trading intentions and trading prices. By collecting and analyzing real-time energy supply and demand information, the transaction matching algorithm can accurately depict the intentions and demands of both trading parties, and calculate the supply-demand matching degree accordingly. The supply-demand matching degree is a quantitative indicator to measure the consistency of the intentions of both trading parties, comprehensively considering the matching degrees in multiple dimensions such as energy variety, trading power, trading price, and trading time. After calculating the supply-demand matching degree, the transaction matching algorithm ranks the trading applications according to the level of the matching degree. The higher the matching degree, the higher the trading priority, thus forming a supply-demand matching queue. The transaction matching algorithm automatically matches the supply and demand sides in the matching queue from high to low priority until a transaction is reached or the matching termination condition is met.

[0106] Further, as Figure 4 shown, step S2211 includes:

[0107] Step S22111, determining the electricity sales demand information according to the first monitoring data; determining the electricity purchase demand information according to the second monitoring data;

[0108] Specifically, the electricity sales demand information is the electricity sales application submitted by distributed energy sources (such as photovoltaic power plants, energy storage power plants, etc.), including the energy equipment ID, the electricity volume available for sale, the expected price, the tradable time window, and other contents. These information mainly come from the first monitoring data, that is, the real-time monitoring data of distributed energy equipment. By analyzing data such as the power generation power, energy storage capacity, and self-consumption of distributed energy equipment, the electricity volume available for sale and trading willingness can be predicted. For example, when the power generation of a photovoltaic power plant continuously exceeds its own load, it indicates that there is a demand for electricity sales; when the state of charge (SOC) of an energy storage power plant approaches full capacity, it may also trigger the demand for electricity sales. At the same time, the electricity sales willingness of distributed energy is also affected by factors such as electricity price policies, subsidy mechanisms, and market conditions. Therefore, it is necessary to dynamically determine the electricity sales demand information by combining the equipment operation parameters and energy equipment energy consumption data.

[0109] The electricity purchase demand information is the electricity purchase application submitted by electricity-consuming equipment (such as industrial parks, commercial buildings, residential communities, etc.), including the electricity-consuming equipment ID, the electricity volume to be purchased, the acceptable price, the tradable time window, and other contents. These information mainly come from the second monitoring data, that is, the real-time monitoring data of electricity-consuming equipment. By analyzing data such as the operation status, load forecast, and electricity bill budget of electricity-consuming equipment, the electricity purchase demand and trading willingness can be predicted. For example, when the production load of an industrial park continuously increases and it is predicted that the future electricity price will rise, the electricity purchase demand may be triggered in advance to lock in a relatively low electricity purchase cost; when the electricity load of a commercial building shows obvious peak characteristics and the electricity bill budget is tight, it may choose to purchase electricity during the low electricity price period to avoid the cost expenditure during the peak period. Therefore, it is necessary to comprehensively consider the equipment operation status data, electricity-consuming equipment energy consumption data, and equipment load data to accurately depict and predict the electricity purchase demand information.

[0110] Step S22112, calculate the supply-demand matching degree based on the electricity sales demand information and the electricity purchase demand information;

[0111] The calculation of the supply-demand matching degree includes: calculating the energy type matching degree, electricity volume matching degree, price matching degree, and time window matching degree according to the electricity sales demand information and the electricity purchase demand information; calculating the supply-demand matching degree according to the energy type matching degree, electricity volume matching degree, price matching degree, and time window matching degree.

[0112] Specifically, the calculation of the supply-demand matching degree requires multi-dimensional comparative analysis of the electricity sales demand information and the electricity purchase demand information, which specifically includes the following steps:

[0113] 1) Calculate the energy type matching degree. Compare the energy varieties (such as photovoltaic, wind power, hydropower, etc.) in the electricity sales demand information with the electricity consumption types (such as general industrial and commercial, large industry, agriculture, etc.) in the electricity purchase demand information, and calculate the adaptability of the energy varieties. For example, photovoltaic power generation is more suitable for electricity consumption types with a high daytime load ratio, while wind power is more suitable for electricity consumption types with a high nighttime load ratio.

[0114] 2) Calculate the electricity quantity matching degree. Compare the electricity quantity available for sale in the electricity sales demand information with the electricity quantity to be purchased in the electricity purchase demand information, and calculate the matching degree of the electricity quantity. If the electricity sales quantity is equal to the electricity purchase quantity, the matching degree is the highest; if the electricity sales quantity is greater than the electricity purchase quantity, the remaining electricity quantity needs to be re-matched; if the electricity sales quantity is less than the electricity purchase quantity, the electricity quantity gap needs to be supplemented from other electricity sellers.

[0115] 3) Calculate the price matching degree. Compare the expected selling price in the electricity sales demand information with the acceptable price in the electricity purchase demand information, and calculate the matching degree of the price. Adopt an appropriate price matching model, considering factors such as electricity price policies, market average prices, and bargaining spaces, to form a dynamic price matching curve. The faster the curve converges, the higher the price matching degree.

[0116] 4) Calculate the time window matching degree. Compare the tradable time windows in the electricity sales demand information and the electricity purchase demand information, and calculate the overlapping degree of the time windows. The higher the overlapping degree of the time windows, the higher the matching degree of supply and demand in the time dimension. Factors such as the start time, duration, trading frequency, and emergencies of the time window need to be considered.

[0117] 5) Calculate the supply-demand matching degree. After calculating the energy type matching degree, electricity quantity matching degree, price matching degree, and time window matching degree, use the method of weighted average or geometric average to form a supply-demand matching degree index. The matching degree index quantifies the degree of fit between electricity sales demand and electricity purchase demand in multiple dimensions, providing an intuitive reference for transaction matchmaking.

[0118] By calculating and comparing the supply-demand matching degree, the transaction matchmaking algorithm can quickly lock in the optimal trading partners from a large number of electricity sales demands and electricity purchase demands, achieving efficient and accurate energy trading matching. Compared with manual matchmaking, algorithmic matchmaking can achieve global optimization on a larger scale, significantly improving transaction efficiency and success rate. On the one hand, both the supply and demand sides can reach a transaction intention more quickly, reducing repeated bargaining and waiting time, and improving energy consumption and utilization efficiency; on the other hand, grid dispatching can more precisely balance the surplus and shortage of energy, reducing resource waste problems such as curtailment of wind and solar power, and improving energy utilization efficiency. In addition, by improving the matching algorithm and bargaining mechanism, it is also possible to dynamically optimize transaction profits on the basis of transaction completion, enabling all parties to reasonably share the dividends of market-based pricing and stimulating the endogenous motivation of market players to participate in energy transactions.

[0119] Step S22113: Generate a supply-demand matching queue based on the supply-demand matching degree and equipment association data, and automatically match and sort the top-ranked supply and demand parties.

[0120] Specifically, according to the level of supply-demand matching degree, arrange the power sales demand and power purchase demand in descending order to form a supply-demand matching priority queue. The power sellers and power buyers ranked at the front of the queue have priority to obtain the opportunities of automatic matching and transaction execution. However, due to the transmission loss of electric energy in the physical space, directly sorting according to the matching degree may lead to long-distance cross-regional transmission of electricity, increasing unnecessary network losses. Therefore, when generating the supply-demand matching queue, it is also necessary to consider the distance factor reflected by the equipment association data. The equipment association data includes the location coordinate information of energy equipment and power-consuming equipment, as well as the topological connection relationship between the equipment. By analyzing the association data, the electrical distance between the power seller and the power buyer can be calculated, and then the matching queue can be optimized.

[0121] The optimized matching rule is: on the premise of meeting the supply-demand matching degree, give priority to matching the power seller and the power buyer with the closest electrical distance for transactions with the same matching degree. This can achieve "proximity balance" in the physical space and minimize the transmission loss of electric energy. After generating the optimized supply-demand matching queue, the transaction matching algorithm starts from the head of the queue and automatically matches the supply and demand parties in descending order of priority. When the matching is successful, promptly send the completed transaction request to the smart contract, and the contract automatically executes the subsequent transaction settlement and energy scheduling. When the matching fails (such as the electricity quantity cannot be met, the price cannot reach an agreement, the time window cannot be aligned, etc.), the transaction matching algorithm continues to match the sub-optimal transactions in the queue until the supply and demand are balanced or the market clearing time limit is reached.

[0122] The optimized matching rule is innovative and practical in the field of energy trading. The traditional power trading and matching mechanism mainly focuses on the matching of trading electricity prices and quantities, and rarely considers the efficiency problem of electric energy transmission in the physical power grid. This mechanism is acceptable in large-scale power markets across regions and provinces, but in the microgrid scenario dominated by distributed energy, it is prone to the deviation between physical transmission electricity quantity and logical trading volume. By integrating equipment association data and innovatively introducing electrical distance as a matching constraint condition, it can minimize the dependence of the microgrid on the external power grid and improve the supply-demand balance ability of the microgrid itself. At the same time, proximity matching and proximity trading can maximize the advantage of local consumption of distributed energy, reduce the losses in the processes of boosting voltage, transmitting electricity, and reducing voltage of electric energy, and improve the overall energy utilization efficiency of the microgrid. In addition, by shortening the transmission radius of electric energy, it is beneficial to relieve the power flow pressure of the distribution network, delay the update and iteration cycle of distribution facilities, thereby reducing the construction and operation and maintenance costs of the power grid and improving the economy and sustainability of the microgrid.

[0123] Step S2212: Design the pricing algorithm and the settlement algorithm;

[0124] Specifically, the pricing algorithm and the settlement algorithm are the core components for constructing the energy trading smart contract, which are used to determine the energy trading price and process transaction clearing respectively.

[0125] When designing the pricing algorithm, factors such as the energy supply and demand situation, the grid electricity price policy, and the market competition pattern need to be comprehensively considered to form a dynamic and real-time pricing mechanism. A feasible pricing algorithm is the bilateral bidding mechanism based on the market clearing principle. Under this mechanism, the electricity seller and the electricity buyer submit their respective bids and electricity quantities. The trading system sorts and matches the buy and sell declarations according to the principles of price priority and time priority to form a supply-demand intersection point and determine the unified clearing price. The clearing price refers to the equilibrium price that can enable the largest trading electricity quantity in the market to be transacted. By adopting the market clearing mechanism, the market supply and demand information can be effectively centralized, the market price can be discovered, and the market efficiency can be improved. At the same time, pricing mechanisms such as peak-valley electricity prices and time-of-use electricity prices can also be designed to guide users to use electricity during off-peak hours and smooth the load curve.

[0126] When designing the settlement algorithm, the principles of fairness, transparency, and credibility need to be followed to ensure the accurate transfer of funds and electricity quantities for all trading parties. A feasible settlement algorithm is the automated settlement based on the smart contract. When the transaction is successfully matched, the transaction results are written into the electricity purchase contract and the electricity sales contract, and the contract automatically triggers the transfer of funds and electricity quantities. The payment funds of the electricity buyer are automatically transferred to the account of the electricity seller through a third-party payment channel or an e-wallet; the electricity sales quantity of the electricity seller is automatically transferred to the electricity buyer through the energy router or the distribution network. At the same time, the trading system also needs to design a reconciliation mechanism for funds and electricity quantities to ensure that the transaction results are consistent with the contract execution. If there is a mismatch in funds or electricity quantities, an exception handling procedure needs to be started in a timely manner to trace and correct the transaction.

[0127] Adopting a market-based pricing mechanism and an automated settlement mechanism can significantly improve the efficiency and credibility of energy trading. The traditional energy trading adopts a "one-on-one" negotiated pricing model, where the trading price lacks a unified standard and market benchmark and is easily affected by factors such as information asymmetry and bargaining power, resulting in low pricing efficiency. After introducing the market clearing mechanism, the supply and demand preferences can be maximally matched, and the dynamic optimization of the trading electricity quantity and the trading price can be realized, enabling the market to play a decisive role in resource allocation. At the same time, using smart contracts to replace manual settlement can reduce the time cost of transaction reconciliation and clearing and improve the capital turnover efficiency. And placing the settlement process on the blockchain can achieve the transparency and traceability of the entire transaction process, enabling all trading parties to form a game relationship of mutual restraint and mutual trust, effectively reducing the default risk and credit risk.

[0128] Step S2213: Convert the transaction matching algorithm, pricing algorithm, and settlement algorithm into smart contract code.

[0129] Specifically, to enable the automatic execution of transaction matching, pricing, and settlement algorithms in the blockchain network, these algorithms need to be converted into smart contract code. A smart contract is a self-executing code based on the blockchain, triggered by events, and automatically performs corresponding operations according to preset conditions. In the energy trading scenario, a series of modular and parameterized smart contracts can be designed for different trading stages and events, such as the matching declaration contract, pricing auction contract, fund settlement contract, power transfer contract, etc.

[0130] When designing the contract code, it is necessary to clarify the roles, responsibilities, and interaction processes of all parties in the transaction and map them to the state variables, function methods, and event handling logic of the contract. For example, in the matching declaration contract, it is necessary to define the quotations and power quantities of the power seller and the power buyer and design functions such as quotation sorting and power quantity matching; in the pricing auction contract, it is necessary to design operations such as inquiry, quotation, order cancellation, and transaction, and embed the market clearing algorithm; in the settlement contract, it is necessary to connect to the blockchain wallet and the energy router, call the transfer interface and the power quantity collection interface, and design balance reconciliation and dispute arbitration clauses. At the same time, it is also necessary to strictly test the security, availability, and concurrency of the contract to ensure the stable operation of the contract code in extreme scenarios such as high-frequency trading, large-scale settlement, and abnormal quotations.

[0131] Embedding transaction rules and business logics in the blockchain in the form of code is an innovative solution to solve the pain points of energy trading. The traditional energy trading process relies on centralized transaction matching, pricing, and settlement systems, which are prone to risks and hidden dangers such as single-point failures and data tampering, and the system upgrade and rule adjustment responses are not timely, making it difficult to meet the requirements of the rapidly changing energy market. By moving the entire transaction process onto the chain through smart contracts, the automatic execution of transaction agreements can be realized, reducing human intervention and operation errors. Utilizing the anti-tampering and traceable characteristics of the blockchain can ensure the objectivity and fairness of transaction data and transaction rules, making energy trading more standardized and normalized. With the help of the distributed architecture of the blockchain, the dynamic expansion and elastic upgrade of the trading system can be achieved, quickly responding to the personalized needs of the energy market and trading entities.

[0132] Step S2220: Set the triggering conditions for the trading contract and deploy the smart contract code to the blockchain network.

[0133] Specifically, setting the trigger conditions for the trading contract means clarifying under what circumstances the energy trading process is initiated and the execution of the smart contract is automatically triggered. The trigger conditions can be set based on multiple dimensions such as the energy balance status, market price fluctuations, and equipment maintenance plans to form an all-round trading trigger mechanism. When the trigger conditions are met, the smart contract code is automatically deployed to the blockchain network, and each node competes for the right to execute. The trading result is reached through the consensus mechanism. Deploying the smart contract code to the blockchain can ensure that the trading rules are verified and trusted by all parties, enabling energy trading to be carried out in a fair, just, and open environment.

[0134] Further, as Figure 5 shown, step S2220 includes:

[0135] Step S2221, setting the trigger condition based on the trading time window;

[0136] Further, step S2221 includes:

[0137] Step S22211, setting the open time period for energy trading;

[0138] Step S22212, automatically triggering the energy trading smart contract during the open time period for energy trading.

[0139] Specifically, the trading time window refers to the time period during which energy trading is allowed. The opening time period of energy trading can be preset according to grid dispatching requirements, market trading habits, user energy consumption characteristics, etc.; the setting of the opening time period of energy trading needs to take into account multiple factors. Generally, the time periods with low grid load and high new energy output can be selected as the trading window, such as 9:00 - 11:00 and 14:00 - 16:00 every day. Opening trading during the low-load period can encourage distributed power sources to participate in grid peak shaving and alleviate the problems of curtailment of wind and solar power; opening trading during the period rich in new energy can guide the local consumption of distributed power sources and reduce long-distance power transmission losses. Of course, the setting of the trading time window also needs to consider factors such as the trading frequency, trading volume, and trading varieties of the power market, as well as the connection and coordination with the conventional power market and the cross-provincial and cross-regional market, and design differentiated trading windows. Once the time enters the opening time period of energy trading, the smart contract automatically enters the "running" state. The power purchaser and the power seller can submit trading applications and quoted electricity volumes to the smart contract. After receiving the trading application, the smart contract immediately starts the trading matching algorithm to match the received power purchase applications and power sale applications until a transaction is reached or the trading period ends. During the opening time period of energy trading, the smart contract keeps running continuously and responds to new trading requests at any time. While during the non-trading period, the smart contract enters the "sleep" state, closes the external interface, and no longer accepts new trading applications, and is awakened again when the next trading window arrives. By setting the window trigger mechanism, energy trading can be carried out automatically and intelligently at specific times, reducing trading time and labor costs.

[0140] Step S2222, set the trigger condition based on the energy balance state;

[0141] Furthermore, step S2222 includes:

[0142] Step S22221, calculate the energy surplus and deficit value according to the power generation power of the distributed energy device and the power consumption power of the power consumption device;

[0143] Step S22222, when the energy surplus and deficit value exceeds the first threshold, trigger the power sale contract; when the energy surplus and deficit value is lower than the second threshold, trigger the power purchase contract.

[0144] Specifically, in the actual operation of the power grid, the situation of power supply and demand imbalance often occurs, manifested as over-supply or under-supply of electric power. The imbalance between supply and demand will have a greater impact on key indicators such as the voltage and frequency of the power grid, threatening the safe and stable operation of the power grid. Therefore, it is necessary to adjust the power grid operation mode in a timely manner according to the energy balance state, trigger energy trading, and guide the flexible interaction of the power source, grid, load, and energy storage.

[0145] By comparing and analyzing the real-time power generation of distributed energy devices and the actual power consumption of electrical equipment in the physical-digital space model of microgrid resources, the energy surplus and deficit values of the microgrid at different times can be calculated. When the power generation is greater than the power consumption, the energy surplus and deficit value is positive, indicating that the microgrid is in a power surplus state and needs to release the surplus power through power sales; when the power generation is less than the power consumption, the energy surplus and deficit value is negative, indicating that the microgrid is in a power shortage state and needs to purchase power to supplement the power gap. By analyzing the energy surplus and deficit trend, the power supply and demand situation of the microgrid in the future period can be accurately predicted, and the power scheduling preparation can be made in advance.

[0146] Setting a threshold trigger line for the energy surplus and deficit value can achieve the automation of power grid energy regulation. For example, set the first threshold (such as 20%) and the second threshold (such as -10%) of the energy surplus and deficit value. When the energy surplus ratio exceeds 20%, it indicates that the power generation output of the microgrid is sufficient and power needs to be sold externally. At this time, the power sales contract is automatically triggered to provide power support to other power-deficient microgrids or the large power grid; when the energy shortage ratio exceeds 10%, it indicates that the power consumption demand of the microgrid is high and power needs to be purchased from outside. At this time, the power purchase contract is automatically triggered to purchase power from the neighboring microgrid or the large power grid to ensure the power supply quality of users. Through the threshold trigger mechanism, the human intervention in power grid scheduling can be reduced, and the timeliness and effectiveness of energy and power allocation can be improved.

[0147] Step S2223, set the trigger condition based on the market electricity price fluctuation;

[0148] The trigger condition based on the market electricity price fluctuation is:

[0149] When the grid electricity price is lower than the first electricity price threshold, the power purchase contract is triggered; when the grid electricity price is higher than the second electricity price threshold, the power sales contract is triggered.

[0150] Specifically, in an open electricity market environment, the electricity price is an important factor affecting the willingness of market players to purchase and sell electricity. When the market electricity price fluctuates violently, the price signal can be used to guide users to use electricity rationally and promote the balance of power supply and demand. Therefore, it is necessary to set a trading trigger mechanism based on the market electricity price fluctuation. When the grid electricity price is lower than a certain electricity price threshold (such as 0.3 yuan / kWh), it indicates that the current electricity price is at a phased low point and the power purchase cost is relatively low. At this time, the power purchase contract can be triggered to encourage users to increase power consumption or cut peaks and fill valleys, which is equivalent to "giving benefits" to users; when the grid electricity price is higher than a certain electricity price threshold (such as 0.6 yuan / kWh), it indicates that the current power purchase cost is relatively high and the grid has peak-period power purchase pressure. At this time, the power sales contract can be triggered to encourage distributed power sources to generate more electricity and feed it into the grid to "reduce the burden" on the grid. Through the price trigger mechanism, the enthusiasm of market players to participate in grid regulation can be mobilized, and a market-based power demand side response can be formed.

[0151] Step S2224, set the trigger conditions based on major events.

[0152] Specifically, during the operation of the power grid, some major events often occur, such as power grid maintenance, equipment failures, extreme weather, etc. These events often have a great impact on the power supply capacity and load trend of the power grid. Therefore, it is necessary to adjust the energy trading strategy in a timely manner according to the impact degree and duration of major events to improve the resilience and flexibility of the power grid. For example, when it is known that a typhoon will land in the next week, the power purchase contract can be triggered in advance to increase the energy storage capacity of the microgrid and improve the disaster prevention and resistance ability; when the routine maintenance of the power grid is about to start, the power sales contract can be triggered in advance to sell the distributed electricity that may be idle during the maintenance period and reduce the waste of electricity. Through the major event trigger mechanism, the emergency regulation ability of the power grid can be improved by using energy trading, and the flexible advantages of distributed energy can be maximally exerted.

[0153] By setting all-round energy trading trigger conditions, the real-time dynamic matching between distributed energy and grid demand can be achieved, the regulation potential of distributed energy can be maximally exerted, and the economy and security of power grid operation can be improved. The traditional energy trading mode often adopts the trading time sequence of "day-ahead + intra-day", with low trading frequency and trading flexibility, and often misses the best time window for grid regulation. After adopting the trigger mechanism based on time, energy balance, electricity price fluctuation and major events, energy trading can be continuously optimized in both time and space dimensions, making distributed energy truly a powerful tool for flexible grid regulation. At the same time, writing the trading trigger conditions clearly into the smart contract can reduce the subjective judgment and human intervention in the trigger process, make energy trading return to the physical essence of electricity, and improve the accuracy and controllability of grid regulation. In addition, with the help of the automatic execution and supervision functions of the smart contract, it can be ensured that the trigger rules are fairly executed, avoiding the abuse of grid dispatching power and safeguarding the legitimate rights and interests of all parties.

[0154] Step S2300, according to the first monitoring data and the second monitoring data, judge whether to trigger the energy trading smart contract. If triggered, carry out energy trading based on the physical-digital space model of microgrid resources according to the deployed energy trading smart contract; if not triggered, continue to monitor the operation status of the microgrid and wait for the trigger conditions to be met.

[0155] Furthermore, step S2300 includes:

[0156] Step S2310, according to the first monitoring data, the second monitoring data and the preset trading contract trigger conditions, judge whether to trigger the energy trading smart contract;

[0157] Specifically, the triggering of energy trading smart contracts is a complex decision-making process based on the fusion analysis of multi-source data. Triggering judgments need to comprehensively consider the real-time operating states of the energy device side and the electricity-consuming device side, as well as the market signals and dispatching instructions of the external power grid. The first monitoring data reflects the power generation capacity and willingness of distributed energy devices, which is the key basis for triggering electricity sales contracts; the second monitoring data reflects the load demand and electricity consumption willingness of electricity-consuming devices, which is the key basis for triggering electricity purchase contracts; the preset triggering conditions for trading contracts provide a "rule library" for triggering transactions, clarifying the specific rules and thresholds for triggering transactions in different scenarios. By comparing the three types of data in real time and making logical judgments, the current energy supply and demand situation and trading opportunities can be automatically identified, and then the corresponding electricity purchase or sales contracts can be triggered.

[0158] By fusing multi-source heterogeneous data for energy trading trigger judgment, more comprehensive, accurate, and rapid trading decisions can be achieved. Compared with single-data triggering, multi-source data fusion can conduct a three-dimensional analysis of the energy trading situation from multiple perspectives such as the supply side, demand side, and market side, overcome the problems of data dispersion and information asymmetry, and reduce the blind spots and misjudgments in trigger decisions. For example, triggering an electricity sales contract only based on the power generation data of distributed energy may ignore the actual electricity consumption demand on the user side, resulting in the transaction being "detached from reality and becoming virtual"; while triggering an electricity purchase contract only based on the load data of electricity-consuming devices may not be able to consider the overall supply and demand balance and trading price situation of the power grid, causing an "information island". By organically integrating the data on the energy side, electricity consumption side, and market side and making trigger judgments based on this, the "supply-demand coordination" and "source-network-load-storage" integration of energy trading triggers can be technically achieved, and then the systematic optimization of the entire energy trading process can be leveraged.

[0159] Furthermore, as Figure 6 shown, step S2310 includes:

[0160] Step S2311, obtain the real-time time, and judge whether the triggering condition based on the trading time window is met;

[0161] Step S2312, obtain the power generation power of the distributed energy device in the first monitoring data and the power consumption power of the electricity-consuming device in the second monitoring data, calculate the real-time energy profit and loss value, and judge whether the triggering condition based on the energy balance state is met;

[0162] Step S2313, obtain the real-time grid electricity price, and judge whether the triggering condition based on the market electricity price fluctuation is met;

[0163] Step S2314, obtain the major event information, and judge whether the triggering condition based on the major event is met;

[0164] Step S2315: If any of the conditions in the above steps S2311 - S2314 is satisfied, it is determined that the energy trading smart contract is triggered.

[0165] Specifically, the trading time window trigger condition is a trading rule based on the time dimension. By presetting the permitted time periods for energy trading (such as 9:00 - 11:00 and 14:00 - 16:00 every day), within the specified trading window period, the current time is automatically obtained to determine whether it is within the permitted trading time period. If the current time belongs to the trading period and other necessary trading conditions are met (such as grid security check, power quantity threshold, etc.), the energy trading smart contract is automatically triggered, allowing market entities to submit trading applications; if the current time does not belong to the trading period or other trading conditions are not met, the trading contract is not triggered temporarily, and trading applications are not accepted. By managing the energy trading time in a windowed manner, it is possible to reserve a time window for distributed energy to participate in cross - regional and cross - provincial transactions while ensuring the grid dispatching control force.

[0166] The trading trigger based on the energy balance state is a commonly used trigger method. The power generation power data of distributed energy devices and the power consumption power data of power consumption devices are obtained in real time, and an energy profit and loss comparison analysis is carried out. When the energy generation is greater than the power consumption, the energy profit and loss value is positive, indicating an excess of energy supply. At this time, the distributed energy power selling contract can be triggered; conversely, when the power consumption is greater than the energy generation, the energy profit and loss value is negative, indicating a shortage of energy supply. At this time, the distributed energy power purchasing contract can be triggered. By setting reasonable trigger thresholds for the energy profit and loss value (such as 20% and - 10%) and conducting real - time monitoring and threshold comparison, the trigger timing of energy trading can be automatically judged, guiding distributed energy to flexibly participate in the real - time balance of the power grid.

[0167] The economic efficiency of grid operation is an important consideration factor for energy trading. Connect to the external power market and obtain the grid electricity price signal in real time, such as the synchronous electricity price of the Beijing - Tianjin - Tang grid or the cross - regional electricity price of neighboring provinces, and judge the market trading opportunities based on this. When the grid electricity price is lower than a preset threshold (such as 0.25 yuan / kWh), the distributed energy power purchasing contract can be triggered to encourage distributed energy to purchase electricity at a low price and "reduce the burden" on the grid; when the grid electricity price is higher than a preset threshold (such as 1 yuan / kWh), the distributed energy power selling contract can be triggered to guide distributed energy to sell electricity at a high price and "increase the power supply" for the grid. By using the electricity price fluctuation to trigger trading behaviors, the economic regulation ability of distributed energy can be improved, and the peak - load complementary of distributed energy and centralized power grid can be realized.

[0168] During special periods and extreme events, it is often necessary to adjust the operation mechanism of conventional energy trading. It is necessary to achieve data interconnection and interoperability with business systems such as the power grid dispatching system and the meteorological system to obtain major event information such as the power grid maintenance plan, equipment failure information, and extreme weather warnings. Once a specific event is detected and has a significant impact on the operation of the microgrid energy, a special trading emergency plan can be activated. For example, during the power grid maintenance period, distributed energy can be triggered to assume the power grid backup and suspend the external output transaction; during the extreme weather warning period, distributed energy can be triggered in advance to increase the transaction to prepare for the power grid peak shaving. By applying the scenario-based event trigger mechanism, it is possible to accurately respond to the complex and changing external environment and improve the flexibility and adaptability of energy trading.

[0169] The trigger judgment of energy trading does not involve a "rigid superposition" of multiple conditions, but follows the principles of "prioritized response" and "flexible trigger". Priorities and weights are set for various trigger conditions. For situations where multiple trigger conditions are met simultaneously, they are sorted and triggered according to the order of importance and urgency of the conditions. For example, the trigger condition of a major event has the highest priority. Once a major event occurs, regardless of whether other trading conditions are met, the event emergency trading plan must be activated immediately; while conventional trigger conditions (such as time window, energy balance, market electricity price, etc.) follow the flexible rule of "trigger when one is met", that is, if any conventional trigger condition is met, it can be determined that the energy trading smart contract is triggered, without the need to meet all conditions simultaneously. By reasonably setting the condition priorities and trigger rules, it is possible to balance overall planning while achieving a rapid response, and improve the refinement level of energy trading triggers.

[0170] The trigger judgment of energy trading smart contracts is a key link that combines availability, reliability, and flexibility. By comprehensively sorting out and finely setting the trigger conditions and achieving "integrated" real-time trigger judgment, the smart contract can truly become the "neuron" connecting the physical energy network and the digital trading system, realizing deep coordination between the power operation layer and the value trading layer. From the perspective of availability, the trigger conditions can cover more than 80% of typical trading scenarios, and the condition setting caliber is unified and the logic is clear, making it easy for market players to understand and trust, which is conducive to improving the practicality and universality of trading contracts; from the perspective of reliability, by fusing multi-source data for cross-verification and embedding fault diagnosis and anomaly analysis algorithms, it is possible to avoid false triggers or missed triggers caused by data quality problems, communication interruptions, etc., and ensure the high reliability of trading contract triggers; from the perspective of flexibility, by reserving emergency channels such as event triggers and manual triggers, it is possible to respond to the complex and changing energy system conditions, achieving "rigidity and flexibility combined", and organically combining rule triggers and flexible triggers.

[0171] Step S2320, if the energy trading smart contract is triggered, then conduct energy trading based on the physical-digital space model of microgrid resources.

[0172] Specifically, once it is determined that the energy trading smart contract is triggered, the transaction enters the automated execution stage of the smart contract. Based on the device mapping relationship in the physical-digital space model of the microgrid resources, distributed energy devices and power-consuming devices can directly interact with the energy trading contract through their digital identities (i.e., digital twin nodes). The distributed energy device determines the willingness to sell electricity and the electricity volume for the transaction based on its own real-time power generation data, and transmits the electricity sales demand to the electricity sales contract through the corresponding digital node; the power-consuming device determines the willingness to purchase electricity and the electricity volume for the purchase based on its own actual electricity consumption data, and transmits the electricity purchase demand to the electricity purchase contract through the corresponding digital node. After receiving the electricity sales demand and the electricity purchase demand, the contract starts the intelligent matching program, and quickly locks in the best trading partner among numerous electricity purchase and sales demands according to the preset matching rules and optimization algorithms, forming a transaction matching plan. Once the transaction is successfully matched, the contract will automatically execute subsequent processes such as transaction settlement and power scheduling, and synchronously record the transaction results in the blockchain ledger. The entire transaction matching process is autonomously executed by the energy trading algorithm and can complete the full-process closed loop from intention matching to electricity settlement within milliseconds.

[0173] Furthermore, as Figure 7 shown, step S2320 includes:

[0174] Step S2321, the distributed energy device transmits the electricity sales demand information to the energy trading smart contract through its device node in the physical-digital space model of the microgrid resources;

[0175] Step S2322, the power-consuming device transmits the electricity purchase demand information to the energy trading smart contract through its device node in the physical-digital space model of the microgrid resources;

[0176] Step S2323, the energy trading smart contract deployed in the blockchain network automatically matches the received electricity sales demand information and electricity purchase demand information according to the preset energy trading algorithm to obtain a matching result;

[0177] Step S2324, the energy trading smart contract automatically executes the energy transaction according to the matching result, conducts settlement, records each energy transaction, and generates a blockchain record.

[0178] Specifically, distributed energy devices are the source of energy transactions, and their willingness and ability to sell electricity directly determine the basic plate of transactions. In order to enable distributed energy devices to perceive and respond to energy trading opportunities, it is necessary to establish a mapping connection between physical devices and digital systems through digital twin technology. The device nodes in the physical digital space model of microgrid resources play the role of "incarnation" of distributed energy devices in the digital space, allowing distributed energy devices to cross the "dimensional gap" and freely transfer their status data, control instructions and other information between the physical domain and the information domain. When distributed energy devices have a certain willingness and ability to sell electricity (such as when photovoltaic power generation continues to be higher than self-consumption electricity), they initiate a power sale request to the energy transaction smart contract through their own digital nodes, and submit key transaction elements including energy device ID, saleable electricity, expected price, and transaction time window. Through the "automatic perception + active request" method, human intervention can be minimized to the greatest extent, allowing distributed energy to truly become an "autonomous subject" in the trading market.

[0179] Similar to distributed energy equipment, power-consuming equipment also needs to obtain the ability to perceive transaction information and initiate transaction demands through digital mapping in the physical digital space model of microgrid resources. Once the power-consuming equipment detects that the local power supply cannot meet the power load demand, or needs to carry out power purchase transactions according to the period electricity price, demand response and other mechanisms, it will initiate a power purchase request to the energy transaction smart contract through its own digital node, and submit key transaction elements including the power-consuming equipment ID, planned power purchase quantity, acceptable power price range, and trading period. These power purchase demand judgment logics are mainly based on the real-time analysis of the equipment operation status data (such as equipment on / off status, load power, etc.) in the second monitoring data, the energy consumption data of the power-consuming equipment (such as daily power consumption, peak power, etc.), and the equipment load data (such as load forecast curve, demand response strategy, etc.). It can be seen that the power purchase transaction demand of the power-consuming equipment not only takes into account its own actual power consumption, but also integrates external signals such as grid dispatching instructions and demand response events, which can realize more accurate, real-time and flexible demand-side transactions. The combination of "self-perception + other regulation" is conducive to improving the intelligence level and transaction efficiency of demand-side response.

[0180] The automatic matching of electricity selling demand and electricity purchasing demand is the core function of the energy trading smart contract. After receiving the two types of trading requests, the contract starts the intelligent matching process, quickly analyzes the trading demands and makes precise matches. Intelligent matching is not simply "queue matching", but fully considers multiple factors such as the preferences of trading entities, trading time sequence, and network status, and globally optimizes trading requests with different times, prices, and electricity quantities. The core of the matching engine is the energy trading algorithm, which comprehensively uses various theoretical tools and optimization models such as energy routing, market clearing, and game optimization. Based on the panoramic data of devices, the trading algorithm initially determines a batch of potential optimal trading counterparts by comparing information such as the willing price range, trading electricity quantity scale, and trading time window of trading entities; on this basis, combined with the network topology constraints reflected by the device association data, it further optimizes the trading path with the shortest energy transmission distance and the smallest network loss to form a globally optimal trading combination plan. Through "willing matching + physical optimization", it can not only lock in the economy of energy trading, but also take into account trading quality and network loss levels, achieving multi-objective balance. The matching result not only includes the identity information of the trading parties and the trading electricity quantity, but also includes the network power flow plan for realizing this transaction, which can be directly used to guide the real-time regulation of the distribution network.

[0181] The energy trading contract not only needs to complete the automatic matching of trading demands, but also perform closed-loop execution on the trading results. The "execution" here mainly includes three aspects: First, the physical dispatch of energy. The contract converts the optimized network power flow plan into control instructions and directly issues them to distributed energy devices and electrical equipment related to the transaction through physical interfaces such as energy routers, adjusts the output power and load power of the devices, and guides electric energy to be transmitted from the power generation device to the electrical equipment at the agreed power at the agreed time to complete the delivery of goods. Second, the automatic settlement of funds. The contract automatically completes the transfer of trading funds between the buyer and the seller through the intelligent payment interface according to the electronic wallet information reserved by the trading parties, and synchronously performs supporting operations such as tax payment and electricity bill payment on behalf of others, minimizing the "flight time" of funds to ensure the capital security of all trading parties. Third, the on-chain evidence preservation of information. The contract automatically records the detailed information of trading execution (such as trading entities, trading electricity quantity, trading price, execution time, power flow plan, invoicing information, etc.) on the chain and completes cross-chain data sharing to achieve transparent and traceable full-process information. Through the trinity of "physical + funds + information", it truly realizes the closed-loop automation of the physical and value transfer of energy trading, improving the timeliness, coordination, and credibility of energy trading.

[0182] Energy trading smart contracts are responsible for implementing transaction intentions, optimizing transaction combinations, and executing transaction results. They are the key "engine" for realizing the digitalization and automation of energy transactions. By embedding energy physical models, contracts can realize online synchronous simulation of energy transactions and distribution networks, greatly improving the security and executability of transaction combination schemes; by applying intelligent matching algorithms, contracts can quickly lock in the global optimal transaction scheme and balance market efficiency and system benefits; by directly connecting with energy routers, smart meters, and smart payment systems, contracts can complete the entire transaction process such as energy scheduling, fee settlement, and information recording within milliseconds, maximizing the compression of the time and space span of transactions. In addition, solidifying the energy transaction process into smart contract code and deploying it on the chain is conducive to strengthening the transparency and certainty of transaction rules. When the transaction subject has objections to the transaction results, the contract code logic can be traced for arbitration to effectively protect the legitimate rights and interests of all parties to the transaction. It can be foreseen that with the continuous enrichment of energy transaction scenarios and the continuous increase in transaction frequency, energy transaction contracts will inevitably evolve from single function execution to comprehensive intelligent decision-making, becoming the commanding heights for leveraging the innovation of energy Internet transaction paradigm.

[0183] Step S3000, integrate the energy consumption traceability function into the distributed energy trading platform, generate an energy consumption traceability report, and form an energy consumption traceability certificate on the blockchain.

[0184] Furthermore, step S3000 includes:

[0185] Step S3100, extracting the first energy consumption data of each energy transaction according to the blockchain record;

[0186] Specifically, blockchain records contain rich and difficult-to-tamper transaction data. By "decoding" blockchain records, the key elements of each energy transaction can be extracted from them, and a structured first energy consumption data set can be formed. The extracted content mainly includes: transaction power data, which reflects the physical flow of energy from the power generation side to the power consumption side; transaction amount data, which reflects the transfer of economic value in the process of energy flow; transaction time series, which reflects the timeline and dynamic process of energy transactions; transaction subject information, which clearly identifies the electricity seller and electricity buyer in each transaction. These elements together constitute the basic portrait of energy transactions, but the physical mechanism and efficiency characteristics of energy flow cannot be fully portrayed from the first energy consumption data alone. Therefore, it is necessary to introduce more dimensional data and superimpose and integrate them with the first energy consumption data to truly gain insight into the energy consumption laws behind energy transactions.

[0187] Although the advantages of blockchain data "going on-chain" are obvious, to realize the data value, "off-chain" applications are still needed. By extracting the fields related to energy consumption analysis from blockchain records in a targeted manner and performing preprocessing operations such as data cleaning and normalization off-chain, high-quality blockchain "raw materials" can be provided for subsequent energy consumption traceability analysis, while also maximizing the protection of the privacy and security of blockchain data. Deeply integrating energy transaction data with blockchain technology is an innovative idea to promote the sharing and opening of energy data and improve the collaborative efficiency of the energy system, which is of great significance for building a ubiquitous, peer-to-peer, and flexible interactive modern energy system.

[0188] Step S3200: Access the physical-digital space model of the microgrid resources and extract the second energy consumption data related to each transaction.

[0189] Specifically, after the extraction of the first energy consumption data, it is also necessary to supplement and extract more comprehensive and three-dimensional energy consumption data from the physical-digital space model of the microgrid resources in combination with the physical perspective of energy consumption analysis to form a second energy consumption data set. The second energy consumption data set mainly includes: the real-time energy consumption data of the equipment related to each transaction, which reflects the energy consumption status of the energy equipment during the transaction period; the energy efficiency data of the equipment, which quantifies the energy conversion efficiency of the energy equipment at different load levels; and the load curve data, which depicts the dynamic distribution characteristics of energy demand in the time domain. These data are directly obtained from the real-time monitoring of physical equipment, making up for the blind spot of energy consumption insight in transaction data. During the access process, first, based on the transaction entity ID, lock the source device and the destination device participating in the transaction in the equipment panoramic information database; then, based on the equipment operation monitoring data and the equipment nameplate information, filter out the energy consumption, energy efficiency, and load data during the transaction period; finally, organize and map these data according to the equipment ID and timestamp to form a structured equipment-level energy consumption portrait as the second energy consumption data set.

[0190] By integrating blockchain transaction data and physical equipment monitoring data, a panoramic digital portrait of energy flow can be constructed from both the macroscopic and microscopic levels, making energy consumption analysis no longer a "black box detection", but based on observable and trustworthy data, maximizing the accuracy and interpretability of energy consumption analysis. In addition, with the increasing maturity of the energy and power Internet of Things, a large amount of multi-source heterogeneous data is continuously collected, and the costs of data collection, transmission, storage, and calculation are continuously reduced. The spatio-temporal granularity and quality of energy consumption data will be significantly improved, which will surely give rise to more new applications for energy consumption management.

[0191] Step S3300: Conduct a fusion analysis on the first energy consumption data and the second energy consumption data to construct an energy consumption traceability tree model.

[0192] Specifically, the energy consumption traceability tree model is an innovative energy consumption analysis paradigm. Starting from transactions, it integrates blockchain data and energy consumption physical data to trace and depict the energy flow trajectory from two dimensions of time and space. The construction process of the model can be divided into four steps: First, based on the first energy consumption data uploaded to the blockchain, construct an original directed graph of energy transactions; Second, use the second energy consumption data extracted from the physical digital space model of microgrid resources as the attribute data of nodes and edges, and append it to the directed graph to form a multi-attribute, heterogeneous complex network; Third, map the transaction time series data to the nodes and edges of the graph to form a spatio-temporal energy consumption traceability graph with a time dimension; Finally, extract frequently co-occurring transaction patterns from the spatio-temporal energy consumption traceability graph to construct an energy consumption traceability tree. From a mathematical perspective, the energy consumption traceability tree is an N-ary tree structure. The levels of the tree reflect the progressive relationship of energy flow from the supply side to the demand side. The nodes of the tree are the key carriers of the flow process, and the edges of the tree carry key attributes such as the direction, efficiency, and time-varying of the flow. From a physical perspective, the energy consumption traceability tree is a microcosm of the coordinated operation of source-network-load-storage within a specific area, revealing the loss mechanism and efficiency bottleneck of energy in the production, transmission, consumption and other links, and providing a data method for carrying out energy consumption control throughout the life cycle.

[0193] Further, as Figure 8 shown, step S3300 includes:

[0194] Step S3310, based on the first energy consumption data in the blockchain record, construct a directed graph of energy transactions;

[0195] The directed graph of energy transactions has distributed energy equipment nodes and power consumption equipment nodes participating in energy transactions as vertices, and energy transactions as directed edges. The direction of the directed edge represents the energy flow direction, and the weight of the edge is the transaction power consumption.

[0196] Step S3320, use the second energy consumption data as attribute data and append it to the corresponding nodes of the directed graph of energy transactions;

[0197] Step S3330, obtain time series data, and embed the time series data in the directed graph of energy transactions to form a spatio-temporal energy consumption traceability graph;

[0198] Step S3340, based on the spatio-temporal energy consumption traceability graph, extract typical transaction patterns and construct an energy consumption traceability tree model.

[0199] Suppose there are m distributed energy equipment nodes, n power consumption equipment nodes, and c energy transaction nodes in the microgrid;

[0200] The energy consumption traceability tree model G is: Among them, V is the set of nodes: , to is a distributed energy device node, to is an electrical equipment node, to is an energy trading node. E is a set of directed edges: ; represents a directed edge pointing from the i-th distributed energy device node to the j-th electrical equipment node.

[0201] Specifically, the energy trading directed graph is the basic network for energy consumption analysis. There are two types of nodes in the graph: one is the distributed energy device nodes participating in energy trading, representing the electricity-selling entities in the transaction; the other is the electrical equipment nodes participating in energy trading, representing the electricity-buying entities in the transaction. The directed edges connecting the nodes represent an actual energy transaction. The direction of the directed edge is from the electricity-selling node to the electricity-buying node, characterizing the actual flow direction of energy; the weight of the directed edge is the transaction electricity quantity, reflecting the scale of energy flow. The data of these key elements all directly come from the first energy consumption data of the blockchain. By performing global mapping and topological reconstruction on numerous distributed transaction records, an associated network of energy trading is established, which can intuitively display elements such as the trading scope, trading objects, and trading scale of distributed energy, and initially reveals the spatial structure of energy flow. However, the energy trading directed graph only reflects the "skeleton" of energy flow and lacks the description of the energy consumption characteristics of the equipment itself.

[0202] Embedding the second energy consumption data into the energy trading directed graph can enrich the semantic information of the graph and improve the analysis and generalization ability of the graph. According to the device ID index in the second energy consumption dataset, data such as real-time energy consumption, energy efficiency, and load can be accurately mapped to the corresponding source node and destination node in the graph. These data, as the attribute data of the nodes, endow each node in the graph network with a distinct energy consumption "portrait". For example, the attributes carried by a distributed photovoltaic node can include information such as the power generation efficiency of the photovoltaic panel, the efficiency of the inverter, and the grid-connected load matching degree; the attributes carried by an industrial park electricity consumption node can include information such as the electricity consumption of the park, the peak-valley difference of the load, and the energy-saving potential. Embedding these device-level energy consumption portraits into the network can more precisely compare and analyze the energy consumption differences of different trading entities, and can also deduce the local transfer effect of energy consumption in space in combination with the network topology. Through the fusion modeling of attribute data and graph data, the "breadth" of energy consumption analysis is greatly expanded, realizing the multi-dimensional deconstruction of the energy flow process.

[0203] Energy trading and energy flow are not isolated static events, but processes that evolve dynamically on a continuous time axis. To comprehensively characterize the spatio-temporal evolution law of energy consumption, it is necessary to introduce time series data into the existing graph model. The time series data mainly comes from the transaction timestamp field in the blockchain record, which reflects the order of transactions. The time series data can be mapped to the time attributes of nodes and edges to form a spatio-temporal complex network with a time dimension. For example, at time t1, a transaction occurs between nodes A and B, forming an edge; at time t2, a new transaction occurs between nodes A and C, forming another edge. As time goes by, the edges will continuously increase and the network will continue to evolve. At the same time, the energy consumption attribute value of the node itself also changes dynamically with time. By embedding time series data into the graph, not only can the snapshot of energy flow at a certain moment be analyzed, but also the longitudinal progressive law of the flow on the time scale can be analyzed, introducing a new degree of freedom of "time" in energy consumption analysis to achieve true spatio-temporal integration for tracing. For example, according to the edge slices at different times, several subgraphs under time windows can be formed to intuitively show the stage characteristics of energy flow in the time dimension.

[0204] The spatio-temporal energy consumption tracing graph reveals the spatio-temporal panorama of energy flow, but the analysis complexity of the graph model is relatively high, which is not conducive to intuitively understanding the energy consumption law. Based on the graph model, it is necessary to further refine the key transaction patterns and construct a concise and highly generalized energy consumption tracing tree paradigm. The transaction pattern is a frequently co-occurring node connection substructure in the spatio-temporal graph, representing the energy consumption combination characteristics in the three dimensions of energy, time, and space. Graph mining algorithms can be used to perform pattern recognition on the transaction paths, transaction electricity, and transaction time distribution in the spatio-temporal energy consumption tracing graph, and screen out the key transaction patterns that frequently appear statistically. These patterns often contain the internal laws of energy flow and are of great value for characterizing the spatio-temporal distribution, conduction mechanism, etc. of energy consumption. After obtaining the key transaction patterns, they can be organized into a tree topology structure. The root node of the tree is the source distributed energy device that initiates the transaction, the leaf nodes are the terminal power consumption devices, and the intermediate layer nodes are the energy trading events that connect the source and the load. Through the structured representation of the key transaction patterns, a concise and semantically rich energy consumption tracing tree is formed, and this tree model has obvious advantages in terms of intuitiveness, interpretability, computability, etc.

[0205] The energy consumption traceability tree model is a brand-new energy consumption analysis method. Through the integrated fusion of graph data, attribute data, and time-series data, a complete energy consumption spatio-temporal characterization framework is established. On this basis, high-level semantic features are extracted to form a tree-shaped traceability structure with "explanatory power" for energy consumption data. Compared with traditional energy consumption statistical analysis, this model has significant advantages such as globality, relevance, evolution, and semantics, and can fully utilize the physical and data value potential of the energy system. From the perspective of globality, the tree model can cover the whole-process data from energy generation to end-use energy, revealing the energy flow laws of each link of the source-network-load-storage; from the perspective of relevance, the tree model takes into account both transaction data and physical attribute data, depicting the dynamic mapping relationship between the energy flow and the entity space; from the perspective of evolution, the tree model maps the time-series features into a hierarchical structure, facilitating the understanding of the distribution and evolution characteristics of energy at different time scales; from the perspective of semantics, the tree model extracts the association rules behind frequent trading behaviors, forming a highly generalized energy consumption combination pattern, which is convenient for knowledge-based interpretation and reasoning decision-making.

[0206] Step S3400, according to the energy consumption traceability tree model, calculate the energy consumption contribution degree and the energy use responsibility sharing ratio of each trading party, generate an energy consumption traceability report, and store it on the chain for certification.

[0207] Specifically, the energy consumption traceability tree model not only depicts the flow trajectory of energy in space and time, but also implies deep information such as efficiency loss and subject responsibility during the flow process. Through quantitative deconstruction and semantic mining of the tree model, a comprehensive and multi-dimensional energy consumption analysis report can be formed, and the report is stored on the chain for certification using blockchain technology to form a credible energy digital certificate. The post-analysis process of the energy consumption traceability tree mainly includes four steps: one is to statistically analyze the key energy consumption data of each equipment node in the tree; the second is to calculate the energy consumption contribution degree of each source node; the third is to calculate the energy use responsibility sharing ratio of each end node; the fourth is to estimate the power transmission loss of each trading node. After obtaining the energy consumption analysis results of each node, a comprehensive energy consumption traceability report of the microgrid as a whole is formed, and the report is hashed and stored on the chain through a cryptographic algorithm to ensure the objectivity, fairness, and immutability of the energy consumption information. The energy consumption traceability report can be used as a new type of energy digital certificate in fields such as energy efficiency assessment, carbon asset management, and green finance, enabling energy consumption data to release application value. At the same time, through the blockchain certification mechanism, all stakeholders can reach a consensus on the report content, accept social supervision, and establish the concept of responsible energy.

[0208] Furthermore, step S3400 includes

[0209] Step S3410, traverse each node in the energy consumption traceability tree model, and statistically analyze the cumulative electricity sales volume E i of each distributed energy equipment node, the cumulative electricity purchase volume L j of each electricity-consuming equipment node, and the power transmission loss H k; represents the cumulative electricity sales of the i-th distributed energy equipment node, ; represents the cumulative power purchase of the jth power consumption device node, ;H k represents the power transmission loss of the kth energy trading node, ; m is the total number of distributed energy device nodes, n is the total number of power consumption device nodes, and c is the total number of energy trading nodes;

[0210] Specifically, the energy consumption traceability tree consists of three types of nodes: distributed energy equipment nodes, representing the source points in the microgrid, such as photovoltaic power stations, energy storage batteries, etc.; power equipment nodes, representing the end points in the microgrid, such as industrial parks, commercial buildings, etc.; energy trading nodes, representing the energy transfer events between the source point and the end point. The tree structure is the basis for the implementation of energy consumption responsibilities. It is necessary to use tree traversal algorithms, such as depth-first search, breadth-first search, etc., to visit the nodes of the tree one by one, and extract key energy consumption data according to the node type. For distributed energy equipment nodes, it is necessary to accumulate and sum the electricity sales of all previous transactions and count them as the cumulative electricity sales E. i , reflecting the total scale of its energy output; for power consumption equipment nodes, it is necessary to accumulate and sum the amount of electricity purchased in all previous transactions and calculate it as the cumulative amount of electricity purchased L j , reflecting the total scale of its energy consumption; for energy trading nodes, it is necessary to combine the spatial location and electrical topology of the two parties to evaluate the energy loss H generated during the power transmission process of the transaction k , reflecting the efficiency of energy transmission. These three types of data are the basis for energy consumption responsibility analysis. Among them, the cumulative electricity sales of distributed energy equipment nodes represent their historical contribution to the energy supply of microgrids; the cumulative electricity purchases of power equipment nodes represent the cumulative impact of their energy consumption behavior on microgrid energy consumption; the transmission loss of energy trading nodes reflects the degree of network flow optimization and energy allocation efficiency. By summarizing the tree structure data horizontally and vertically, the overall distribution and individual differences of energy flow can be portrayed.

[0211] Step S3420, calculating the energy consumption contribution of each distributed energy device to generate a first analysis result;

[0212] The calculation of the energy consumption contribution of each distributed energy device includes:

[0213] The cumulative electricity sales of each distributed energy device node are summed up to obtain the total energy supply of each distributed energy device node, and the cumulative electricity sales of the distributed energy device node E i Divided by the total energy supply, the energy consumption contribution of the i-th distributed energy device node is obtained.

[0214] Specifically, the energy consumption contribution degree is a dimensionless index used to measure the contribution of a single distributed energy device to the total energy supply of the microgrid. The calculation method is to divide the cumulative electricity sales of the device by the sum of the cumulative electricity sales of all distributed energy devices to obtain a percentage value. The larger this index, the higher the proportion of green electricity provided by the device in the microgrid energy supply, and the greater the contribution to reducing fossil energy consumption and promoting energy conservation and emission reduction. Objectively, the energy consumption contribution degree is affected by factors such as the installed capacity of the device, power generation efficiency, and grid connection duration. However, in the long run, it mainly depends on the clean attributes of the device itself and the operation and management level. By horizontally comparing the energy consumption contribution degrees of different devices, the "main force" and "new force" of energy supply can be identified, and targeted support policies can be formulated to optimize the energy structure configuration. For example, assume that there are three distributed energy devices in Microgrid A, and their cumulative electricity sales in one year are 1 million kWh, 800,000 kWh, and 500,000 kWh respectively. Then the energy consumption contribution degrees of the three are 43.5%, 34.8%, and 21.7% respectively. Based on this, it can be judged that the leading power stations with larger installed scales and advanced technologies are the ballast stones of the green energy in this microgrid. Subsequently, efforts should be made to improve their power generation efficiency and utilization hours to consolidate the dominant position of clean energy.

[0215] Step S3430, calculate the energy consumption responsibility sharing ratio of each electrical device, and generate a second analysis result;

[0216] The calculation of the energy consumption responsibility sharing ratio of each electrical device includes:

[0217] Sum up the cumulative electricity purchases of each electrical device node to obtain the total energy consumption of each electrical device node. Divide the cumulative electricity purchase L of each electrical device node j by the total energy consumption to obtain the energy consumption responsibility sharing ratio of the jth electrical device node.

[0218] Specifically, the energy consumption responsibility sharing ratio is also a percentage indicator, which is used to measure the proportion of the electricity consumption behavior of a single electrical device in the total electricity consumption of the microgrid and the impact degree of its electricity consumption characteristics on the microgrid energy balance. The calculation method is to divide the cumulative electricity purchase amount of the device by the sum of the cumulative electricity purchase amounts of all electrical devices. The higher the responsibility sharing ratio, on the one hand, it indicates that the electricity consumption scale of the device is more prominent in the microgrid and its dependence on the power grid is stronger; on the other hand, it indicates that the electricity consumption curve of the device may have greater volatility and uncertainty, which may induce the peak regulation and frequency modulation pressure of the power grid. Therefore, when analyzing the energy consumption responsibility, it is necessary to combine the electricity consumption scale and the characteristics of the electricity consumption curve. For devices with a relatively large electricity consumption scale but a relatively stable load curve, a certain preferential electricity price can be given to guide them to use electricity at off-peak times and avoid peak loads; for devices with a small electricity consumption scale but a large load curve fluctuation, they can be required to bear the standby capacity cost or share the power grid loss, so as to encourage them to actively optimize the internal electricity consumption arrangement. For example, the annual cumulative electricity consumption of three parks, namely Park A, Park B, and Park C in Microgrid B, is 2 million kWh, 1.2 million kWh, and 0.8 million kWh respectively, and Park C is a continuous chemical enterprise with a large amount of electricity consumption at night. Based on this, the electricity consumption responsibility sharing ratios of the three can be calculated as 50%, 30%, and 20% respectively, and Park C bears the power grid loss and standby costs caused by the peak-valley difference, so as to bear a greater electricity consumption responsibility under the same electricity consumption.

[0219] Step S3440, calculate the transmission loss of each energy trading node to generate the third analysis result;

[0220] The calculation of the transmission loss shared by each energy trading node includes:

[0221]

[0222] Where:

[0223] : The transaction electricity volume of the kth energy trading node, ;

[0224] : The transaction electricity volume of the sth energy trading node, ;

[0225] : The transmission loss of the kth energy trading node.

[0226] Specifically, during the transmission of electric energy in space, certain active power losses will occur due to factors such as line resistance. Transmission losses are closely related to factors such as transmission distance, voltage level, and power flow load. Energy trading nodes record the spatial location information and electrical distance parameters of both trading parties, as well as the real-time power flow level at the time of the transaction. Therefore, they can serve as an important carrier for mapping and calculating transmission losses. For each energy trading node, its transmission losses can be estimated through a network power flow model, and considering the proportion of the traded electricity in the total electricity, the total transmission losses can be allocated among the trading nodes. The loss electricity can be converted into electricity cost or line loss rate. By calculating the transmission losses allocated to each trading node, the economy and energy utilization efficiency of each transaction can be evaluated. Transmission losses are an important influencing factor for the efficiency of the energy system and are also the key object of optimization in the energy Internet. By analyzing the transmission loss data, weak links in the power grid can be discovered and the grid structure can be optimized; the characteristics of line losses at different times can also be summarized to achieve economic dispatching under time-of-use electricity prices. For example, in remote mountainous areas with overly long transmission lines, nearby trading can be guided through energy routers to reduce long-distance power transmission; during low electricity consumption periods, the transmission voltage level can be appropriately increased to reduce line current and power losses. The energy trading process is integrated with the physical network, and the definition of energy consumption responsibility is synchronized with power flow optimization.

[0227] Step S3450: Summarize the first analysis result, the second analysis result, and the third analysis result to generate an energy consumption traceability report and form an energy consumption traceability certificate on the blockchain.

[0228] Specifically, the energy consumption traceability report is a "physical examination report" for the operation of the microgrid, presenting a panoramic view of the ins and outs of energy flow. On the one hand, the report includes a summary and evaluation of key indicators such as the total energy supply and demand, power purchase and sale structure, load change trend, and transmission and distribution losses of the microgrid, reflecting the overall energy efficiency level and energy conservation and emission reduction achievements of the microgrid; on the other hand, it includes a portrait analysis of the energy consumption contribution degree of key equipment and individual users, the proportion of energy consumption responsibility sharing, and the energy trading mode, reflecting the participation distribution and influence distribution of internal energy consumption in the microgrid. The generation of the energy consumption traceability report can be embedded in various time scales of energy management. Macroscopically, it can be released annually and quarterly for energy planning, power trading, comprehensive energy efficiency assessment, etc.; microscopically, it can be released monthly, daily, and hourly for equipment start-stop optimization, real-time demand response incentives, etc. After the report is released, it should be authenticated and stored through blockchain technology. Using cryptographic principles, the content of the report is hash-calculated to obtain the digital "fingerprint" of the energy consumption report, and then the fingerprint information is written into the energy blockchain to form an energy consumption traceability certificate. Utilizing the immutable and traceable characteristics of the blockchain, all parties can verify the authenticity of the energy consumption report at any time and accept social supervision. The energy consumption traceability certificate can serve as basic data assets in scenarios such as energy efficiency verification, carbon asset trading, and green bond issuance, providing a "trust anchor" for the transfer of energy value.

[0229] The post - analysis process of the energy consumption traceability tree model is a crucial part of deepening the digital application of the energy system. Through the fusion calculation of energy consumption data, the quantitative evaluation of energy consumption behavior, and the blockchain certification of energy consumption reports, a scientific and complete energy consumption responsibility definition mechanism is constructed. The traditional power grid lacks fine accounting of carbon footprint and power quantity, and often apportions energy consumption responsibility in a "flood irrigation" manner according to the production and sales power quantity, which easily leads to insufficient motivation for energy - saving entities and "free - riding" by high - consumption entities. After introducing the energy consumption traceability tree model, "precision drip irrigation" of energy consumption responsibility can be achieved. Vertically, the behavior performance of each energy consumption entity can be objectively quantified and fairly evaluated; horizontally, the contribution degree differences and efficiency gaps between different energy consumption entities are also clear at a glance. In policy formulation, energy - saving incentives and demand response measures can be formulated according to local conditions, making energy consumption data truly become the "vane" for optimizing the power grid and the "measurement standard" for serving users. In supervision and implementation, the energy consumption traceability certificate provides a data foundation for means such as new - energy consumption, energy - efficiency labels, and energy - use rights trading, enabling the market mechanism to play a decisive role in allocating energy resources. It can be foreseen that with the continuous deepening of the digital transformation of the energy and power system, this innovative paradigm of the energy consumption traceability tree will surely move from micro - grids to large - scale power grids, awakening the endogenous motivation of participants in all links of the energy value chain with energy consumption data, promoting the high - degree integration of energy flow, information flow, and value flow, and providing new ideas and new paths for building a clean, low - carbon, safe, and efficient modern energy system.

[0230] Example 2

[0231] Based on Example 1, this example provides an energy consumption analysis system based on the physical - digital space model of micro - grid resources, as Figure 9 shown, including:

[0232] Digital model construction module: used to obtain the first monitoring data of distributed energy devices and the second monitoring data of electrical equipment in the micro - grid, fuse the first monitoring data and the second monitoring data, and construct a physical - digital space model of micro - grid resources;

[0233] Energy trading platform construction module: based on the physical - digital space model of micro - grid resources, construct a blockchain - based distributed energy trading platform;

[0234] Energy consumption analysis module: used to integrate the energy consumption traceability function in the distributed energy trading platform, construct an energy consumption traceability tree model; according to the energy consumption traceability tree model, calculate the energy consumption contribution degree of each distributed energy device and the energy - use responsibility sharing ratio of each electrical equipment, generate an energy consumption traceability report, and store it on the chain for certification.

[0235] In the digital model construction module, the process of fusing the first monitoring data and the second monitoring data to construct a physical - digital space model of micro - grid resources includes:

[0236] Step S1310: Match the first monitoring data and the second monitoring data according to the energy equipment ID and the power consumption equipment ID to form equipment panoramic data;

[0237] Step S1320: Establish the association relationship between equipment nodes according to the energy equipment location information and the power consumption equipment location information to form equipment association data;

[0238] Step S1330: Based on the equipment panoramic data and the equipment association data, construct a physical digital space model of microgrid resources. The distributed energy equipment and the power consumption equipment are equipment nodes in the physical digital space model of microgrid resources.

[0239] In the energy trading platform construction module, the construction of the distributed energy trading platform based on blockchain includes:

[0240] Step S2100: Upload the first monitoring data and the second monitoring data in the physical digital space model of microgrid resources to the blockchain;

[0241] Step S2200: Design an energy trading smart contract based on blockchain and deploy it to the blockchain network; the energy trading smart contract includes a power purchase contract and a power sale contract;

[0242] Step S2300: According to the first monitoring data and the second monitoring data, determine whether to trigger the energy trading smart contract. If triggered, conduct energy trading based on the physical digital space model of microgrid resources according to the deployed energy trading smart contract; if not triggered, continue to monitor the operation status of the microgrid and wait for the triggering condition to be met;

[0243] The said Step S2100 includes:

[0244] Step S2110: Select the consortium blockchain as the blockchain underlying architecture, set each entity in the microgrid as consortium members, and allocate nodes to the consortium members;

[0245] Step S2120: Format the first monitoring data and the second monitoring data into transaction events on the blockchain and upload them to the blockchain nodes;

[0246] Step S2130: Each blockchain node verifies and confirms the transaction events through the consensus mechanism.

[0247] The said Step S2200 includes:

[0248] Step S2210: Design an energy trading algorithm, and convert the energy trading algorithm into smart contract code; the energy trading algorithm includes a transaction matching algorithm, a pricing algorithm, and a settlement algorithm;

[0249] Step S2220: Set the transaction contract triggering condition and deploy the smart contract code to the blockchain network.

[0250] The step S2300 includes:

[0251] Step S2310, judging whether to trigger an energy trading smart contract according to the first monitoring data, the second monitoring data and a preset trading contract triggering condition;

[0252] Step S2320, if the energy trading smart contract is triggered, then carry out an energy transaction based on the physical digital space model of microgrid resources.

[0253] The step S2320 includes:

[0254] Step S2321, the distributed energy device transmits the electricity selling demand information to the energy trading smart contract through its device node in the physical digital space model of microgrid resources;

[0255] Step S2322, the power consumption device transmits the electricity purchasing demand information to the energy trading smart contract through its device node in the physical digital space model of microgrid resources;

[0256] Step S2323, the energy trading smart contract deployed in the blockchain network automatically matches the received electricity selling demand information and electricity purchasing demand information according to a preset energy trading algorithm to obtain a matching result;

[0257] Step S2324, the energy trading smart contract automatically executes the energy transaction according to the matching result, conducts settlement, records each energy transaction, and generates a blockchain record.

[0258] In the energy consumption analysis module, the construction of the energy consumption traceability tree model includes:

[0259] Step S3310, constructing a directed graph of energy transactions based on the first energy consumption data in the blockchain record;

[0260] The directed graph of energy transactions takes the distributed energy device nodes and power consumption device nodes participating in the energy transaction as vertices, takes the energy transaction as a directed edge, the direction of the directed edge represents the energy flow direction, and the weight of the edge is the transaction power consumption.

[0261] Step S3320, taking the second energy consumption data as attribute data and attaching it to the corresponding nodes of the directed graph of energy transactions;

[0262] Step S3330, obtaining time series data and embedding the time series data in the directed graph of energy transactions to form a spatio-temporal energy consumption traceability graph;

[0263] Step S3340, on the basis of the spatio-temporal energy consumption traceability graph, extracting typical trading patterns and constructing an energy consumption traceability tree model.

[0264] In the energy consumption analysis module, calculating the energy consumption contribution degree of each distributed energy device and the energy consumption responsibility sharing ratio of each electrical device, and generating an energy consumption traceability report. The blockchain-based evidence storage includes:

[0265] Step S3410: Traverse each node in the energy consumption traceability tree model, and count the cumulative electricity sales volume E of each distributed energy device node i , the cumulative electricity purchase volume L of each electrical device node j and the power transmission loss H of each energy trading node k ; represents the cumulative electricity sales volume of the i-th distributed energy device node, ; represents the cumulative electricity purchase volume of the j-th electrical device node, ; H k represents the power transmission loss of the k-th energy trading node, ; m is the total number of distributed energy device nodes, n is the total number of electrical device nodes, and c is the total number of energy trading nodes;

[0266] Step S3420: Calculate the energy consumption contribution degree of each distributed energy device to generate a first analysis result;

[0267] Step S3430: Calculate the energy consumption responsibility sharing ratio of each electrical device to generate a second analysis result;

[0268] Step S3440: Calculate the transmission loss of each energy trading node to generate a third analysis result;

[0269] Step S3450: Summarize the first analysis result, the second analysis result, and the third analysis result to generate an energy consumption traceability report and form an energy consumption traceability certificate on the blockchain.

[0270] The methods, systems, and devices of the present application can be implemented in many ways. For example, the methods, systems, and devices of the present application can be implemented through software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present application are not limited to the specific order described above, unless otherwise specifically stated. In addition, in some embodiments, the present application can also be implemented as a program recorded in a recording medium, and these programs include machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers a recording medium storing a program for executing the method according to the present application.

[0271] In addition, parts of the above technical solutions provided in the embodiments of the present application that are consistent with the corresponding technical solutions in the prior art in terms of implementation principles are not described in detail to avoid excessive elaboration.

[0272] The specific embodiments described above further elaborate in detail the objectives, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An energy consumption analysis method based on a physical digital space model of microgrid resources, characterized in that: The method comprises: Acquire first monitoring data of distributed energy equipment and second monitoring data of power consumption equipment in the microgrid, fuse the first monitoring data and the second monitoring data, and construct a physical digital space model of microgrid resources; the first monitoring data includes energy equipment ID and energy equipment location information; the second monitoring data includes power consumption equipment ID and power consumption equipment location information; Based on the physical digital space model of microgrid resources, a distributed energy trading platform based on blockchain is constructed; energy transactions based on the physical digital space model of microgrid resources are carried out to generate blockchain records; Integrate the energy consumption traceability function in the distributed energy trading platform and build an energy consumption traceability tree model; according to the energy consumption traceability tree model, calculate the energy consumption contribution of each distributed energy device and the energy consumption responsibility sharing ratio of each power-consuming device, generate an energy consumption traceability report, and store it on the chain; The construction of the physical digital space model of microgrid resources includes: matching the first monitoring data and the second monitoring data according to the energy device ID and the power device ID to form device panoramic data; establishing an association relationship between device nodes according to the energy device location information and the power device location information to form device association data; constructing the physical digital space model of microgrid resources based on the device panoramic data and the device association data, wherein the distributed energy device and the power device are device nodes in the physical digital space model of microgrid resources; The energy consumption traceability function is integrated into the distributed energy trading platform to construct an energy consumption traceability tree model, including: extracting first energy consumption data of each energy transaction according to the blockchain record; accessing the physical digital space model of microgrid resources to extract second energy consumption data related to each transaction; fusing and analyzing the first energy consumption data and the second energy consumption data to construct an energy consumption traceability tree model; The fusion analysis of the first energy consumption data and the second energy consumption data to construct an energy consumption traceability tree model includes: constructing an energy transaction directed graph based on the first energy consumption data in the blockchain record; attaching the second energy consumption data as attribute data to the corresponding node of the energy transaction directed graph; acquiring time series data, embedding the time series data in the energy transaction directed graph, and forming a spatiotemporal energy consumption traceability graph; and extracting typical transaction patterns based on the spatiotemporal energy consumption traceability graph to construct an energy consumption traceability tree model.

2. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 1 is characterized in that: The distributed energy trading platform based on blockchain and based on the physical digital space model of microgrid resources includes: Uploading the first monitoring data and the second monitoring data in the physical digital space model of the microgrid resources to the chain; Design blockchain-based energy trading smart contracts and deploy them to the blockchain network; Based on the first monitoring data and the second monitoring data, determine whether the energy trading smart contract is triggered. If triggered, energy trading based on the physical digital space model of microgrid resources is carried out according to the deployed energy trading smart contract; if not triggered, continue to monitor the operating status of the microgrid and wait for the triggering conditions to be met.

3. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 2 is characterized in that: The energy transaction smart contract designed based on blockchain includes: Design energy trading algorithms and convert them into smart contract codes; the energy trading algorithms include transaction matching algorithms, pricing algorithms and settlement algorithms; Set the trigger conditions for the transaction contract and deploy the smart contract code to the blockchain network.

4. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 3 is characterized in that: The design method of the energy trading algorithm includes: Determine the electricity sales demand information based on the first monitoring data; determine the electricity purchase demand information based on the second monitoring data; Calculate the matching degree of supply and demand based on the electricity sales demand information and the electricity purchase demand information; Based on the supply and demand matching degree and equipment association data, a supply and demand matching queue is generated to automatically match the top-ranked supply and demand parties.

5. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 4 is characterized in that: The calculating of the supply-demand matching degree based on the electricity sales demand information and the electricity purchase demand information includes: calculating the energy type matching degree, the electricity quantity matching degree, the price matching degree and the time window matching degree according to the electricity sales demand information and the electricity purchase demand information; calculating the supply-demand matching degree according to the energy type matching degree, the electricity quantity matching degree, the price matching degree and the time window matching degree; The setting of trigger conditions for transaction contracts includes: setting trigger conditions based on transaction time windows, setting trigger conditions based on energy balance status, setting trigger conditions based on market electricity price fluctuations, and setting trigger conditions based on major events.

6. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 5 is characterized in that: The energy transaction based on the physical digital space model of microgrid resources includes: Distributed energy devices transmit electricity sales demand information to energy trading smart contracts through their device nodes in the physical digital space model of microgrid resources; The power-consuming equipment transmits the power purchase demand information to the energy trading smart contract through its device node in the physical digital space model of the microgrid resources; The energy trading smart contract deployed in the blockchain network automatically matches the received electricity sales demand information and electricity purchase demand information according to the preset energy trading algorithm to obtain the matching result; The energy trading smart contract automatically executes energy transactions and settles according to the matching results, records each energy transaction, and generates blockchain records.

7. An energy consumption analysis system based on a physical digital space model of microgrid resources, which is used to implement the energy consumption analysis method based on a physical digital space model of microgrid resources as described in any one of claims 1 to 6, characterized in that: The system comprises: A digital model building module: used to obtain first monitoring data of distributed energy equipment and second monitoring data of power consumption equipment in the microgrid, integrate the first monitoring data and the second monitoring data, and build a physical digital space model of microgrid resources; Energy trading platform construction module: Based on the physical digital space model of microgrid resources, a distributed energy trading platform based on blockchain is constructed; Energy consumption analysis module: used to integrate the energy consumption traceability function in the distributed energy trading platform and build an energy consumption traceability tree model; according to the energy consumption traceability tree model, calculate the energy consumption contribution of each distributed energy device and the energy consumption responsibility sharing ratio of each power-consuming device, generate an energy consumption traceability report, and store it on the chain.

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