Distributed energy management system and method for industrial automation

Abstract

The invention provides a distributed energy management system and method for industrial automation, and relates to the technical field of energy management, and the method comprises the steps: collecting the operation state data of different types of energy in distributed energy from an industrial equipment terminal; local energy state analysis is carried out on the operation feedback instruction by using a parallel chain technology to obtain collaborative allocation boundaries of different energy types, and static scheduling constraints of the distributed energy under each operation condition are constructed; performing energy efficiency anomaly identification on an energy efficiency scheduling strategy during distributed energy scheduling to obtain an interconnection scheduling trend of the distributed energy under source network load storage dynamic coordination, and further determining a correction scheduling hierarchy during graded energy load change; and performing accurate load matching by using the intelligent transformer area fusion terminal to perform quantitative planning on distributed energy operation. According to the invention, dynamic cooperative scheduling can be carried out on the distributed energy system in an industrial automatic operation mode, so that the response speed of energy hierarchical management is improved.

Description

Technical Field

[0001] The present application relates to the technical field of energy management. More specifically, the present application relates to a distributed energy management system and method for industrial automation. Background Art

[0002] Energy management refers to the management process of achieving efficient, stable, and sustainable use of energy through monitoring, control, and optimization of energy. In an industrial automation environment, an energy management system involves comprehensive monitoring and scheduling of different types of energy to ensure the economy, reliability, and environmental friendliness of system operation. With the intelligent development of industrial equipment, distributed energy systems have gradually become an important part, which can effectively utilize local renewable energy, reduce energy costs, and reduce dependence on the traditional power grid while ensuring production requirements. In this context, as an emerging distributed ledger technology, parallel chain technology provides a new solution for energy management. Through its characteristics of decentralization, immutability, and high transparency, it can achieve trustworthy recording and efficient sharing of energy data, further improving the intelligent level of energy management.

[0003] However, existing distributed energy management systems for industrial automation usually lack real-time dynamic monitoring and precise scheduling of different types of energy, resulting in difficulties for the system to achieve collaborative optimization across energy units and precise adjustment of load matching. Existing systems can only perform energy scheduling based on historical data, ignoring the real-time changes in operating states and energy efficiency fluctuations, easily leading to inefficient use and unreasonable allocation of energy resources, thereby affecting the energy efficiency and economy of the system. Consequently, it is impossible to make effective adjustments in a timely manner under load changes, energy fluctuations, and external disturbances, causing instability and waste in energy supply. Therefore, how to perform dynamic collaborative scheduling of distributed energy systems in an industrial automation operation mode to improve the response speed of energy hierarchical management is a problem faced by the industry. Summary of the Invention

[0004] The present application provides a distributed energy management system and method for industrial automation, which can perform dynamic collaborative scheduling of a distributed energy system in an industrial automation operation mode to improve the response speed of energy hierarchical management.

[0005] In a first aspect, the present application provides a distributed energy management method for industrial automation. The management method includes the following steps: Collect operation status data of different types of energy in distributed energy from industrial equipment terminals, and obtain corresponding operation feedback instructions of distributed energy nodes in real time through the distributed ledger of the parallel chain; In the industrial automation operation mode, the parallel chain technology is used to analyze the local energy state of the operation feedback instruction, obtain the collaborative allocation boundaries of different energy types, and construct static scheduling constraints for distributed energy under various operating conditions according to the collaborative allocation boundaries. For the energy efficiency scheduling strategy during the distributed energy scheduling, energy efficiency anomalies are identified to obtain the interconnected scheduling trend of distributed energy under the dynamic coordination of the source, grid, load, and storage. Furthermore, based on the interconnected scheduling trend and the operation status data, the allocation of distributed energy is inspected layer by layer to obtain the corrected scheduling level when the hierarchical energy load changes. According to the static scheduling constraints and the corrected scheduling level, the intelligent substation area integration terminal is used to perform precise load matching to quantitatively plan the operation of distributed energy, and at the same time, the parallel chain technology is used to record the quantitative planning strategy in real time.

[0006] In this embodiment, the operation feedback instruction refers to the scheduling instruction issued by the master control system based on the operation status.

[0007] In this embodiment, in the industrial automation operation mode, using the parallel chain technology to analyze the local energy state of the operation feedback instruction to obtain the collaborative allocation boundaries of different energy types specifically includes: Using the parallel chain technology to deploy a multi-source heterogeneous energy sensing network to capture the dynamic energy flow spectrum of industrial equipment in the automation operation mode in real time. Based on the dynamic energy flow spectrum, determine the transient fluctuation characteristics of each energy carrier. According to the transient fluctuation characteristics, determine the energy coordination degree of different energy types in the distribution adjustment. Determine the collaborative allocation boundaries of different energy types from the energy coordination degree.

[0008] In this embodiment, the collaborative allocation boundary represents the adjustable power range of different energy types during the scheduling process.

[0009] In this embodiment, constructing the static scheduling constraints for distributed energy under various operating conditions according to the collaborative allocation boundaries specifically includes: According to the collaborative allocation boundary, determine the power fluctuation characteristics and load response deviation of the distributed energy system under different operating conditions. According to the power fluctuation characteristics and load response deviation, determine the output deviation margin and capacity overlimit risk of each subunit of the distributed energy during the collaborative allocation process. Determine the static scheduling constraints of the distributed energy under various operating conditions from the output deviation margin and the capacity overlimit risk.

[0010] In this embodiment, an energy efficiency anomaly identification is performed on the energy efficiency scheduling strategy during distributed energy scheduling, and the interconnected scheduling trend of distributed energy under the dynamic coordination of the source-network-load-storage is obtained, which specifically includes: Construct a multi-dimensional energy efficiency evaluation index for the coordinated operation of the source-network-load-storage according to the energy efficiency scheduling strategy, and obtain the real-time energy efficiency data and operation coupling characteristics of distributed energy during the dynamic coordination process; Based on the energy efficiency deviation analysis model, determine the energy efficiency deviation degree and dynamic coupling coefficient of each unit within the source-network-load-storage coordination domain; Through the energy efficiency deviation degree, the dynamic coupling coefficient, the real-time energy efficiency data, and the operation. In this embodiment, the interconnected scheduling trend represents the scheduling change trend of different energy units under the framework of the coordinated operation of the source-network-load-storage.

[0011] In this embodiment, the distribution of distributed energy is hierarchically inspected based on the interconnected scheduling trend and the operation status data, and the corrected scheduling level during the hierarchical energy load change is obtained, which specifically includes: Based on the interconnected scheduling trend and operation status data, extract the cross-layer collaborative fluctuation parameters in the distributed energy load hierarchy; Combine the load hierarchy model with the cross-layer collaborative fluctuation parameters to construct a fluctuation collaborative function between energy distribution levels; Output the load fluctuation boundary of the hierarchical load change through the fluctuation collaborative function; Determine the corrected scheduling level during the hierarchical energy load change according to the load fluctuation boundary.

[0012] In this embodiment, based on the static scheduling constraints and the corrected scheduling level, the intelligent substation area integration terminal is used to perform precise load matching to quantitatively plan the operation of distributed energy, and at the same time, the quantitative planning strategy is recorded in real time through the parallel chain technology, which specifically includes: Generate an adaptive scheduling instruction set for the intelligent substation area integration terminal according to the static scheduling constraints and the corrected scheduling level, and define the capacity allocation rules for load matching; Determine the real-time topology verification matrix of the substation area load topology by collecting the output status of distributed energy in real time and combining the adaptive scheduling instruction set; Generate a closed-loop control logic for the quantitative planning of distributed energy operation based on the capacity allocation rules and the real-time topology verification matrix, and record the quantitative planning strategy in real time through the parallel chain technology.

[0013] In a second aspect, the present application provides a distributed energy management system for industrial automation, which is used to execute a distributed energy management method for industrial automation. The management system includes: A data acquisition module, configured to collect operation status data of different types of energy in distributed energy from industrial equipment terminals, and to obtain in real time operation feedback instructions corresponding to distributed energy nodes through the distributed ledger of the parallel chain; A status analysis module, configured to, in an industrial automation operation mode, perform local energy status analysis on the operation feedback instructions by using parallel chain technology to obtain the collaborative deployment boundaries of different energy types, and to construct static scheduling constraints of distributed energy under various operation conditions according to the collaborative deployment boundaries; A hierarchical inspection module, configured to perform energy efficiency anomaly identification on the energy efficiency scheduling strategy during distributed energy scheduling to obtain the interconnected scheduling trend of distributed energy under the dynamic coordination of the source-network-load-storage, and then to perform hierarchical inspection on the distribution of distributed energy based on the interconnected scheduling trend and the operation status data to obtain the corrected scheduling level when the hierarchical energy load changes; A quantitative planning module, configured to, based on the static scheduling constraints and the corrected scheduling level, use an intelligent substation area integration terminal to perform precise load matching to perform quantitative planning on the operation of distributed energy, and at the same time record the quantitative planning strategy in real time through parallel chain technology.

[0014] The technical solution provided by the disclosed embodiments of the present application has the following beneficial effects: Collect operation status data of different types of energy in distributed energy from industrial equipment terminals, and obtain in real time operation feedback instructions corresponding to distributed energy nodes through the distributed ledger of the parallel chain; in an industrial automation operation mode, perform local energy status analysis on the operation feedback instructions by using parallel chain technology to obtain the collaborative deployment boundaries of different energy types, and to construct static scheduling constraints of distributed energy under various operation conditions according to the collaborative deployment boundaries; perform energy efficiency anomaly identification on the energy efficiency scheduling strategy during distributed energy scheduling to obtain the interconnected scheduling trend of distributed energy under the dynamic coordination of the source-network-load-storage, and then to perform hierarchical inspection on the distribution of distributed energy based on the interconnected scheduling trend and the operation status data to obtain the corrected scheduling level when the hierarchical energy load changes; based on the static scheduling constraints and the corrected scheduling level, use an intelligent substation area integration terminal to perform precise load matching to perform quantitative planning on the operation of distributed energy, and at the same time record the quantitative planning strategy in real time through parallel chain technology.

[0015] It can be seen that in this application, the real-time changes in the operating status of distributed energy and the fluctuations in energy efficiency can be accurately captured. Among them, by collecting the operating status data of each energy unit in the distributed energy system in real time and obtaining feedback instructions, the real-time operating status of the energy system can be accurately grasped, realizing dynamic monitoring and precise scheduling of energy use, and ensuring the system stability and response ability. Through local energy status analysis and combining the collaborative deployment boundaries of different energy types, static scheduling constraints can be precisely formulated to avoid inefficient scheduling and waste of energy, while optimizing the coordinated use of various types of energy and improving the system operating efficiency and energy efficiency. Through energy efficiency anomaly identification and interconnected scheduling trend analysis, hierarchical inspection of the distribution of distributed energy is realized, effectively identifying and coping with energy efficiency deviations and fluctuations in the system, improving the optimized scheduling and load management capabilities of energy, and avoiding energy efficiency losses and resource waste. Through the precise load matching of intelligent substation integrated terminals, quantitative planning and adjustment can be achieved, ensuring that the output of each energy unit matches the demand, improving the flexibility and stability of the energy system, effectively avoiding overload and unstable operation, and optimizing resource allocation.

[0016] In summary, the technical solution adopted in this application can perform dynamic collaborative scheduling on the distributed energy system in the industrial automation operation mode to improve the response speed of energy hierarchical management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application 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 the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of the distributed energy management method for industrial automation provided by the present application; Figure 2 It is a schematic flow diagram for determining the collaborative deployment boundary provided by the present application; Figure 3 It is a schematic flow diagram for determining the corrected scheduling level provided by the present application; Figure 4 It is a module structure diagram of the distributed energy management system for industrial automation provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0020] The embodiment of the present application provides a distributed energy management system and method for industrial automation. The core is to collect the operation status data of different types of energy in distributed energy from industrial equipment terminals, and obtain the corresponding operation feedback instructions of distributed energy nodes in real time through the distributed ledger of the parallel chain; in the industrial automation operation mode, use the parallel chain technology to analyze the local energy status of the operation feedback instructions to obtain the collaborative deployment boundaries of different energy types, and construct static scheduling constraints for distributed energy under various operating conditions according to the collaborative deployment boundaries; perform energy efficiency anomaly identification on the energy efficiency scheduling strategy during distributed energy scheduling to obtain the interconnection scheduling trend of distributed energy under the dynamic coordination of the source-network-load-storage, and then conduct hierarchical inspection on the distribution of distributed energy based on the interconnection scheduling trend and the operation status data to obtain the corrected scheduling level when the hierarchical energy load changes; according to the static scheduling constraints and the corrected scheduling level, use the intelligent substation area integration terminal to perform accurate load matching to quantitatively plan the operation of distributed energy, and at the same time record the quantitative planning strategy in real time through the parallel chain technology.

[0021] Embodiment 1. To better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners. Refer to Figure 1 As shown, this figure is an exemplary flowchart of a distributed energy management method for industrial automation according to the present embodiment of the present application. The management method includes the following steps: In step S1, collect the operation status data of different types of energy in distributed energy from industrial equipment terminals, and obtain the corresponding operation feedback instructions of distributed energy nodes in real time through the distributed ledger of the parallel chain.

[0022] In specific implementation, first, at each distributed energy node, where the distributed energy nodes include: photovoltaic, wind power, and energy storage devices, intelligent sensors are installed. The intelligent sensors can be current transformers, voltage sensors, temperature sensors, etc., which collect operating parameters such as voltage, current, power, and temperature. The sensor data is transmitted to the industrial terminal through Modbus, CAN bus, or 4G / 5G. The industrial terminal integrates an edge computing unit, such as an embedded system with an ARM architecture, for data preprocessing, including filtering, denoising, and outlier removal. The operating parameters after preprocessing are used as the operating state data of different types of energy. Then, the industrial device terminal establishes a real-time connection with the energy management system or the dispatching control center through protocols such as Message Queuing Telemetry Transport (MQTT) and Open Platform Communications (OPC). The communication uses 4G / 5G, optical fiber, or Time-Sensitive Networking (TSN) to ensure low latency and high reliability. The main control system calculates the dispatching instructions using linear programming based on the real-time operating state of the distributed energy, including power fluctuations and load changes. Among them, by deploying smart contracts at the distributed energy nodes, the power fluctuations and load changes are recorded in real time in the distributed ledger of the parallel chain. The smart contract is automatically triggered to encrypt and store the operating state data and feedback instructions of the real-time operating state to ensure the immutability of the data. And through the publish-subscribe mode or Remote Procedure Call (RPC), the instructions are pushed to the terminal. After the terminal parses the instructions, the local controller is used to execute the load adjustment and output the adjustment result, which is used as the operating feedback instruction, and no specific limitation is made here.

[0023] It should be noted that in this application, the operating state data refers to the parameters of the voltage, current, power, temperature, frequency, load change, and energy storage state of the distributed energy node in real-time operation; different types of energy in the distributed energy include photovoltaic power generation, wind power generation, biomass energy, gas distributed energy, geothermal energy, small hydropower, energy storage systems, and hydrogen energy, covering various forms of renewable energy and efficient clean energy; the distributed energy node refers to the energy unit that operates independently or collaboratively in the distributed energy system, such as photovoltaic modules, wind turbines, energy storage devices, gas turbines, etc., which can perform energy production, storage, or dispatching; the operating feedback instruction refers to the dispatching instruction issued by the main control system based on the operating state.

[0024] In step S2, in the industrial automation operation mode, the parallel chain technology is used to analyze the local energy state of the operating feedback instruction to obtain the collaborative deployment boundaries of different energy types, and static dispatching constraints of the distributed energy under various operating conditions are constructed according to the collaborative deployment boundaries.

[0025] Preferably, in this embodiment, in the industrial automation operation mode, the parallel chain technology is used to analyze the local energy state of the operating feedback instruction to obtain the collaborative deployment boundaries of different energy types. Refer to Figure 2As shown in the figure, this is a schematic flowchart for determining the collaborative deployment boundary in some embodiments of the present application. In this embodiment, the determination of the collaborative deployment boundary can be implemented through the following steps: In step S21, a multi-source heterogeneous energy sensing network is deployed using parallel chain technology to capture the dynamic energy flow spectrum diagram of industrial equipment in the automated operation mode in real time; In step S22, the transient fluctuation characteristics of each energy carrier are determined based on the dynamic energy flow spectrum diagram; In step S23, the energy collaboration degree of different energy types in the distribution adjustment is determined according to the transient fluctuation characteristics; In step S24, the collaborative deployment boundary of different energy types is determined from the energy collaboration degree.

[0026] Specifically, in implementation, first, intelligent sensors such as current transformers, voltage sensors, and temperature sensors are installed at distributed energy nodes such as photovoltaic, wind energy, energy storage, and power grid to monitor key data such as power, current, voltage, temperature, and frequency in real time. Through the distributed ledger feature of the parallel chain, the data collected by the sensors is uploaded to the chain in real time to ensure the authenticity and immutability of the data. Using the intelligent contract mechanism of the parallel chain, the automated management and data verification of the sensor network are realized. At the same time, combined with the wireless sensor network technology, a multi-source heterogeneous sensing network is constructed to ensure the real-time and stability of data transmission. The terminal preprocesses the data using edge computing and constructs a dynamic energy flow spectrum diagram based on the time series database, and at the same time feeds the processing result back to the parallel chain to realize the distributed management and collaborative optimization of the energy network. Then, the dynamic energy flow spectrum diagram is analyzed using Fourier transform or wavelet transform to output the transient fluctuation signals of each energy carrier (photovoltaic, wind power, energy storage, power grid, etc.). The short-term energy fluctuation trend is output through the autoregressive model to predict the dynamic change patterns of different energies. Then, the correlation coefficient between energies is calculated. This correlation coefficient can be represented by the Pearson correlation coefficient or mutual information entropy to quantify the collaborative degree between different energies. The quantified correlation coefficient is used as the energy collaboration degree, which is not limited here. Then, fuzzy clustering or hierarchical clustering is used to classify the energies to determine the energy types that can be collaboratively deployed. A multi-energy optimal deployment model is established through dynamic programming or reinforcement learning to analyze the complementarity between energies and improve the collaborative efficiency. Finally, a non-linear constraint optimization algorithm such as the Lagrange multiplier method is used to calculate the optimal output range of each energy, and the output optimal output range is used as the collaborative deployment boundary. Then, the Markov decision process is used to simulate the state changes of different energies during the scheduling process to optimize the collaborative boundary, and the energy deployment model is trained in combination with historical operation data to realize the dynamic adjustment of the collaborative deployment boundary.

[0027] It should be noted that in this application, the dynamic energy flow spectrum diagram represents the energy flow relationship of distributed energy under different times and operating conditions, including energy input, output, conversion, and loss conditions; the transient fluctuation characteristics represent the energy change mode of energy carriers in a short period of time, including short-term fluctuation characteristics such as amplitude, frequency, and duration; the energy coordination degree represents the degree of mutual compensation and balance among different types of energy during the dispatching process; the coordinated dispatching boundary represents the adjustable power range of different energy types during the dispatching process; the industrial automation operation mode represents an operation mode that realizes the monitoring and adjustment of the production process through an automated control system, reduces manual intervention, and improves production efficiency and stability; the local energy status analysis refers to a method of real-time processing and analysis of the operation data of distributed energy to evaluate the energy status, optimize the dispatching strategy, and ensure the stability of the system.

[0028] In this embodiment, constructing the static dispatching constraints of distributed energy under each operating condition according to the coordinated dispatching boundary can be realized by the following steps: Determine the power fluctuation characteristics and load response deviation of the distributed energy system under different operating conditions according to the coordinated dispatching boundary; Determine the output deviation margin and capacity overlimit risk of each subunit of the distributed energy during the coordinated dispatching process according to the power fluctuation characteristics and load response deviation; Determine the static dispatching constraints of the distributed energy under each operating condition from the output deviation margin and the capacity overlimit risk.

[0029] In specific implementation, first, based on the collaborative deployment boundary, by analyzing the time-series data of the power output of different types of energy and the load demand, the power fluctuation characteristics are extracted. The Fourier transform or wavelet transform method can be used to perform frequency-domain analysis on the power output to identify the fluctuation frequency, amplitude and other characteristics of different energies under different working conditions, that is, the power fluctuation characteristics. At the same time, based on the real-time load data, analyze the deviation generated by each energy during the load response process, that is, the gap between energy supply and demand, calculate the load response deviation through error analysis, and use the standard deviation to quantify the fluctuation characteristics and deviation to ensure the reliability and stability of the accurate analysis of energy response. Then, according to the power fluctuation characteristics, analyze the maximum fluctuation amplitude of different energies under each working condition, determine its output deviation margin, that is, the maximum range within which the output of the energy can deviate from its target value, and then through extreme value theory and probability analysis, evaluate whether the capacity of the energy system may exceed the design capacity under the given working condition, form a capacity over-limit risk, and use Monte Carlo simulation or risk assessment model to simulate different fluctuation situations, predict the maximum load fluctuation during the operation of different energy types, and combine with the load response deviation to calculate the risk level of the system. Finally, according to the output deviation margin and capacity over-limit risk, formulate static scheduling constraints for each distributed energy unit. For example, set the upper and lower limits of power output to ensure that the energy system always operates within the safe load range during operation. Use linear programming or non-linear programming to solve the optimization model to determine the optimal scheduling strategy of each distributed energy under different working conditions, and form constraint conditions. Take this constraint condition as the static scheduling constraint, which will not be elaborated here.

[0030] It should be noted that in this application, the power fluctuation characteristic represents the change mode of the energy output power within a period of time; the load response deviation represents the gap between energy supply and actual load demand; the output deviation margin represents; the capacity over-limit risk represents the maximum allowable deviation range between energy output and the set target; the static scheduling constraint represents the measure that the actual output of the energy system exceeds its design capacity.

[0031] In step S3, perform energy efficiency anomaly identification on the energy efficiency scheduling strategy during the distributed energy scheduling to obtain the interconnected scheduling trend of the distributed energy under the dynamic coordination of the source, network, load and storage, and then perform hierarchical inspection on the distribution of the distributed energy based on the interconnected scheduling trend and the operation state data to obtain the corrected scheduling level when the hierarchical energy load changes.

[0032] In this embodiment, the energy efficiency anomaly identification of the energy efficiency scheduling strategy during the distributed energy scheduling to obtain the interconnected scheduling trend of the distributed energy under the dynamic coordination of the source, network, load and storage can be implemented by the following steps: Construct a multi-dimensional energy efficiency evaluation index for the collaborative operation of the source, network, load and storage according to the energy efficiency scheduling strategy, and obtain the real-time energy efficiency data and operation coupling characteristics of the distributed energy during the dynamic coordination process; Determine the energy efficiency deviation degree and dynamic coupling coefficient of each unit within the source-grid-load-storage collaborative domain based on the energy efficiency deviation analysis model; Determine the interconnected scheduling trend of distributed energy under the dynamic collaboration of the source-grid-load-storage through the energy efficiency deviation degree, the dynamic coupling coefficient, the real-time energy efficiency data, and the operation coupling characteristics.

[0033] When specifically implemented, first, according to the energy efficiency scheduling strategy, by analyzing the operation characteristics of different energy units, where the energy units include: photovoltaic, wind power, energy storage, power grid, etc., construct multi-dimensional energy efficiency evaluation indicators, including but not limited to power factor, energy conversion efficiency, load tracking accuracy, energy storage efficiency, etc. Then, use Internet of Things sensors and intelligent metering devices to collect the real-time energy efficiency data of each energy node in real time, especially parameters such as power output, energy storage power, system temperature, charge and discharge efficiency, etc., and conduct operation coupling characteristic analysis, that is, based on the collaborative operation mode, by dynamically calculating the energy efficiency coupling characteristics, such as the energy exchange and conversion efficiency between multiple energy units, to identify the coupling behavior of the energy system under different operation modes. Then, by constructing a multi-variable regression model or a machine learning model, such as a support vector machine (SVM) or a neural network, analyze the operation data of different energy units to identify energy efficiency deviations. For example, analyze the deviation between the charge and discharge efficiency of the energy storage system and the expected value, calculate the energy efficiency deviation degree of each unit by comparing the real-time energy efficiency data with the predetermined reference energy efficiency target, that is, the difference between the actual efficiency and the target efficiency, and then use the coupling degree analysis method, that is, the Pearson correlation coefficient, to analyze the degree of collaborative work between multiple energy units, and use the analysis result as the dynamic coupling coefficient to reflect the mutual influence and dependence relationship between energy units. Finally, for the energy efficiency deviation degree and the dynamic coupling coefficient, identify energy efficiency anomalies by setting threshold rules or K-means clustering, timely discover the mismatched or inefficient energy efficiency operation states that occur in the system, and based on the identified energy efficiency anomalies, combined with real-time data and operation coupling characteristics, model the scheduling trend of the distributed energy system through time series analysis, predict the scheduling trend of energy units in future time periods through the model, and use this scheduling trend as the interconnected scheduling trend to evaluate the changes in energy flow and scheduling efficiency under the source-grid-load-storage collaboration, so as to achieve more intelligent scheduling decisions.

[0034] It should be noted that in this application, the energy efficiency scheduling strategy refers to a quantitative index for measuring the output and efficiency of energy units; the dynamic coordination of the power grid, energy production, load demand, and energy storage system means that they work together in real-time scheduling; the multi-dimensional energy efficiency evaluation index represents comprehensively evaluating the operating efficiency and performance of the energy system through power factor, conversion efficiency, and load tracking accuracy; the real-time energy efficiency data represents the dynamic data of energy output, conversion efficiency, and energy storage status; the operating coupling characteristics represent the characteristics of mutual influence and dependence among different energy units during collaborative operation; the energy efficiency deviation represents the deviation between the actual operating efficiency of the energy unit and the theoretical or target efficiency; the dynamic coupling coefficient represents the intensity of interaction among different energy units during dynamic coordination; the interconnected scheduling trend represents the scheduling change trend of different energy units under the framework of the collaborative operation of the power grid, energy production, load demand, and energy storage system.

[0035] Preferably, in this embodiment, the distribution of distributed energy is hierarchically inspected by the interconnected scheduling trend and the operating state data to obtain the corrected scheduling level when the hierarchical energy load changes, with reference to Figure 3 As shown, this figure is a schematic flowchart of determining the corrected scheduling level in some embodiments of this application. The corrected scheduling level in this embodiment can be implemented by the following steps: In step S31, based on the interconnected scheduling trend and operating state data, the cross-layer collaborative fluctuation parameters in the distributed energy load layer are extracted; In step S32, combining the load layer model with the cross-layer collaborative fluctuation parameters, a fluctuation collaborative function between energy distribution layers is constructed; In step S33, the load fluctuation boundary of the hierarchical load change is output through the fluctuation collaborative function; In step S34, the corrected scheduling level when the hierarchical energy load changes is determined according to the load fluctuation boundary.

[0036] In specific implementation, first, collect the interconnection scheduling trends and operation status data, including real-time data such as the load, power output, and energy storage status of each distributed energy unit, and combine the load fluctuations at multiple levels in the system, such as the grid level, load level, and energy storage level. Use wavelet transform or Fourier analysis to extract the cross-layer collaborative fluctuation parameters, which reflect the mutual influence of energy flow and fluctuations between different levels. For example, when the grid load fluctuates, the energy storage system may need to adjust its power output to match the load demand. Next, by establishing the correlation between a multi-level load model, such as distributed load, industrial load, and household load, and the cross-layer collaborative fluctuation parameters, construct the fluctuation collaborative function between energy distribution levels. When constructing the fluctuation collaborative function, use a non-linear regression model or Kalman filter algorithm to model the collaborative effect between levels, characterizing the energy fluctuation coordination between different levels. For example, when a fluctuation occurs in one energy layer, how the other layers respond to compensate or balance. Then, continuously optimize the fluctuation collaborative function through historical data and simulation results to ensure that it accurately reflects the energy fluctuations and scheduling responses between each level. Then, based on the fluctuation collaborative function, combined with the real-time status of each distributed energy, output the load fluctuation boundary, that is, the maximum load fluctuation range that the distributed energy system can withstand under different working conditions. Use the load fluctuation boundary as a constraint condition for scheduling optimization to ensure that each energy unit operates within the fluctuation range, avoiding overloading or over-scheduling, and then dynamically adjust the load fluctuation boundary according to the real-time load demand and system status to ensure that the scheduling system always operates within a reasonable range. Finally, combined with the load fluctuation boundary, according to different energy load changes, use dynamic programming or optimization algorithms to determine the corrected scheduling levels of the distributed energy. When setting the corrected scheduling levels, to ensure the stable operation of the system, according to the load fluctuation boundary, set new scheduling strategies and constraint conditions, and apply them to the energy unit scheduling of each level. Then, through a real-time feedback control mechanism, dynamically adjust the scheduling strategies of each energy unit to ensure timely response to load changes.

[0037] It should be noted that in this application, the hierarchical energy load change refers to the load adjustment amount generated by the energy system at different levels during the process of working condition change; the cross-layer collaborative fluctuation parameter represents the fluctuation characteristics of collaborative changes between different levels; the fluctuation collaborative function represents a mathematical model of the mutual influence and coordination of load fluctuations between different levels; the load fluctuation boundary represents the load fluctuation range that the distributed energy system can withstand under different load conditions within a certain period of time; the corrected scheduling level represents the energy scheduling condition after dynamically adjusting the load changes generated by each level.

[0038] In step S4, according to the static scheduling constraints and the corrected scheduling levels, use the intelligent substation area integration terminal to perform precise load matching and quantitatively plan the operation of the distributed energy, and at the same time, use the parallel chain technology to record the quantitative planning strategy in real time.

[0039] In this embodiment, according to the static scheduling constraint and the corrected scheduling level, the intelligent substation area integration terminal is used to perform precise load matching to quantitatively plan the operation of distributed energy. At the same time, the quantitative planning strategy can be recorded in real time through the parallel chain technology, which can be realized by the following steps: Generate an adaptive scheduling instruction set for the intelligent substation area integration terminal according to the static scheduling constraint and the corrected scheduling level, and define the capacity allocation rule for load matching; Determine the real-time topology verification matrix of the substation area load topology by collecting the output state of distributed energy in real time and combining the adaptive scheduling instruction set; Generate a closed-loop control logic for the quantitative planning of the operation of distributed energy based on the capacity allocation rule and the real-time topology verification matrix, and record the quantitative planning strategy in real time through the parallel chain technology.

[0040] In specific implementation, first, according to the static scheduling constraints and the corrected scheduling hierarchy, combined with the load fluctuation situation, an adaptive scheduling instruction set for the intelligent substation area integration terminal is generated through a machine learning algorithm. This instruction set reflects the optimal scheduling scheme of the distributed energy system under different load conditions. It specifically includes instructions such as load scheduling, energy output, and energy storage regulation. The capacity allocation rule is to formulate a capacity allocation rule for load matching based on the maximum load, load response ability, and scheduling constraints of each energy unit in the system. This rule ensures that when the load changes, the power output and energy storage state of each unit can be reasonably allocated according to the load demand. For example, low-carbon and efficient energy units are preferentially used. Then, through sensors and monitoring systems, the output states of each distributed energy node are collected in real time, including data such as power output and energy storage level. These data can be monitored and updated in real time through Internet of Things technology or cloud computing platforms. When generating the real-time topology verification matrix, by combining the adaptive scheduling instruction set and the output data collected in real time, the topology structure of the substation area load is analyzed to generate a real-time topology verification matrix. This real-time topology verification matrix is used to verify the matching degree between the current load and each energy unit, and to judge whether there is a mismatch or overload. If there is a mismatch, the real-time topology verification matrix can provide a correction scheme for subsequent scheduling. Finally, in the generation of the closed-loop control logic, according to the capacity allocation rule and the real-time topology verification matrix, combined with an optimization algorithm, a quantitative planning closed-loop control logic for the operation of the distributed energy is generated. This quantitative planning closed-loop control logic can automatically adjust the operation states of each energy unit when the load changes or an emergency occurs, ensuring the stability and energy efficiency of the system under all operating conditions; the real-time adjustment of the scheduling strategy is through the closed-loop control logic, and the scheduling strategy is adjusted according to the real-time data feedback to ensure the load adaptation and scheduling optimization of each energy unit. For example, when the load increases, the scheduling system will automatically start the standby energy unit or adjust the charge-discharge strategy of the energy storage device to maintain load balance. At the same time, the formulation, adjustment, and execution process of the quantitative planning strategy are deployed on the parallel chain in the form of a smart contract. Each time the strategy is updated or executed, the smart contract is automatically triggered, and the relevant data and operations are recorded in the distributed ledger of the parallel chain. The non-tamperable and distributed characteristics of the ledger ensure the authenticity and transparency of the records. At the same time, the consensus mechanism of the parallel chain is used to verify and synchronize the data in real time to achieve real-time and reliable recording of the quantitative planning strategy.

[0041] It should be noted that in this application, the intelligent substation area integration terminal refers to a device that integrates various energy monitoring, dispatching, and optimization functions; the adaptive dispatching instruction set refers to a set of dispatching instructions that are dynamically adjusted according to the current load demand and operating status; the capacity allocation rule refers to the conditions for allocating the output capacity of each energy unit; the output status of distributed energy represents the current power output, energy storage status, and other operating indicators of the distributed energy unit; the real-time topology verification matrix refers to a matrix for verifying the matching between the substation area load and the energy unit and the system stability; the closed-loop control logic represents a control mechanism generated by real-time data and the capacity allocation rule.

[0042] It can be seen that in this application, the real-time changes in the operating status of distributed energy and the energy efficiency fluctuations can be accurately captured. Among them, by collecting the operating status data of each energy unit in the distributed energy system in real time and obtaining feedback instructions, the real-time operating conditions of the energy system can be accurately grasped, realizing dynamic monitoring and precise dispatching of energy use, and ensuring system stability and response ability. Through local energy status analysis and combining the coordination boundaries of different energy types, static dispatching constraints can be accurately formulated, avoiding inefficient dispatching and waste of energy, while optimizing the coordinated use of various types of energy and improving the system operation efficiency and energy efficiency. Through energy efficiency anomaly identification and interconnected dispatching trend analysis, hierarchical inspection of the distribution of distributed energy is realized, effectively identifying and coping with energy efficiency deviations and fluctuations in the system, improving the optimized dispatching and load management capabilities of energy, and avoiding energy efficiency losses and resource waste. Through the accurate load matching of the intelligent substation area integration terminal, quantitative planning and adjustment can be realized, ensuring that the output of each energy unit matches the demand, improving the flexibility and stability of the energy system, effectively avoiding overload and unstable operation, and optimizing resource allocation.

[0043] In summary, the technical solution adopted in this application can perform dynamic collaborative dispatching on the distributed energy system in the industrial automation operation mode to improve the response speed of energy hierarchical management.

[0044] Embodiment 2. This application provides a distributed energy management system for industrial automation. Refer to Figure 4 As shown in the figure, which is a module structure diagram of the distributed energy management system for industrial automation according to this embodiment of this application. The management system includes: A data acquisition module 100, which is used to collect the operating status data of different types of energy in the distributed energy from the industrial equipment terminal and obtain the corresponding operating feedback instructions of the distributed energy node in real time through the distributed ledger of the parallel chain. A status analysis module 200, configured to perform local energy status analysis on the operation feedback instruction by using parallel chain technology in the industrial automation operation mode, obtain the collaborative allocation boundaries of different energy types, and construct static scheduling constraints for distributed energy under various operating conditions according to the collaborative allocation boundaries; A hierarchical inspection module 300, configured to perform energy efficiency anomaly identification on the energy efficiency scheduling strategy during distributed energy scheduling, obtain the interconnected scheduling trend of distributed energy under the dynamic coordination of the source, grid, load, and storage, and then perform hierarchical inspection on the allocation of distributed energy based on the interconnected scheduling trend and the operation status data to obtain the corrected scheduling level when the hierarchical energy load changes; A quantitative planning module 400, configured to perform quantitative planning on the operation of distributed energy by using an intelligent substation area integration terminal to execute precise load matching according to the static scheduling constraints and the corrected scheduling level, and simultaneously record the quantitative planning strategy in real time by using parallel chain technology.

[0045] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in one Figure 1 one or more flows and / or blocks Figure 1 or a device for implementing the specified functions in multiple blocks.

[0046] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disc memories, tape memories, or any other medium that can be used to carry or store data and is computer-readable.

[0047] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent in such a process, method, commodity or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, commodity or device including the element.

Claims

1. A distributed energy management method for industrial automation, characterized in that, The management method includes the following steps: Collect the operation status data of different types of energy in distributed energy from industrial equipment terminals, and obtain the corresponding operation feedback instructions of distributed energy nodes in real time through the distributed ledger of the parallel chain; In the industrial automation operation mode, use the parallel chain technology to perform local energy status analysis on the operation feedback instructions to obtain the collaborative allocation boundaries of different energy types, and construct static scheduling constraints of distributed energy under various operating conditions according to the collaborative allocation boundaries; Perform energy efficiency anomaly identification on the energy efficiency scheduling strategy during distributed energy scheduling to obtain the interconnected scheduling trend of distributed energy under the dynamic coordination of the source-network-load-storage, and then perform hierarchical inspection on the allocation of distributed energy based on the interconnected scheduling trend and the operation status data to obtain the corrected scheduling level when the hierarchical energy load changes; According to the static scheduling constraints and the corrected scheduling level, use the intelligent substation area integration terminal to execute precise load matching to quantitatively plan the operation of distributed energy, and at the same time record the quantitative planning strategy in real time through the parallel chain technology.

2. The distributed energy management method for industrial automation according to claim 1, wherein The operation feedback instruction refers to the scheduling instruction issued by the main control system based on the operation status.

3. A distributed energy management method for industrial automation according to claim 1, characterized in that, In the industrial automation operation mode, using the parallel chain technology to perform local energy status analysis on the operation feedback instruction to obtain the collaborative allocation boundaries of different energy types specifically includes: Use the parallel chain technology to deploy a multi-source heterogeneous energy sensing network to capture the dynamic energy flow spectrum of industrial equipment in the automation operation mode in real time; Determine the transient fluctuation characteristics of each energy carrier based on the dynamic energy flow spectrum; Determine the energy collaboration degree of different energy types in the distribution adjustment according to the transient fluctuation characteristics; Determine the collaborative allocation boundaries of different energy types from the energy collaboration degree.

4. The distributed energy management method for industrial automation according to claim 1, wherein, The collaborative allocation boundary represents the adjustable power range of different energy types during the scheduling process.

5. A distributed energy management method for industrial automation according to claim 1, characterized in that, Constructing the static scheduling constraints of distributed energy under various operating conditions according to the collaborative allocation boundary specifically includes: Determine the power fluctuation characteristics and load response deviation of the distributed energy system under different operating conditions according to the collaborative allocation boundary; Determine the output deviation margin and capacity over-limit risk of each subunit of distributed energy during the collaborative allocation process according to the power fluctuation characteristics and load response deviation; Determine the static scheduling constraints of distributed energy under various operating conditions from the output deviation margin and the capacity over-limit risk.

6. A distributed energy management method for industrial automation according to claim 1, characterized in that, Performing energy efficiency anomaly identification on the energy efficiency scheduling strategy during distributed energy scheduling to obtain the interconnected scheduling trend of distributed energy under the dynamic coordination of the source-network-load-storage specifically includes: Construct a multi-dimensional energy efficiency evaluation index for the collaborative operation of the source-network-load-storage according to the energy efficiency scheduling strategy, and obtain the real-time energy efficiency data and operation coupling characteristics of distributed energy during the dynamic coordination process; Determine the energy efficiency deviation degree and dynamic coupling coefficient of each unit within the collaborative domain of the source-network-load-storage based on the energy efficiency deviation analysis model; Determine the interconnected scheduling trend of distributed energy under the dynamic coordination of the source-network-load-storage through the energy efficiency deviation degree, the dynamic coupling coefficient, the real-time energy efficiency data and the operation coupling characteristics.

7. A distributed energy management method for industrial automation according to claim 1, characterized in that, The described interconnected scheduling trend represents the scheduling change trends of different energy units under the framework of coordinated operation of the source-grid-load-storage.

8. A distributed energy management method for industrial automation according to claim 1, characterized in that, Hierarchical inspection of the distribution of distributed energy is carried out based on the described interconnected scheduling trend and the operation status data to obtain the corrected scheduling levels when the hierarchical energy load changes, specifically including: Based on the described interconnected scheduling trend and operation status data, cross-layer collaborative fluctuation parameters in the distributed energy load hierarchy are extracted; Combining the load hierarchy model with the cross-layer collaborative fluctuation parameters, a fluctuation collaborative function between energy distribution levels is constructed; The load fluctuation boundary of the hierarchical load change is output through the fluctuation collaborative function; According to the load fluctuation boundary, the corrected scheduling levels when the hierarchical energy load changes are determined.

9. The distributed energy management method for industrial automation according to claim 1, wherein Based on the described static scheduling constraints and the corrected scheduling levels, the intelligent substation area integration terminal is used to perform precise load matching to quantitatively plan the operation of distributed energy, and at the same time, the quantitative planning strategy is recorded in real time through parallel chain technology, specifically including: An adaptive scheduling instruction set for the intelligent substation area integration terminal is generated according to the static scheduling constraints and the corrected scheduling levels, and the capacity allocation rules for load matching are defined; The real-time topology verification matrix of the substation area load topology is determined by collecting the output status of distributed energy in real time and combining the adaptive scheduling instruction set; Based on the capacity allocation rules and the real-time topology verification matrix, a closed-loop control logic for quantitatively planning the operation of distributed energy is generated, and the quantitative planning strategy is recorded in real time through parallel chain technology.

10. A distributed energy management system for industrial automation, which is used to execute a distributed energy management method for industrial automation according to any one of claims 1 to 9, characterized in that, The management system includes: A data acquisition module, which is used to collect the operation status data of different types of energy in distributed energy from industrial equipment terminals and obtain the corresponding operation feedback instructions of distributed energy nodes in real time through the distributed ledger of the parallel chain; A status analysis module, which is used to perform local energy status analysis on the operation feedback instructions by using parallel chain technology in the industrial automation operation mode to obtain the collaborative allocation boundaries of different energy types, and construct static scheduling constraints for distributed energy under various operating conditions according to the collaborative allocation boundaries; A hierarchical inspection module, which is used to identify energy efficiency anomalies in the energy efficiency scheduling strategy during the scheduling of distributed energy to obtain the interconnected scheduling trend of distributed energy under the dynamic coordination of the source-grid-load-storage, and then perform hierarchical inspection on the distribution of distributed energy based on the interconnected scheduling trend and the operation status data to obtain the corrected scheduling levels when the hierarchical energy load changes; A quantitative planning module, which is used to quantitatively plan the operation of distributed energy by using the intelligent substation area integration terminal to perform precise load matching based on the described static scheduling constraints and the corrected scheduling levels, and at the same time, record the quantitative planning strategy in real time through parallel chain technology.

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