Energy conversion control method and system for energy router

Abstract

The present invention discloses an energy conversion control method and system for an energy router, which belongs to the field of energy management, wherein the method includes: connecting an energy router to obtain equipment operating parameters; setting energy conversion indicators based on an energy conversion link; capturing long-term dependent energy supply and demand connection relationships to obtain a primary supply and demand path set; performing prominent marking to collect a set of energy transfer nodes; combining equipment operating parameters and energy conversion indicators to obtain a first-category and second-category screening energy transfer node set; iteratively updating the primary supply and demand path set to obtain an optimal supply and demand path set. The present application solves the technical problem in the prior art that it is difficult for energy routers to efficiently and accurately control energy conversion and optimize supply and demand paths under complex energy conversion links, and achieves the technical effect of realizing intelligent and precise energy conversion control of energy routers under complex energy conversion links, and obtaining an optimal supply and demand path with maximized stability.

Description

Technical Field

[0001] The present invention relates to the field of energy management, and in particular to an energy conversion control method and system for an energy router. Background Art

[0002] Energy routers are devices that can realize the conversion and control of various forms of energy, and can effectively manage and optimize the production, storage, transmission and consumption of energy. With the continuous development and application of new energy technologies, energy routers play an increasingly important role in energy systems. However, energy routers in the prior art still have some problems when facing complex energy conversion links. First, under complex energy conversion links, it is difficult for existing energy routers to achieve efficient and accurate energy conversion control, resulting in energy loss and low efficiency during the energy conversion process, affecting the operating performance of the entire energy system. Secondly, it is difficult for existing energy routers to optimize the supply and demand paths in complex environments, resulting in a mismatch between energy supply and demand, which in turn affects the stability and reliability of the energy system. Summary of the invention

[0003] The present application provides an energy conversion control method and system for an energy router, aiming to solve the technical problem in the prior art that it is difficult for energy routers to efficiently and accurately control energy conversion and optimize supply and demand paths under complex energy conversion links.

[0004] In view of the above problems, the present application provides an energy conversion control method and system for an energy router.

[0005] One aspect disclosed in the present application provides an energy conversion control method for an energy router, the method comprising: connecting an energy router to obtain device operating parameters, the device operating parameters including power consumption parameters, energy storage status and grid connection status; setting energy conversion indicators based on the energy conversion link of the energy router, the energy conversion indicators including energy conversion efficiency, energy conversion rate and energy utilization rate; based on the energy conversion link, capturing the long-term dependent energy supply and demand connection relationship, obtaining a primary supply and demand path set, each supply and demand path in the primary supply and demand path set has an energy supply end as the average starting point and an energy demand end as the average end point; based on the energy conversion link, highlighting the primary supply and demand path set, collecting an energy transfer node set, the energy transfer node set is not Including the average path starting point and the average path end point corresponding to each supply and demand path in the primary supply and demand path set; based on the energy transfer node set, combined with the equipment operating parameters and energy conversion indicators, the energy transfer nodes are screened by superposition and substitutability to obtain the first type of screened energy transfer node set; based on the energy transfer node set, combined with the equipment operating parameters and energy conversion indicators, the energy transfer nodes are screened by entanglement and substitutability to obtain the second type of screened energy transfer node set; in the energy conversion link, through the first type of screened energy transfer node set and the second type of screened energy transfer node set, it is verified that under the operating conditions limited by the equipment operating parameters, the primary supply and demand path set is iteratively updated with the goal of maximizing the stability of the energy conversion link to obtain the optimal supply and demand path set.

[0006] Another aspect disclosed in the present application provides an energy conversion control system for an energy router, the system comprising: a parameter acquisition module for connecting to the energy router and acquiring device operation parameters, the device operation parameters including power consumption parameters, energy storage status and grid connection status; an indicator setting module for setting energy conversion indicators based on the energy conversion link of the energy router, the energy conversion indicators including energy conversion efficiency, energy conversion rate and energy utilization rate; a relationship capture module for capturing long-term dependent energy supply and demand connection relationships based on the energy conversion link, and acquiring a primary supply and demand path set, wherein each supply and demand path in the primary supply and demand path set has an energy supply end as the average starting point and an energy demand end as the average end point; a node collection module for highlighting the primary supply and demand path set based on the energy conversion link, collecting energy transfer node sets, and energy transfer The node set does not include the average starting point and average ending point corresponding to each supply and demand path in the primary supply and demand path set; a superposition screening module is used to screen the energy transfer nodes based on the energy transfer node set, in combination with equipment operating parameters and energy conversion indicators, by superposition and substitutability, to obtain a first-category screened energy transfer node set; an entanglement screening module is used to screen the energy transfer nodes based on the energy transfer node set, in combination with equipment operating parameters and energy conversion indicators, by entanglement and substitutability, to obtain a second-category screened energy transfer node set; a path optimization module is used to verify in the energy conversion link that under the operating conditions limited by the equipment operating parameters, the primary supply and demand path set is iteratively updated with the goal of maximizing the stability of the energy conversion link to obtain the optimal supply and demand path set.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] Due to the use of connected energy routers, the equipment operating parameters are obtained, including power consumption parameters, energy storage status and grid connection status, so as to fully understand the real-time operating status of the energy router and its system, and provide a basis for subsequent energy conversion control; based on the energy conversion link of the energy router, energy conversion indicators are set, including energy conversion efficiency, energy conversion rate and energy utilization rate, which are helpful to quantitatively evaluate the performance of energy conversion and provide target guidance for optimization control; based on the energy conversion link, the long-term dependent energy supply and demand connection relationship is captured, and the primary supply and demand path set is obtained. Each supply and demand path in the primary supply and demand path set takes the energy supply end as the average path starting point and the energy demand end as the average path end point, identifies the relationship between energy supply and demand, and generates an initial supply and demand path set, laying the foundation for subsequent path optimization; on this basis, based on the energy conversion link, the primary supply and demand path set is highlighted and marked, and the energy transfer node set is collected. The energy transfer node set does not include the average path starting point and average path end point corresponding to each supply and demand path in the first supply and demand path set. By marking and collecting energy transfer nodes, the key nodes in the supply and demand path are identified. , prepare for node screening; based on the energy transfer node set, combined with the equipment operating parameters and energy conversion indicators, the energy transfer nodes are screened by superposition and substitutability to obtain the first type of screened energy transfer node set; the energy transfer nodes are screened by entanglement and substitutability to obtain the second type of screened energy transfer node set, and key nodes are selected from the energy transfer node set through different screening strategies to optimize the supply and demand path; in the energy conversion link, through the first type of screening energy transfer node set and the second type of screening energy transfer node set, it is verified that under the operating conditions limited by the equipment operating parameters, the primary supply and demand path set is iteratively updated with the goal of maximizing the stability of the energy conversion link, and the optimal supply and demand path set is obtained. The supply and demand path set with the best stability under the conditions of meeting the equipment operating conditions is obtained, and the technical solution for optimizing the supply and demand path is completed, which solves the technical problem that the energy router in the prior art is difficult to efficiently and accurately control energy conversion and optimize the supply and demand path under the complex energy conversion link, and achieves the technical effect of realizing intelligent and precise energy conversion control of the energy router under the complex energy conversion link and obtaining the optimal supply and demand path with maximized stability.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic flow chart of an energy conversion control method for an energy router is provided in an embodiment of the present application;

[0011] Figure 2 A structural schematic diagram of an energy conversion control system of an energy router is provided for an embodiment of the present application.

[0012] Explanation of the reference numerals: parameter acquisition module 11, indicator setting module 12, relationship capturing module 13, node collection module 14, overlay screening module 15, entanglement screening module 16, path optimization module 17. DETAILED DESCRIPTION

[0013] The overall idea of ​​the technical solution provided by this application is as follows:

[0014] The embodiment of the present application provides an energy conversion control method and system for an energy router. First, the energy router is connected to obtain the equipment operating parameters including power consumption parameters, energy storage status and grid connection status, and energy conversion indicators such as energy conversion efficiency, energy conversion rate and energy utilization rate are set based on the energy conversion link to provide a basis and goal orientation for subsequent control optimization. Then, the primary supply and demand path set is obtained by capturing the long-term dependent energy supply and demand connection relationship, and the primary supply and demand path set is highlighted and the energy transfer nodes are collected. Next, in combination with the equipment operating parameters and energy conversion indicators, the energy transfer nodes are screened in a superimposed substitutable and entangled substitutable manner to obtain two types of screened energy transfer node sets. Afterwards, through the two types of screening sets, with the goal of maximizing the stability of the energy conversion link, the primary supply and demand path set is iteratively updated under the operating conditions defined by the equipment operating parameters to obtain the optimal supply and demand path set.

[0015] Through the above technical solution, the present application achieves the technical effect of realizing intelligent and precise energy conversion control of energy routers under complex energy conversion links, and obtaining the optimal supply and demand path with maximized stability, effectively solving the problems existing in the prior art.

[0016] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0017] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides an energy conversion control method for an energy router, the method comprising:

[0018] S1: Connect to the energy router and obtain device operating parameters, which include power consumption parameters, energy storage status, and grid connection status.

[0019] Specifically, in order to achieve connection with the energy router and data collection, a variety of communication methods and protocols can be used. For example, through wired communication interfaces such as Ethernet, serial port, CAN, etc., and using industrial communication protocols such as ModBus and Profibus, a stable data communication link can be established with the energy router to obtain equipment operating parameters; using wireless communication technologies such as WIFI, ZigBee, LoRa, etc., through a wireless gateway or directly connected to the wireless module of the energy router, the wireless transmission and collection of equipment operating parameters can be achieved; the energy router can also be connected to the Internet of Things cloud platform, and the Internet of Things communication protocol and API interface provided by the cloud platform can be used to achieve the reporting and remote acquisition of equipment operating parameters.

[0020] By connecting to the energy router, you can obtain the various equipment operating parameters of the energy router, including power consumption parameters, energy storage status, and grid connection status. Among them, the power consumption parameters reflect the energy consumption of the energy router itself, the energy storage status parameters reflect the charge state and capacity information of the energy storage device, and the grid connection status parameters feedback the connection between the energy router and the external grid. By obtaining the equipment operating parameters, the necessary data basis is provided for the subsequent transfer node screening in the energy conversion control.

[0021] S2: Based on the energy conversion link of the energy router, energy conversion indicators are set, and the energy conversion indicators include energy conversion efficiency, energy conversion rate, and energy utilization rate.

[0022] Specifically, the energy conversion link refers to the path for energy transmission and conversion between nodes such as the energy supply end, energy storage equipment, and load end. In this energy conversion link, there are multiple key indicators that affect the efficiency and quality of energy conversion. According to the characteristics of the energy conversion link, three energy conversion indicators are set, namely energy conversion efficiency, energy conversion rate, and energy utilization rate. Among them, energy conversion efficiency reflects the degree of energy loss in the energy conversion process. By evaluating and optimizing the energy conversion efficiency, the loss in the energy conversion process can be minimized and the economy of energy utilization can be improved; the energy conversion rate measures the speed of energy conversion. By controlling and adjusting the energy conversion rate, the timely transmission and balance of energy between different nodes can be achieved, and the flexibility and real-time performance of energy scheduling can be improved; the energy utilization rate reflects the proportion of converted energy in the total energy. By monitoring and optimizing the energy utilization rate, the use efficiency of converted energy can be maximized, waste can be reduced, and the efficiency of the entire energy system can be improved. By setting the above three energy conversion indicators, support is provided for transit node screening.

[0023] S3: Based on the energy conversion link, capture the long-term dependent energy supply and demand connection relationship and obtain a primary supply and demand path set, wherein each supply and demand path in the primary supply and demand path set has the energy supply end as the average starting point and the energy demand end as the average end point.

[0024] Specifically, the energy supply and demand connection relationship refers to the correlation between energy transmission and distribution between the energy supply end and the energy demand end. In complex energy systems, the supply and demand connection relationship often has long-term dependencies, that is, the current energy supply and demand status will be affected by the historical status. In order to optimize energy transmission and distribution, it is necessary to capture and utilize this long-term dependency.

[0025] By analyzing the energy flow and interaction between nodes in the energy conversion link, and using time series analysis, data mining and other technologies, the long-term dependency between energy supply and demand is captured. Based on the long-term dependency, a series of primary supply and demand paths are discovered and extracted. The primary supply and demand path refers to the energy transmission path from the energy supply end to the energy demand end, reflecting the basic flow of energy from production to consumption. When obtaining the primary supply and demand path, the starting point and end point of energy transmission are considered, with the energy supply end as the average starting point and the energy demand end as the average end point, ensuring the integrity and coherence of the primary supply and demand path.

[0026] By obtaining the primary supply and demand path set, the basic connection relationship between supply and demand in the energy system is fully characterized, providing important topological structure information for subsequent energy optimization control. On this basis, the characteristics and limitations of the primary supply and demand path are further analyzed, the space for optimization is discovered, and targeted energy scheduling and optimization are carried out.

[0027] S4: Based on the energy conversion link, the primary supply and demand path set is highlighted and marked, and an energy transfer node set is collected. The energy transfer node set does not include the average path starting point and average path ending point corresponding to each supply and demand path in the primary supply and demand path set.

[0028] Specifically, since there are multiple energy transfer nodes in the process of energy transmission from the energy supply end to the energy demand end, in a complex energy system, the primary supply and demand path is not the optimal path for energy transmission. Among them, the transfer node plays an important role as a bridge and link in the process of energy transmission and conversion.

[0029] First, the primary supply and demand path set is analyzed and labeled, and the nodes that play an important role in the energy transmission process are highlighted through energy flow tracking and key node identification. These marked nodes are energy transfer nodes, which undertake the function of converting and transferring energy between different forms and different regions, thereby obtaining an energy transfer node set. When collecting energy transfer nodes, the nodes included in the primary supply and demand path set are not collected, that is, the energy transfer node set does not include the average starting point (energy supply end) and average end point (energy demand end) corresponding to each supply and demand path in the primary supply and demand path set, thereby ensuring the independence and specificity of the energy transfer node and highlighting its special status and role in the energy transmission process.

[0030] By collecting the energy transfer node set, the distribution and role of key nodes in the energy system can be fully characterized, providing important node information for subsequent energy path optimization. On this basis, the properties, capabilities and constraints of energy transfer nodes can be further analyzed, and these nodes can be reasonably deployed and utilized to achieve global optimization of energy transmission paths.

[0031] S5: Based on the energy transfer node set, combined with the equipment operation parameters and energy conversion indicators, the energy transfer nodes are screened by superimposing and replacing, and a first type of screened energy transfer node set is obtained.

[0032] Specifically, after collecting the energy transfer node set, based on the energy transfer node set, combined with equipment operating parameters and energy conversion indicators, the energy transfer nodes are screened in a superimposed and replaceable manner to obtain a first type of screened energy transfer node set.

[0033] In the process of optimizing the energy transmission path, not all energy transfer nodes are necessary or optimal. In order to obtain a more concise and efficient energy transmission path, the energy transfer nodes are further screened and optimized using the superimposed replaceable node screening method. The so-called superimposed replaceable means that in the energy transmission path, if two or more energy transfer nodes can replace each other in function, and the replacement will not significantly affect the energy transmission efficiency and quality, then these nodes are regarded as superimposed replaceable nodes. In the node screening process, for superimposed replaceable nodes, only one or several key nodes are retained, and other redundant nodes are eliminated to simplify the energy transmission path.

[0034] First, the importance of equipment operation parameters was analyzed. By calculating the weighted average of power consumption parameters, energy storage status, grid connection status and other parameters of each energy transfer node, a comprehensive node operation status index was obtained. The higher the index, the more critical the role of the node in the energy transmission path and the greater its importance. Secondly, the substitutability analysis of energy conversion indicators was carried out. By calculating the difference coefficient between each energy transfer node and other nodes in indicators such as energy conversion efficiency, energy conversion rate, and energy utilization rate, a comprehensive node substitutability index was obtained. The lower the index, the stronger the uniqueness of the node in the energy transmission path and the smaller its substitutability. When calculating the node operation status index and substitutability index, the dynamic weight adjustment method was adopted to dynamically adjust the weight coefficients of various parameters and indicators according to the real-time status and performance requirements of the energy system. This can make node evaluation more flexible and adaptive, and can respond to changes in the energy system in a timely manner. By comprehensively evaluating the node operation status index and substitutability index, for nodes with a high operation status index and a low substitutability index, it is judged that they are of high importance in the energy transmission path and difficult to be replaced, and they should be retained first when screening nodes; while for nodes with a low operation status index and a high substitutability index, it is judged that they are of low importance in the energy transmission path and easy to be replaced, so they can be considered for elimination when screening nodes. In the specific node screening process, an iterative optimization strategy is adopted to sort all energy transfer nodes to form a priority list. Starting from the node with the lowest priority, it is tried to remove them from the energy transmission path one by one, and the path performance before and after elimination is evaluated. If the path performance does not decrease significantly after elimination, the node is removed from the first type of screening energy transfer node set; otherwise, the node is retained in the first type of screening energy transfer node set. By iteratively executing this process until all nodes are evaluated, an optimized first type of screening energy transfer node set can be obtained. The first type of screening energy transfer node set obtained after screening contains the most critical and indispensable energy transfer nodes under the superposition substitutability principle. These nodes play a vital role in the energy transmission path and have a decisive influence on ensuring the efficiency and quality of energy transmission.

[0035] By superimposing alternative screening, while ensuring the energy transmission performance, the energy transmission path is simplified to the maximum extent, redundant nodes and links are reduced, the efficiency and economy of energy transmission are improved, and the foundation is laid for subsequent energy path optimization.

[0036] S6: Based on the energy transfer node set, combined with the device operation parameters and energy conversion indicators, the energy transfer nodes are screened by entanglement substitutability to obtain a second type of screened energy transfer node set.

[0037] Specifically, after obtaining the first type of screened energy transfer node set, the energy transfer nodes are further screened in an entangled and replaceable manner based on the energy transfer node set, combined with equipment operating parameters and energy conversion indicators, to obtain the second type of screened energy transfer node set.

[0038] In addition to superposition substitutability, another node substitution relationship needs to be considered in the process of optimizing the energy transmission path, namely entanglement substitutability. The so-called entanglement substitutability means that in the energy transmission path, if two or more energy transfer nodes are strongly correlated and complementary in function, and replacing any one of the nodes will significantly affect the performance of other nodes, then these nodes can be regarded as entangled substitutable nodes. Unlike superposition substitutability, there are close connections and constraints between entangled substitutable nodes, which support and condition each other in the energy transmission path. In the node screening process, for entangled substitutable nodes, the correlation influence between nodes is comprehensively considered, and they are retained or eliminated to ensure the integrity and efficiency of the energy transmission path.

[0039] First, the correlation analysis of equipment operation parameters was carried out. By calculating the correlation coefficients of parameters such as power consumption parameters, energy storage status, and grid connection status between different energy transfer nodes, the degree of correlation between nodes in equipment operation characteristics was found. The higher the correlation coefficient, the more synchronized the operation status between nodes, and the more similar their roles and positions in the energy transmission path, which is more likely to form an entangled substitutable relationship. Secondly, the synergy analysis of energy conversion indicators was carried out. By calculating the synergy coefficients between indicators such as energy conversion efficiency, energy conversion rate, and energy utilization rate of different energy transfer nodes, the degree of complementarity between nodes in energy conversion performance was found. The higher the synergy coefficient, the more the energy conversion performance between nodes can complement and promote each other, and the more they can form a mutually conditional and mutually supportive relationship in the energy transmission path, which is more likely to form an entangled substitutable relationship. When calculating the correlation coefficient and synergy coefficient, the dynamic time window and weighted average method were used, which not only considered the real-time changes of parameters and indicators, but also took into account the cumulative effect of historical data, so as to improve the accuracy and robustness of association relationship identification. By comprehensively analyzing the correlation of equipment operation parameters and the synergy of energy conversion indicators, the entangled substitutable characteristic identification mechanism is realized. For node pairs or node groups with high correlation coefficients and synergy coefficients, they are judged to have significant entanglement substitutability; while for node pairs or node groups with low correlation coefficients and synergy coefficients, they are judged to have no or only weak entanglement substitutability. In the node screening process, the key nodes in the node pairs or node groups with significant entanglement substitutability are preferentially retained, and the redundant or secondary nodes are eliminated. While maximizing the integrity and efficiency of the energy transmission path, the number of nodes is optimized and simplified, thereby obtaining the second type of screened energy transfer node set. The second type of screened energy transfer node set obtained after screening contains the most critical and representative energy transfer nodes under the principle of entanglement substitutability. These nodes play an indispensable supporting and connecting role in the energy transmission path, and have a decisive influence on ensuring the stability and reliability of energy transmission.

[0040] Through entanglement substitutability screening, the energy transmission path is further optimized while taking into account the node correlation, the robustness and adaptability of energy transmission are improved, and support is provided for subsequent energy path optimization.

[0041] S7: In the energy conversion link, by screening the first category energy transfer node set and the second category energy transfer node set, it is verified that under the operating conditions specified by the equipment operating parameters, the primary supply and demand path set is iteratively updated with the goal of maximizing the stability of the energy conversion link to obtain the optimal supply and demand path set.

[0042] Specifically, after obtaining the first type screening energy transfer node set and the second type screening energy transfer node set, the primary supply and demand path set is further optimized and improved to obtain the optimal supply and demand path set with maximized stability.

[0043] When iteratively updating the primary supply and demand path set, the optimization goal is to maximize the stability of the energy conversion link. The higher the stability, the better the matching degree between energy supply and demand, and the higher the efficiency and quality of energy transmission. In order to achieve the goal of maximizing stability, a constraint-based iterative optimization strategy is adopted. First, the first-class screening energy transfer node set and the second-class screening energy transfer node set are integrated into the primary supply and demand path set to form an extended candidate path set. Then, according to the various constraints defined by the equipment operation parameters, such as node power consumption limit, storage capacity limit, transmission capacity limit, etc., the candidate path set is gradually screened and optimized. In each iteration, the stability index of the current candidate path set is evaluated and compared with the result of the previous iteration. If the stability index is improved, the current candidate path set is used as the new primary supply and demand path set, and the next round of iteration is continued; if the stability index does not improve or decreases, the iteration is stopped, and the candidate path set of the previous round is used as the final optimal supply and demand path set.

[0044] In the process of iterative optimization, heuristic rules and strategies can be introduced to accelerate convergence and improve the quality of solution. For example, when evaluating stability indicators, different weight coefficients are assigned to different types of nodes and paths through weighted average to highlight their importance in energy transmission; for example, when screening paths, priority rules are set to focus on protecting and optimizing key nodes and key paths to ensure the stability of the energy transmission backbone network.

[0045] Through iterative optimization, under the constraints of equipment operating parameters, we can fully utilize the advantages of the first type of screened energy transfer node set and the second type of screened energy transfer node set, comprehensively improve and enhance the primary supply and demand path set, and obtain the optimal supply and demand path set with maximized stability, providing highly optimized scheduling solutions and control strategies for the actual operation of the energy router.

[0046] Furthermore, the embodiment of the present application also includes:

[0047] Connect to the energy demand end, monitor the grid connection status in real time, and obtain real-time monitoring parameters, where the real-time monitoring data includes grid frequency, voltage, and current; based on the real-time monitoring parameters, use a reinforcement learning algorithm to set a grid load balancing strategy; and use the grid load balancing strategy as the first constraint condition for iterative updating of the primary supply and demand path set.

[0048] In a feasible implementation, an energy conversion control method based on energy demand end optimization is provided, wherein each supply and demand path in the primary supply and demand path set has the energy demand end as the average path end point, and the power grid load balancing strategy is used as the path optimization constraint to further improve the efficiency and performance of energy conversion control.

[0049] First, a smart meter is installed at each energy demand end to measure key parameters such as grid frequency, voltage, and current in real time. The smart meter is connected to the communication gateway wirelessly or wired to form a local data acquisition network. The communication gateway is responsible for summarizing, processing, and reporting the data collected by the smart meter, and communicating with the energy router through wide area networks such as 4G / 5G and Ethernet to achieve remote transmission and interaction of data. Through the data acquisition architecture, real-time monitoring parameters of the energy demand end can be obtained efficiently and reliably, providing data support for subsequent policy settings.

[0050] Then, based on the real-time monitoring parameters of the energy demand side, an environmental model that reflects the real-time operation status of the power grid is constructed, including key indicators such as the load level, flow distribution, and voltage stability of each node. Subsequently, an intelligent agent model is designed, which uses neural networks and other methods to take the power grid environment status as input and output a load balancing strategy, such as the load scheduling plan for key nodes and the flow optimization direction. Next, by continuously interacting and simulating with the power grid environment, the intelligent agent model is trained and optimized using the reward and punishment mechanism of reinforcement learning, so that it can generate power grid load balancing strategies in various power grid operation scenarios. After that, the trained intelligent agent model is deployed to the energy router, so that it can dynamically generate and update the power grid load balancing strategy based on real-time monitoring data, and realize the intelligent and adaptive optimization of power grid scheduling.

[0051] After that, when updating the primary supply and demand path set each time, the first constraint condition is generated according to the current power grid load balancing strategy, such as the load upper limit of the key node, the power flow balance equation, etc. Then, the first constraint condition is used as the hard constraint of the optimization problem, and together with other optimization objectives, a path optimization model with constraints is constructed. Next, the path optimization model is solved to obtain a set of primary supply and demand path sets that meet the power grid load balancing constraints.

[0052] Furthermore, the embodiment of the present application also includes:

[0053] Connect to the energy supply end, use data mining algorithm to collect and extract real-time output data of renewable energy; combine the energy storage status, apply prediction algorithm to predict the output of renewable energy, and obtain predicted output data; based on the real-time output data and predicted output data, use optimization algorithm to set renewable energy integration strategy; use the renewable energy integration strategy as the second constraint condition for iterative update of the primary supply and demand path set.

[0054] In another feasible implementation, an energy conversion control method based on energy supply end optimization is provided, wherein each supply and demand path in the primary supply and demand path set is characterized in that each supply and demand path has the energy supply end as the average starting point, and a renewable energy integration strategy is used as a path optimization constraint to further promote the optimization of the primary supply and demand path set.

[0055] First, a group of intelligent sensors and data acquisition terminals are deployed at each energy supply end, such as wind farms and photovoltaic power stations, to monitor the output data of renewable energy in real time, such as wind speed, irradiance, and power generation. These sensors and acquisition terminals form an IoT network covering the energy supply end through low-power wide area network technologies such as Zigbee and LoRa. At the same time, multiple edge computing nodes are deployed in the IoT network to perform preliminary processing and analysis on the collected output data and extract key features and statistical indicators. Afterwards, the edge computing nodes upload the processed data to the energy center in the cloud through high-speed networks such as 5G and satellite communications, so that the energy router can perform subsequent strategy optimization. Through the data acquisition architecture of the Internet of Things and edge computing, real-time output data of the energy supply end can be obtained efficiently and at low cost, providing a data foundation for intelligent energy integration.

[0056] Then, first, according to the type and characteristics of the energy supply end, multiple deep neural network models are constructed, such as long short-term memory networks, convolutional neural networks, etc., to model the nonlinear relationship between the output of the energy supply end and various influencing factors. Subsequently, historical production data, meteorological data, geographical environment data, etc. are used as training samples, and the neural network model is trained and optimized using the back propagation algorithm, so that it can learn the general rules of production prediction from complex data patterns. In the application stage, the real-time collected production data, energy storage status, etc. are used as input, and forward reasoning is performed through the trained neural network model to obtain the predicted production data of the energy supply end in the future. Through the prediction method of deep learning, it is possible to fully explore and utilize the spatiotemporal correlation and nonlinear laws contained in the production data of the energy supply end, and improve the accuracy and timeliness of production prediction.

[0057] Subsequently, based on the real-time production data and predicted production data, an optimization model reflecting the balance of renewable energy supply and demand is constructed. The objective function includes maximizing the utilization rate of renewable energy, minimizing the dependence on traditional energy, minimizing the cost of energy storage, and other aspects. At the same time, various constraints are introduced into the optimization model, such as grid load balance constraints, energy storage equipment capacity constraints, and energy transmission loss constraints, to ensure the feasibility and robustness of the generated integration strategy. Then, the optimization model is solved using intelligent optimization algorithms, such as genetic algorithms and particle swarm optimization algorithms, to obtain the optimal solution set that can balance multiple objectives. Thereafter, multiple integration strategies are selected from the optimal solution set as the final solution for renewable energy integration. Through the strategy generation method of multi-objective optimization, it is possible to generate a renewable energy integration strategy that takes into account efficiency, economy, and reliability on the basis of fully considering the supply and demand of renewable energy, providing a decision-making basis for the green transformation of the energy system.

[0058] In the iterative update process of the primary supply and demand path set, the renewable energy integration strategy is used as the second constraint. At each iterative update, it is checked whether the current candidate path set meets the various indicators and requirements defined by the renewable energy integration strategy, such as the proportion of renewable energy, carbon emission intensity, etc. Only candidate paths that meet the second constraint can enter the next round of iteration; those that do not meet the second constraint will be directly eliminated. Through the second constraint, it can be ensured that the primary supply and demand path set always evolves and optimizes in the direction of meeting the renewable energy integration strategy during the iterative update process, and a highly green and sustainable energy transmission path solution is obtained.

[0059] Furthermore, the embodiment of the present application also includes:

[0060] The energy storage end is introduced to monitor the status of the energy storage device in real time. The status of the energy storage device includes the state of charge, charging and discharging efficiency, and health status. Based on the status of the energy storage device and the grid connection status, the charging and discharging strategy of the energy storage device is adaptively adjusted. The charging and discharging strategy includes a charging balance plan for off-peak periods and a discharging balance plan for peak periods. The charging and discharging strategy of the energy storage device is used as the first auxiliary factor for iterative updating of the primary supply and demand path set.

[0061] In a preferred embodiment, by introducing the energy storage end, the state of the energy storage device is monitored in real time, and based on the state of the energy storage device and the grid connection state, the charging and discharging strategy of the energy storage device is adaptively adjusted, and the charging and discharging strategy is used as an auxiliary factor for path optimization, thereby further improving the stability and reliability of the grid.

[0062] First, for each energy storage terminal, such as battery packs, flywheels, supercapacitors, etc., a group of intelligent sensors are installed to collect various state parameters of the energy storage terminal, including state of charge, charge and discharge efficiency, health status, etc. These sensors report the collected data to the monitoring center of the energy router in real time through the wireless Internet of Things network, so as to fully grasp the operating status of the energy storage equipment and provide a reliable decision-making basis for adaptive adjustment of the charge and discharge strategy. Then, based on the state of the energy storage equipment and the grid connection status, a Markov decision process model for energy scheduling is constructed, and the electricity price, load demand, renewable energy supply, etc. in different time periods are used as environmental states, the charge and discharge power of the energy storage equipment is used as the decision behavior, and the grid operation cost, renewable energy utilization rate, etc. are used as reward functions. Using the deep reinforcement learning algorithm, the optimal charge and discharge strategy is obtained through continuous interaction with the environment and trial and error learning. After that, the obtained charge and discharge strategy is converted into a set of specific scheduling schemes, including a charging balance scheme during the valley period and a discharge balance scheme during the peak period. Through adaptive strategy adjustment, the operation mode of the energy storage end can be dynamically optimized according to the real-time status of the energy storage equipment and the grid connection status, maximizing its role in peak-loading and valley-filling and smoothing fluctuations in the grid, thereby improving the overall stability and economy of the grid.

[0063] Subsequently, the charging and discharging strategy of the energy storage device is integrated into the path optimization process as the first auxiliary factor and incorporated into the iterative update of the primary supply and demand path set. Specifically, in each iteration, the charging and discharging strategy is used as the first auxiliary factor based on the current state of the energy storage device and the charging and discharging strategy, and together with other constraints, it affects the iterative update of the primary supply and demand path set, thereby fully considering the real-time state and dispatching capacity of the energy storage device, while ensuring the feasibility of the charging and discharging strategy, optimizing the path selection, and improving the flexibility and reliability of energy transmission. At the same time, by dynamically coordinating the first auxiliary factor with other constraints, the coordinated optimization of energy storage and paths can be achieved, avoiding the unbalanced situation of sufficient energy storage and limited paths or sufficient paths and insufficient energy storage, thereby comprehensively improving the operation performance and response capabilities of the power grid.

[0064] Furthermore, the embodiment of the present application also includes:

[0065] A real-time data analysis algorithm is used to monitor and identify supply and demand imbalance periods in real time, wherein the supply and demand imbalance periods correspond to grid frequency fluctuations, voltage anomalies, and current mutations. During the supply and demand imbalance periods, fuzzy logic is used to adjust the energy router to determine an immediate supply and demand adjustment strategy, wherein the immediate supply and demand adjustment strategy includes sacrificing the energy conversion efficiency corresponding to the energy utilization rate and sacrificing the energy conversion rate corresponding to the energy utilization rate. The immediate supply and demand adjustment strategy is used as a second auxiliary factor for iterative updating of the primary supply and demand path set.

[0066] In a preferred embodiment, based on the real-time monitoring of the status of energy storage equipment, the identification of supply and demand imbalance periods and the generation of immediate supply and demand adjustment strategies are further introduced, and the immediate supply and demand adjustment strategies are used as the second auxiliary factor of path optimization to alleviate the supply and demand imbalance to the greatest extent and ensure the stable operation of the power grid.

[0067] First, high-frequency sampling smart meters and data acquisition terminals are deployed at key nodes of the power grid (such as substations, distribution rooms, etc.) to obtain the operating parameters of the power grid in real time, such as frequency, voltage, current, etc. Then, the collected real-time data is aggregated to the edge computing node of the energy router in the form of a stream, and the stream data analysis platform is used for online processing and analysis. During the analysis process, by setting anomaly detection rules and thresholds, it is possible to determine in real time whether the power grid is in a state of supply and demand imbalance, and identify specific imbalance periods and abnormal indicators, such as frequency fluctuations, voltage anomalies, and current mutations. Once a supply and demand imbalance is detected, the alarm and decision-making process is immediately triggered, and the generation stage of the immediate supply and demand adjustment strategy is entered. Through the anomaly detection method of data analysis, the supply and demand imbalance problem of the power grid can be discovered in a timely and accurate manner, which can save time for rapid response and disposal.

[0068] In order to quickly generate effective instant supply and demand adjustment strategies during the supply and demand imbalance period, a set of fuzzy control rules are first predefined according to the device status and capabilities of the energy router, such as "If the frequency deviation is NB and the voltage deviation is PB, the energy conversion efficiency is adjusted to NS", etc., where NB, PB, and NS are predefined fuzzy language values, which respectively represent the frequency being too negative, the voltage being too positive, and the energy conversion efficiency being too low. Then, according to the specific abnormal indicators identified during the supply and demand imbalance period, such as frequency deviation and voltage deviation, specific control instructions are obtained through fuzzification, reasoning, and defuzzification, such as reducing the energy utilization rate of a certain path and increasing the energy conversion rate of a certain device, as instant supply and demand adjustment strategies, including sacrificing the energy conversion efficiency corresponding to the energy utilization rate and sacrificing the energy conversion rate corresponding to the energy utilization rate. In the process of generating strategies, a dynamic trade-off is made between energy utilization and conversion efficiency by setting fuzzy membership functions and reasoning rules, so as to minimize the impact on the supply and demand balance while ensuring the stability of the power grid.

[0069] Subsequently, the instant supply and demand adjustment strategy is incorporated into the iterative update of the primary supply and demand path set as the second auxiliary factor. In each iteration, it is first determined whether the current supply and demand imbalance period is in progress. If so, the generation process of the instant supply and demand adjustment strategy is triggered to obtain targeted path adjustment instructions; if not, the conventional path optimization process is directly entered. In the process of path optimization, the instructions generated by the instant supply and demand adjustment strategy are used as new constraints, together with other constraints, to jointly guide the iterative update of the primary supply and demand path set, thereby avoiding the problem of supply and demand imbalance.

[0070] Furthermore, the embodiment of the present application also includes:

[0071] Based on the energy router, a multi-energy supply and demand parameter set is collected, and the multi-energy supply and demand parameter set includes an electric energy supply and demand parameter subset, a thermal energy supply and demand parameter subset, a cold energy supply and demand parameter subset, and an operating status of an energy conversion device; an optimization starting point is set based on a primary supply and demand path set, and optimization is performed through the first-category screening energy transfer node set and the second-category screening energy transfer node set; based on the first auxiliary factor and the second auxiliary factor, multi-energy coordination and decentralization are performed in combination with the multi-energy supply and demand parameter set.

[0072] In a feasible implementation, with the goal of maximizing the stability of the energy conversion link, the primary supply and demand path set is iteratively updated by collecting multi-energy supply and demand parameters, introducing a screened set of energy transfer nodes, integrating auxiliary optimization factors, etc., thereby enhancing the resilience and flexibility of energy supply and demand.

[0073] First, the supply and demand status and equipment operation status of the multi-energy system are comprehensively collected and perceived, and the integrated perception and unified management of multi-source heterogeneous energy data are realized by deploying a multi-energy data collection and fusion module in the energy router. Specifically, this module establishes communication connections with various intelligent sensors, intelligent meters, energy management systems, etc., collects supply and demand data of various forms of energy such as electric energy, thermal energy, and cold energy, as well as the operation status data of related energy conversion equipment, and divides them into electric energy supply and demand parameter subsets, thermal energy supply and demand parameter subsets, cold energy supply and demand parameter subsets, etc. according to energy types and attributes, thereby obtaining a multi-energy supply and demand parameter set. Through classification and integration, the real-time status of the multi-energy system can be fully grasped, providing an information basis for subsequent collaborative optimization.

[0074] Then, based on the primary supply and demand path set, the first type of screening energy transfer node set and the second type of screening energy transfer node set are introduced to construct a collaborative optimization solution framework. Specifically, the primary supply and demand path set is taken as the optimization starting point and regarded as the initial energy transmission and conversion scheme; the first type of screening energy transfer node set and the second type of screening energy transfer node set are respectively used as two different levels of optimization variables, and combined optimization is performed through intelligent algorithms (such as evolutionary algorithms, swarm intelligence algorithms, etc.) to search for the optimal energy transmission path and conversion scheme. In the optimization process, by defining the fitness function, multiple optimization objectives such as energy transmission efficiency, energy conversion efficiency, and load balance are comprehensively considered, and the stability of the energy conversion link is maximized as the ultimate goal to guide the algorithm search convergence. In order to further improve the accuracy and effect of multi-energy collaborative optimization, on the basis of optimization solution, the energy storage equipment charging and discharging strategy (first auxiliary factor) and the instant supply and demand adjustment strategy (second auxiliary factor) are integrated, and the multi-energy supply and demand parameter set is combined to realize the decentralized control of multi-energy coordination. Specifically, in each optimization iteration, the first auxiliary factor and the second auxiliary factor are quantified into corresponding optimization constraints or optimization variables, and are included in the collaborative optimization process together with the multi-energy supply and demand parameter set. In this way, when searching for energy transmission paths and formulating energy conversion plans, the regulation capabilities and emergency control measures of energy storage equipment can be fully considered, making the optimization results closer to reality and more flexible. After obtaining the optimization results, they are decoupled into control instructions for each sub-energy system and sent to the corresponding energy routing equipment and energy conversion equipment. According to local status and demand, coordinated control is performed autonomously to achieve decentralized multi-energy collaborative optimization, which can not only give full play to the global scheduling capabilities of the energy router, but also fully mobilize the autonomy and enthusiasm of each subsystem, thereby improving the operating efficiency and reliability of the multi-energy system.

[0075] Furthermore, the embodiment of the present application also includes:

[0076] Based on the multi-energy supply and demand parameter set, a multi-energy supply and demand model is constructed; based on the multi-energy supply and demand model, a coordination optimization function is set, and the initial competition threshold of the energy supply end is configured in accordance with the stability of the energy conversion link; based on the initial competition threshold of the energy supply end, multi-energy coordination and decentralization is performed.

[0077] In a preferred embodiment, first, based on the multi-energy supply and demand parameter set, a multi-energy supply and demand model is constructed. The model adopts a modeling method based on energy hubs, abstracts the supply and demand of different forms of energy such as electricity, heat, and cold energy into energy hubs, and uses a directed graph to represent the topological connection relationship. At each energy hub, the energy flow characteristics and conversion characteristics of the hub are described by defining corresponding energy balance equations, energy conversion equations, energy loss equations, etc. Between hubs, the energy interactions and influences between different hubs are described by defining energy transmission equations, energy coupling equations, etc. Through modular and hierarchical modeling methods, the supply and demand characteristics of multi-energy systems can be comprehensively and finely characterized, providing a general analysis framework for subsequent coordinated optimization.

[0078] Then, based on the multi-energy supply and demand model, a coordination optimization function is designed, and the initial competition threshold of the energy supply end is configured in accordance with the stability of the energy conversion link. Specifically, the coordination optimization function is a multi-objective optimization function, which comprehensively considers indicators such as energy utilization efficiency, economic cost, environmental benefits, etc., and takes the stability of the energy conversion link as the most important optimization goal. In mathematical form, the function is expressed as the weighted sum of multiple sub-objective functions. In the solution process, by adjusting the weight coefficients of each sub-objective function, the relationship between different optimization objectives can be flexibly balanced to achieve coordinated optimization of the multi-energy system. Among them, each energy hub defines its own local coordination optimization function according to its own characteristics and needs, as long as the local coordination optimization function meets the global stability requirements as a whole. Through distributed optimization methods, the autonomy and flexibility of each energy hub can be fully utilized, avoiding the inefficiency and fragility of centralized optimization.

[0079] On the basis of the coordinated optimization function, the initial competition threshold of the energy supply end is configured according to the stability requirements of the energy conversion link. The so-called competition threshold refers to the upper limit of the energy demand of each energy supply end energy hub competing with each other during the multi-energy coordinated optimization process. When the energy supply of a certain energy supply end exceeds its competition threshold, it means that the energy supply end is at a disadvantage in the competition and needs to reduce the energy supply to give more energy demand to other energy supply ends. On the contrary, when the energy supply of a certain energy supply end does not reach its competition threshold, it means that the energy supply end is at an advantage in the competition and can increase the energy supply to strive for more energy demand. By configuring the initial competition threshold of the energy supply end, the competition pattern of multi-energy supply is balanced, and the problem of over-concentration or over-dispersion of energy supply is avoided, thereby improving the stability of the energy conversion link.

[0080] Afterwards, based on the initial competition threshold of the energy supply end, the decentralized optimization of multi-energy coordination is realized through the competition adventure algorithm. Specifically, the competition adventure algorithm is a distributed optimization algorithm based on game theory and reinforcement learning. It realizes the distributed optimization of the entire system by simulating the competition and risk-taking behavior between multiple intelligent agents (i.e., energy hubs). In the initial stage of the algorithm, each energy supply end randomly generates an energy supply strategy as the initial solution according to its initial competition threshold. Then, in each round of iteration, each energy supply end competes with other energy supply ends for energy according to the current energy supply strategy and competition threshold, and strives for more energy demand and improves its own benefits by constantly adjusting its supply strategy. In the competition process, each energy supply end dynamically updates its own competition threshold according to its own competition results and system feedback, and continuously optimizes its own competition strategy by taking appropriate risks (raising the threshold) or being conservative (lowering the threshold). After multiple rounds of iterations, the energy supply strategies of each energy supply end will gradually converge to the equilibrium point, that is, all participants cannot improve their benefits by unilaterally changing their strategies, that is, a distributed optimization solution for the multi-energy system is obtained, which can achieve coordinated optimization of the entire system while satisfying the interests of each energy supply end.

[0081] In summary, the energy conversion control method of an energy router provided in the embodiment of the present application has the following technical effects:

[0082] Connect to the energy router to obtain equipment operating parameters, including power consumption parameters, energy storage status, and grid connection status. By obtaining the real-time operating data of the energy router, basic information and constraints are provided for subsequent energy conversion control.

[0083] Based on the energy conversion link of the energy router, energy conversion indicators are set. The energy conversion indicators include energy conversion efficiency, energy conversion rate, and energy utilization rate, providing optimization goals and evaluation criteria for energy conversion control. Based on the energy conversion link, the long-term dependent energy supply and demand connection relationship is captured, and the primary supply and demand path set is obtained. Each supply and demand path in the primary supply and demand path set takes the energy supply end as the average path starting point and the energy demand end as the average path ending point. By analyzing the supply and demand relationship in the energy conversion link, the initial supply and demand path set is generated, laying the foundation for subsequent path optimization. Based on the energy conversion link, the primary supply and demand path set is highlighted and marked, and the energy transfer node set is collected. The energy transfer node set does not include the average path starting point and average path ending point corresponding to each supply and demand path in the primary supply and demand path set. By marking and collecting energy transfer nodes, the key nodes in the supply and demand path are identified to prepare for node screening. Based on the energy transfer node set, combined with the equipment operating parameters and energy conversion indicators, the energy transfer nodes are screened by superposition and substitution to obtain the first type of screened energy transfer node set. Through the superposition and substitution screening strategy, a type of key nodes are selected from the energy transfer node set to optimize the supply and demand path. Based on the energy transfer node set, combined with the equipment operating parameters and energy conversion indicators, the energy transfer nodes are screened by entanglement and substitution to obtain the second type of screened energy transfer node set. Through the entanglement and substitution screening strategy, another type of key nodes are selected from the energy transfer node set to optimize the supply and demand path. In the energy conversion link, through the first type of screening energy transfer node set and the second type of screening energy transfer node set, it is verified that under the operating conditions limited by the equipment operating parameters, the primary supply and demand path set is iteratively updated with the goal of maximizing the stability of the energy conversion link, and the optimal supply and demand path set is obtained. The optimization of the supply and demand path is completed, achieving the technical effect of realizing intelligent and precise energy conversion control of energy routers under complex energy conversion links and obtaining the optimal supply and demand path with maximized stability.

[0084] Embodiment 2, based on the same inventive concept as the energy conversion control method of an energy router in the above embodiment, Figure 2 As shown, an embodiment of the present application provides an energy conversion control system for an energy router, the system comprising:

[0085] The parameter acquisition module 11 is used to connect to the energy router and acquire the device operation parameters, which include power consumption parameters, energy storage status and grid connection status;

[0086] An indicator setting module 12, used to set energy conversion indicators based on the energy conversion link of the energy router, wherein the energy conversion indicators include energy conversion efficiency, energy conversion rate, and energy utilization rate;

[0087] A relationship capturing module 13 is used to capture the long-term dependent energy supply and demand connection relationship based on the energy conversion link, and obtain a primary supply and demand path set, wherein each supply and demand path in the primary supply and demand path set has an energy supply end as a path average starting point and an energy demand end as a path average end point;

[0088] A node collection module 14 is used to highlight the primary supply and demand path set based on the energy conversion link and collect an energy transfer node set, wherein the energy transfer node set does not include the average path starting point and average path end point corresponding to each supply and demand path in the primary supply and demand path set;

[0089] A superposition screening module 15 is used to screen the energy transfer nodes based on the energy transfer node set, in combination with the equipment operation parameters and the energy conversion index, by superposition and substitution, to obtain a first type of screened energy transfer node set;

[0090] An entanglement screening module 16 is used to screen the energy transfer nodes based on the energy transfer node set, in combination with the device operation parameters and the energy conversion index, using entanglement as a substitute to obtain a second type of screened energy transfer node set;

[0091] The path optimization module 17 is used to verify in the energy conversion link that under the operating conditions specified by the equipment operating parameters, the primary supply and demand path set is iteratively updated with the goal of maximizing the stability of the energy conversion link through the first category screening energy transfer node set and the second category screening energy transfer node set to obtain the optimal supply and demand path set.

[0092] Furthermore, the relationship capturing module 13 includes the following execution steps:

[0093] Connecting to the energy demand end, monitoring the grid connection status in real time, and acquiring real-time monitoring parameters, wherein the real-time monitoring data includes grid frequency, voltage, and current;

[0094] Based on the real-time monitoring parameters, a grid load balancing strategy is set using a reinforcement learning algorithm;

[0095] The power grid load balancing strategy is used as the first constraint condition for iterative updating of the primary supply and demand path set.

[0096] Furthermore, the relationship capturing module 13 further includes the following execution steps:

[0097] Connecting to the energy supply end, using data mining algorithms to collect and extract real-time production data of renewable energy;

[0098] In combination with the energy storage state, a prediction algorithm is applied to predict the output of renewable energy and obtain predicted output data;

[0099] Based on the real-time production data and the predicted production data, using an optimization algorithm, setting a renewable energy integration strategy;

[0100] The renewable energy integration strategy is used as a second constraint condition for iteratively updating the primary supply and demand path set.

[0101] Furthermore, the embodiment of the present application also includes an auxiliary factor module, which includes the following execution steps:

[0102] Introduce the energy storage end to monitor the status of the energy storage device in real time, including the state of charge, charging and discharging efficiency, and health status;

[0103] Based on the state of the energy storage device and the grid connection state, adaptively adjust the charging and discharging strategy of the energy storage device, wherein the charging and discharging strategy includes a charging balance solution during off-peak hours and a discharging balance solution during peak hours;

[0104] The charging and discharging strategy of the energy storage device is used as a first auxiliary factor for iterative updating of the primary supply and demand path set.

[0105] Furthermore, the auxiliary factor module also includes the following execution steps:

[0106] Use real-time data analysis algorithms to monitor and identify supply and demand imbalance periods in real time, which correspond to power grid frequency fluctuations, voltage anomalies, and current mutations;

[0107] During the supply-demand imbalance period, fuzzy logic adjusts the energy router to determine an immediate supply-demand adjustment strategy, wherein the immediate supply-demand adjustment strategy includes sacrificing the energy conversion efficiency corresponding to the energy utilization rate and sacrificing the energy conversion rate corresponding to the energy utilization rate;

[0108] The instant supply and demand adjustment strategy is used as a second auxiliary factor for iteratively updating the primary supply and demand path set.

[0109] Furthermore, the path optimization module 17 includes the following execution steps:

[0110] Based on the energy router, a multi-energy supply and demand parameter set is collected, wherein the multi-energy supply and demand parameter set includes an electric energy supply and demand parameter subset, a thermal energy supply and demand parameter subset, a cold energy supply and demand parameter subset, and an operating state of an energy conversion device;

[0111] An optimization starting point is set based on the primary supply and demand path set, and optimization is performed through the first type of screening energy transfer node set and the second type of screening energy transfer node set;

[0112] Based on the first auxiliary factor and the second auxiliary factor, multi-energy coordination and decentralization are performed in combination with the multi-energy supply and demand parameter set.

[0113] Furthermore, the path optimization module 17 also includes the following execution steps:

[0114] Based on the multi-energy supply and demand parameter set, construct a multi-energy supply and demand model;

[0115] Based on the multi-energy supply and demand model, a coordination optimization function is set, and in accordance with the stability of the energy conversion link, an initial competition threshold of the energy supply end is configured;

[0116] Based on the initial competition threshold of the energy supply end, multi-energy coordination and decentralization are carried out.

[0117] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0118] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these changes and variations.

Claims

1. An energy conversion control method for an energy router, characterized in that: The method comprises: Connecting to an energy router to obtain device operating parameters, including power consumption parameters, energy storage status, and grid connection status; Based on the energy conversion link of the energy router, setting energy conversion indicators, the energy conversion indicators include energy conversion efficiency, energy conversion rate, and energy utilization rate; Based on the energy conversion link, the long-term dependent energy supply and demand connection relationship is captured to obtain a primary supply and demand path set, wherein each supply and demand path in the primary supply and demand path set has an energy supply end as a path average starting point and an energy demand end as a path average end point; Based on the energy conversion link, the primary supply and demand path set is highlighted and marked, and an energy transfer node set is collected, wherein the energy transfer node set does not include the average path starting point and the average path ending point corresponding to each supply and demand path in the primary supply and demand path set; Based on the energy transfer node set, combined with the equipment operation parameters and energy conversion indicators, the energy transfer nodes are screened by superposition and substitution to obtain a first type of screened energy transfer node set, wherein superposition and substitution means that two or more energy transfer nodes can replace each other in function, and the replacement does not affect the energy transmission efficiency and quality; Based on the energy transfer node set, combined with the device operation parameters and energy conversion indicators, the energy transfer nodes are screened by entanglement substitutability to obtain a second type of screened energy transfer node set, wherein entanglement substitutability means that two or more energy transfer nodes are functionally related and complementary, and replacing any one node will affect the performance of other nodes; In the energy conversion link, by screening the first category energy transfer node set and the second category energy transfer node set, it is verified that under the operating conditions specified by the equipment operating parameters, the primary supply and demand path set is iteratively updated with the goal of maximizing the stability of the energy conversion link to obtain the optimal supply and demand path set.

2. The energy conversion control method of an energy router according to claim 1, characterized in that: Each supply and demand path of the primary supply and demand path set has the energy demand end as the average end point, and the method includes: Connecting to the energy demand end, monitoring the grid connection status in real time, and acquiring real-time monitoring parameters, wherein the real-time monitoring parameters include grid frequency, voltage, and current; Based on the real-time monitoring parameters, a grid load balancing strategy is set using a reinforcement learning algorithm; The power grid load balancing strategy is used as the first constraint condition for iterative updating of the primary supply and demand path set.

3. The energy conversion control method of an energy router according to claim 2, characterized in that: Each supply and demand path in the primary supply and demand path set is based on the energy supply end as the average starting point, and the method includes: Connecting to the energy supply end, using data mining algorithms to collect and extract real-time production data of renewable energy; In combination with the energy storage state, a prediction algorithm is applied to predict the output of renewable energy and obtain predicted output data; Based on the real-time production data and the predicted production data, using an optimization algorithm, setting a renewable energy integration strategy; The renewable energy integration strategy is used as a second constraint condition for iteratively updating the primary supply and demand path set.

4. The energy conversion control method of an energy router according to claim 3, characterized in that: The method comprises: Introduce the energy storage end to monitor the status of the energy storage device in real time, including the state of charge, charging and discharging efficiency, and health status; Based on the state of the energy storage device and the grid connection state, adaptively adjust the charging and discharging strategy of the energy storage device, wherein the charging and discharging strategy includes a charging balance solution during off-peak hours and a discharging balance solution during peak hours; The charging and discharging strategy of the energy storage device is used as a first auxiliary factor for iterative updating of the primary supply and demand path set.

5. The energy conversion control method of an energy router according to claim 4, characterized in that: Real-time monitoring of the state of the energy storage device, the method further comprising: Use real-time data analysis algorithms to monitor and identify supply and demand imbalance periods in real time, which correspond to power grid frequency fluctuations, voltage anomalies, and current mutations; During the supply-demand imbalance period, fuzzy logic adjusts the energy router to determine an immediate supply-demand adjustment strategy, wherein the immediate supply-demand adjustment strategy includes sacrificing the energy conversion efficiency corresponding to the energy utilization rate and sacrificing the energy conversion rate corresponding to the energy utilization rate; The instant supply and demand adjustment strategy is used as a second auxiliary factor for iteratively updating the primary supply and demand path set.

6. The energy conversion control method of an energy router according to claim 5, characterized in that: The primary supply and demand path set is iteratively updated with the goal of maximizing the stability of the energy conversion link, the method comprising: Based on the energy router, a multi-energy supply and demand parameter set is collected, wherein the multi-energy supply and demand parameter set includes an electric energy supply and demand parameter subset, a thermal energy supply and demand parameter subset, a cold energy supply and demand parameter subset, and an operating status of an energy conversion device; An optimization starting point is set based on the primary supply and demand path set, and optimization is performed through the first type of screening energy transfer node set and the second type of screening energy transfer node set; Based on the first auxiliary factor and the second auxiliary factor, multi-energy coordination and decentralization are performed in combination with the multi-energy supply and demand parameter set.

7. The energy conversion control method of an energy router according to claim 6, characterized in that: Combining the multi-energy supply and demand parameter set to perform multi-energy coordinated decentralization, the method includes: Based on the multi-energy supply and demand parameter set, construct a multi-energy supply and demand model; Based on the multi-energy supply and demand model, a coordination optimization function is set, and in accordance with the stability of the energy conversion link, an initial competition threshold of the energy supply end is configured; Based on the initial competition threshold of the energy supply end, multi-energy coordination and decentralization are carried out.

8. An energy conversion control system for an energy router, characterized in that: A method for controlling energy conversion of an energy router according to any one of claims 1 to 7, the system comprising: A parameter acquisition module, which is used to connect to the energy router and acquire device operating parameters, including power consumption parameters, energy storage status, and grid connection status; An indicator setting module, the indicator setting module is used to set energy conversion indicators based on the energy conversion link of the energy router, the energy conversion indicators include energy conversion efficiency, energy conversion rate, and energy utilization rate; A relationship capture module, the relationship capture module is used to capture the long-term dependent energy supply and demand connection relationship based on the energy conversion link, and obtain a primary supply and demand path set, wherein each supply and demand path in the primary supply and demand path set has an energy supply end as a path average starting point and an energy demand end as a path average end point; A node collection module, the node collection module is used to highlight the primary supply and demand path set based on the energy conversion link, and collect an energy transfer node set, wherein the energy transfer node set does not include the average path starting point and the average path end point corresponding to each supply and demand path in the primary supply and demand path set; A superposition screening module, wherein the superposition screening module is used to screen the energy transfer nodes based on the energy transfer node set, in combination with the equipment operation parameters and the energy conversion index, to obtain a first type of screened energy transfer node set, wherein superposition substitutability means that two or more energy transfer nodes are functionally interchangeable, and the replacement does not affect the energy transmission efficiency and quality; An entanglement screening module, the entanglement screening module is used to screen the energy transfer nodes based on the energy transfer node set, in combination with the device operation parameters and energy conversion indicators, to obtain a second type of screened energy transfer node set, wherein entanglement substitutability means that two or more energy transfer nodes are functionally related and complementary, and replacing any one node will affect the performance of other nodes; A path optimization module, wherein the path optimization module is used to iteratively update the primary supply and demand path set in the energy conversion link by screening the energy transfer node set of the first category and the energy transfer node set of the second category, and to obtain the optimal supply and demand path set under the operating conditions specified by the equipment operating parameters with the goal of maximizing the stability of the energy conversion link.

Images