Optical storage and charging integrated interaction management system and method based on distributed architecture

Abstract

The invention discloses an optical storage and charging integrated interaction management system and method based on a distributed architecture, and relates to the technical field of energy interaction management, and the system comprises a multi-source data processing module, an energy supply and demand analysis module, and an energy management decision module. The multi-source data processing module is used for processing data of each station in the optical storage and charging system and drawing an optical storage and charging energy sharing path diagram; the energy supply and demand analysis module is used for predicting energy consumption expectations of the users for the second-class nodes according to the traffic historical data of the users, training an energy supply prediction model according to the functional characteristic parameters of the first-class nodes, predicting the energy supply quantity, and analyzing pre-stored energy consumption demands of the third-class nodes according to the energy consumption volatility of the second-class nodes; and the energy management decision module updates the node weight in the optical storage and charging energy sharing path diagram in real time according to various node function characteristic parameter monitoring data, and manages and optimizes the energy circulation strategy in the optical storage and charging system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy interaction management, and particularly to an integrated light storage and charging interaction management system and method based on a distributed architecture. Background Art

[0002] With the rapid development of new energy technologies, integrated light storage and charging systems have gradually become an important part of smart grids. However, the following technical deficiencies still exist in the actual application of existing technologies: On the one hand, current integrated light storage and charging systems mostly adopt a centralized management architecture, relying on a single control center for data collection and energy scheduling. There are problems such as high data processing latency and concentrated communication load, making it difficult to meet the scale expansion requirements of distributed architectures. On the other hand, using a static global model to predict the energy supply of photovoltaic modules and charging energy consumption requirements in the entire region, due to the inconsistency and dynamic variability of data deviations between sites in a distributed multi-site scenario, it is inevitable that the prediction results are disconnected from the actual situation. In addition, in the energy storage link of existing integrated light storage and charging systems, redundant energy storage is usually carried out according to the health status of energy storage batteries. In the charging link, a fixed ratio of reserve is usually used for pre-storage. For example, in a charging station, an additional percentage of energy is pre-stored using energy storage batteries for backup, resulting in the absence of the energy storage link in the energy scheduling of the integrated light storage and charging system architecture and causing unnecessary transmission energy consumption during energy storage reuse. Therefore, an integrated light storage and charging interaction management system and method based on a distributed architecture are needed to solve the above technical deficiencies. Summary of the Invention

[0003] The purpose of the present invention is to provide an integrated light storage and charging interaction management system and method based on a distributed architecture to solve the problems raised in the existing technologies.

[0004] To achieve the above purpose, the present invention provides the following technical solutions: An integrated light storage and charging interaction management method based on a distributed architecture, the method comprising the following steps: Step S100: Obtain data of each site in the integrated light storage and charging system, divide each site into type-one nodes, type-two nodes, and type-three nodes according to functional attributes, and label characteristic parameters for each type of node; Step S200: Obtain energy sharing paths between each site, analyze the energy transfer efficiency between the nodes corresponding to each site, and draw an integrated light storage and charging energy sharing path diagram; Step S300: Obtain user information of users expected to pass through type-two nodes through a navigation software, and predict the energy consumption expectations of users for each type-two node according to the historical travel data of each user; Step S400: Obtain the functional characteristic parameter data of each type-I node, construct an energy supply parameter data set for each type-I node, and train an energy supply prediction model. Step S500: Monitor the functional characteristic parameters of each node in real time, predict the energy supply of type-I nodes and the energy consumption expectations of type-II nodes in the future time period, analyze the pre-stored energy consumption demand allocation strategy of type-III nodes, and optimize the energy sharing strategy among various types of nodes in the photovoltaic energy storage charging system.

[0005] In the above technical solution, the step S100 includes the following analysis steps: Step S101: Obtain the actual geographical orientation data and functional information of each site in the photovoltaic energy storage charging system. Step S102: Divide each site into nodes according to the functional attributes of each site. The nodes include type-I nodes, type-II nodes, and type-III nodes. Among them, the type-I node refers to a power generation site that generates energy through photovoltaic modules; the type-II node refers to a charging site that provides a charging function for users; the type-III node refers to an energy storage site that stores the remaining energy after the energy circulation and distribution of type-I nodes. Step S103: Label the characteristic parameters of each type of node. For any node x, the parameter label is: x[Type_x, Fun_x{f_1, f_2, …, f_n}]. Among them, Type_x is the functional attribute number of node x, Fun_x is the set of functional characteristic parameters of node x, and f_1, f_2, …, f_n are the functional characteristic parameters of node x. For type-I nodes, the functional characteristic parameters refer to the energy supply condition parameters for power generation by photovoltaic modules; for type-II nodes, the functional characteristic parameters refer to the user charging condition parameters; for type-III nodes, the functional characteristic parameters refer to the energy storage condition parameters. The photovoltaic energy storage charging system is divided into nodes according to the site function, simplifying the overall energy supply and demand analysis method in the traditional method into a distributed management with division of labor and cooperation, clarifying the node role division of labor, simplifying data processing, and reducing the centralized pressure of communication load.

[0006] In the above technical solution, the step S200 includes the following analysis steps: Step S201: Obtain the actual line information of energy transmission among each node in the photovoltaic energy storage charging system, and analyze the energy transmission efficiency among each node in combination with the functional characteristic parameters of each node. Step S202: Use the energy transmission efficiency among each node as the edge weight between nodes, and draw a photovoltaic energy storage charging energy sharing path diagram. For the node weights of each node, the energy supply of the photovoltaic module in the future time period is used as the node weight of each type-I node, the sum of the energy consumption expectations of all users in the future time period is used as the node weight of the type-II node, and the energy storage amount at the initial time point of the future time period is used as the node weight of the type-III node; By dynamically combining the physical network with the supply and demand prediction, an accurate mathematical expression of the energy flow is realized, and further, the actual efficiency of energy sharing among nodes in the system is refined and reflected.

[0007] In the above technical solution, the S300 includes the following analysis steps: Step S301: Obtain all user route information through the navigation software, and inductively store the passing historical data of the selected users at each user end. Screen all users passing through at least one type-II node, and obtain the passing historical data of the selected users; Step S302: Extract the passing line information, energy storage battery state parameters, and charging parameters in the passing historical data, construct a user passing and charging data set, and train a user charging demand prediction model; The input of the user charging demand prediction model is the user passing line information and the user energy storage battery state parameters, and the output is the probability of the user charging at each charging station on the line in the future time period and the expected energy consumption; Multiply the charging probability and the expected energy consumption of the user at each type-II node in the optical storage and charging system as the energy consumption expectation of the user for each type-II node; By analyzing the charging preferences of the target users on the user side, counting all the target user information that meets the conditions on the navigation software side, and then mapping it to the expected demand of each charging station, the user demand prediction based on time series in the traditional method is transformed into individual user preference-group user expected demand-station energy consumption demand expectation based on big data, making the final demand analysis and prediction results have a more scientific data analysis basis. At the same time, through the multi-layer distributed architecture, the operation process of each node in big data processing is effectively simplified, and the system operation pressure is significantly reduced.

[0008] In the above technical solution, the step S400 includes the following analysis steps: Step S401: Obtain the historical data of the energy supply working condition parameters of each type-I node in the optical storage and charging system; the energy supply working condition parameters include the equipment state parameters and environmental parameters of the photovoltaic modules at each type-I node; Step S402: For each type-I node, perform data cleaning and normalization on the historical data of the energy supply working condition parameters, and construct an energy supply parameter data set; Step S403: For each type-I node, use the energy supply parameter data set to train an energy supply prediction model at the node end; The input of the energy supply prediction model is the device status parameters of a certain type of node and the predicted data of environmental parameters at future time points, and the output is the energy supply volume data of a certain type of node in a future time period; On the energy supply side, the method of using independent models for each node is also adopted, effectively avoiding the problem of weakened sensitivity of local features caused by differences in objective factors such as equipment and environment of energy supply nodes. While improving the energy supply prediction accuracy of each node, a distributed computing architecture is also adopted to simplify the computing process of a single node, share the system data transmission and computing pressure, and improve the real-time and security of data.

[0009] In the above technical solution, the following analysis steps are included in step S500: Step S501: Real-time monitor the functional characteristic parameters of each type-I node in the photovoltaic-storage-charging system, use the energy supply prediction model corresponding to each type-I node to predict the energy supply volume of each type-I node in a future time period, and update the weights of all type-I nodes in the photovoltaic-storage-charging energy sharing path diagram; Step S502: Real-time obtain the user line information passing through at least one type-II node through the navigation software. When the number of users or the driving routes of users meeting the conditions change, use the user charging demand prediction model trained by each user terminal to analyze the energy consumption expectations of users for each type-II node, and then statistically calculate the sum of the energy consumption expectations of all users for each type-II node in a future time period as the weights of all type-II nodes in the photovoltaic-storage-charging energy sharing path diagram, and perform real-time update; Step S503: Calculate the comprehensive efficiency of energy sharing between various types of nodes according to the edge weights between nodes in the photovoltaic-storage-charging energy sharing path diagram; For any type-I node a and type-II node b, according to the formula: η_dis(a,b)=k_dis×η(a,b), calculate the comprehensive efficiency of a type-I node a distributing energy to a type-II node b; For any type-I node a and type-III node c, according to the formula: η_store(a,c)=k_store×η(a,c), calculate the comprehensive efficiency of a type-I node a storing energy to a type-III node c; For any type-II node b and type-III node c, according to the formula: η_share=k_share×η(c,b), calculate the comprehensive efficiency of energy sharing between a type-II node b and a type-III node c; For any type-III node c, take the ratio of the expected self-discharge stock of the type-III node c in a future time period to the energy storage amount at the initial time point as the storage comprehensive efficiency of the type-III node c; Step S504: Obtain the energy consumption deviation between the sum of the energy consumption expectations of all users and the actual energy consumption data of each type-II node in each historical time period, and then calculate the variance of the energy consumption deviation of each type-II node as the energy consumption volatility of each type-II node; For each type-II node, multiply the energy consumption volatility by the expected energy consumption of all users in the future time period as the pre-stored energy quantity of each type-II node. According to the comprehensive energy sharing efficiency between type-II nodes and type-III nodes, use the dynamic programming method to make a decision on the maximum pre-storage efficiency strategy, and take the decision result as the pre-stored energy consumption demand of each type-III node in the future time period; Step S505: Further calculate the energy surplus of each type-III node in the future time period according to the energy storage quantity of each type-III node at the initial time point of the future time period; If the value of the energy surplus is greater than or equal to 0, it means that the energy storage quantity of the type-III node at the initial time point of the future time period can cover the pre-stored energy consumption demand. Take the energy surplus greater than 0 as the distributable energy of the corresponding type-III node in the future time period, and set the energy replenishment demand to 0; If the value of the energy surplus is less than 0, it means that the energy storage quantity of the type-III node at the initial time point of the future time period cannot cover the pre-stored energy consumption demand. Take the absolute value of the energy surplus less than 0 as the energy replenishment demand of the corresponding type-III node in the future time period, and set the distributable energy to 0; Step S506: Take the energy supply of all type-I nodes in the future time period and the distributable energy of all type-III nodes as the energy input of the integrated photovoltaic energy storage and charging system, and take the expected energy consumption sum of all users of all type-II nodes in the future time period and the energy replenishment demand of all type-III nodes as the energy output of the integrated photovoltaic energy storage and charging system. Use the dynamic programming method to make a decision on the maximum comprehensive efficiency of energy sharing in the future time period of the integrated photovoltaic energy storage and charging system; Calculating the pre-storage demand based on the energy consumption volatility of type-II nodes, minimizing the energy storage cost and demand fluctuation risk, realizing the elastic sharing of energy storage resources, avoiding the isolated status of energy storage in traditional methods, improving the effective utilization rate of energy storage resources. At the same time, through the real-time optimization algorithm of double-layer dynamic programming, decoupling long-term risk control and short-term efficiency optimization, avoiding the overfitting problem of traditional single-stage planning, and significantly improving the stability and practicability of the system strategy decision.

[0010] An integrated photovoltaic energy storage and charging interactive management system based on a distributed architecture of the above technical solution. The system includes: a multi-source data processing module, an energy supply and demand analysis module, and an energy management decision module; The multi-source data processing module is used to process the data of each site in the optical storage charging system and draw an energy sharing path map for the optical storage charging system; the energy supply and demand analysis module predicts the energy consumption expectations of users for each secondary node according to the historical travel data of each user, trains an energy supply prediction model according to the functional characteristic parameters of the primary nodes, conducts energy supply quantity prediction, and also analyzes the pre-stored energy consumption demands of each tertiary node according to the energy consumption volatility of each secondary node; the energy management decision-making module monitors the data according to the functional characteristic parameters of each type of node, updates the node weights in the energy sharing path map of the optical storage charging system in real time, and manages and optimizes the energy circulation strategy in the optical storage charging system.

[0011] In the above technical solution, the multi-source data processing module includes: a system site data processing unit, a functional characteristic parameter processing unit, and an energy sharing path analysis unit; The system site data processing unit obtains the data of each site in the optical storage charging system and divides each site into nodes according to the functional attributes; the functional characteristic parameter processing unit marks the functional characteristic parameters of each type of node respectively; the energy sharing path analysis unit analyzes the energy transfer efficiency between the nodes corresponding to each site according to the energy sharing path between each site, and draws an energy sharing path map for the optical storage charging system.

[0012] In the above technical solution, the energy supply and demand analysis module includes: an energy supply analysis unit, an energy demand analysis unit, and a pre-stored demand analysis unit; The energy supply analysis unit trains an energy supply prediction model according to the functional characteristic parameters of the primary nodes and conducts energy supply quantity prediction; the energy demand analysis unit predicts the energy consumption expectations of users for each secondary node according to the historical travel data of each user; the pre-stored demand analysis unit analyzes the pre-stored energy consumption of each secondary node according to the energy consumption volatility of each secondary node.

[0013] In the above technical solution, the energy management decision-making module includes: a comprehensive efficiency analysis unit, a pre-stored energy consumption demand allocation unit, and an energy integration interaction management unit; The comprehensive efficiency analysis unit calculates the comprehensive efficiency of energy sharing between each type of node according to the edge weights between the nodes in the energy sharing path map of the optical storage charging system; the pre-stored energy consumption demand allocation unit uses the dynamic programming method to allocate the pre-stored energy consumption of each secondary node to each tertiary node with the maximum pre-stored efficiency strategy decision; the energy integration interaction management unit uses the dynamic programming method to make the maximum comprehensive efficiency decision on the energy sharing in the future time period of the optical storage charging integrated system.

[0014] Compared with the prior art, the beneficial effects of the present invention are: In the present invention, a bilateral distributed architecture is adopted for the photovoltaic energy storage charging system, and distributed operations are carried out on the energy supply and demand analysis of the system. Site-independent models and system architectures of individual user preferences - group user predicted demands - site energy consumption demand expectations are respectively adopted to decouple data analysis and energy scheduling, effectively avoiding problems such as high data processing latency and concentrated communication load in traditional centralized architectures, and effectively improving the potential for system scale expansion; In the present invention, a multi-level distributed data analysis method is adopted to refine and process the operation data of different nodes in the system, improving the sensitivity of the system to local feature changes of each node, thereby realizing personalized analysis of various nodes of the photovoltaic energy storage charging system, effectively improving the accuracy of energy supply and demand analysis, and providing a scientific and practical data analysis basis for the final integrated energy interaction management; In the present invention, a two-layer dynamic programming method is adopted to balance long-term energy storage planning and short-term energy sharing efficiency, minimize energy storage costs and demand fluctuation risks, improve the accuracy of energy scheduling strategy analysis, and ensure the risk resistance of the implementation of the final strategy. While avoiding the isolation of energy storage in traditional methods, ensuring the sharing efficiency of energy storage resources, and further improving the efficiency of integrated energy interaction management of the photovoltaic energy storage charging system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flowchart of a method for integrated photovoltaic energy storage charging interaction management based on a distributed architecture according to the present invention; Figure 2 is an organizational structure diagram of a system for integrated photovoltaic energy storage charging interaction management based on a distributed architecture according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Embodiment: Please refer to Figure 1 - Figure 2 The present invention provides the following technical solutions: As Figure 1 shown, the present invention provides a method for integrated photovoltaic energy storage charging interaction management based on a distributed architecture, and the method includes the following steps: Step S100: Obtain data of each site in the photovoltaic energy storage charging system, divide each site into type I nodes, type II nodes, and type III nodes according to functional attributes, and label characteristic parameters for each type of node; Step S200: Obtain the energy sharing paths between stations, analyze the energy transfer efficiency between the nodes corresponding to each station, and draw an energy sharing path diagram for the photovoltaic energy storage and charging system; Step S300: Obtain user information of users who are expected to pass through secondary nodes through a navigation software, and predict the energy consumption expectations of users for each secondary node according to the historical travel data of each user; Step S400: Obtain the functional characteristic parameter data of each primary node, construct an energy supply parameter data set for each primary node, and train an energy supply prediction model; Step S500: Monitor the functional characteristic parameters of each node in real time, predict the energy supply of primary nodes and the energy consumption expectations of secondary nodes in the future time period, analyze the pre-stored energy consumption demand distribution strategy of tertiary nodes, and optimize the energy sharing strategy between various types of nodes in the photovoltaic energy storage and charging system.

[0018] In the above technical solution, the following analysis steps are included in step S100: Step S101: Obtain the actual geographical orientation data and functional information of each station in the photovoltaic energy storage and charging system; Step S102: Divide each station into nodes according to the functional attributes of each station. The nodes include primary nodes, secondary nodes, and tertiary nodes; Among them, the primary node refers to a power generation station that generates energy through photovoltaic modules, the secondary node refers to a charging station that provides a charging function for users, and the tertiary node refers to an energy storage station that stores the remaining energy after the energy circulation and distribution of the primary node; Step S103: Label the characteristic parameters of each type of node. For any node x, the parameter label is: x[Type_x, Fun_x{f_1, f_2, …, f_n}]; Among them, Type_x is the functional attribute number of node x, Fun_x is the set of functional characteristic parameters of node x, and f_1, f_2, …, f_n are the functional characteristic parameters of node x; For primary nodes, the functional characteristic parameters refer to the energy supply condition parameters for power generation by photovoltaic modules; for secondary nodes, the functional characteristic parameters refer to the user charging condition parameters; for tertiary nodes, the functional characteristic parameters refer to the energy storage condition parameters; In specific implementation, various types of stations involved in the photovoltaic energy storage and charging system are divided into power generation stations, charging stations, and energy storage stations, the division of labor of different stations is clarified, and a personalized analysis method is adopted; monitor the operating status data of photovoltaic modules in power generation stations, the status parameters of electrical equipment such as charging piles in charging stations, and the status parameters of each energy storage battery unit in energy storage stations, and classify and store them according to the stations in the form of data units.

[0019] In the above technical solution, the following analysis steps are included in step S200: Step S201: Obtain the actual line information of energy transmission between nodes in the photovoltaic-storage-charging system, and analyze the energy transmission efficiency between nodes by combining the functional characteristic parameters of each node. Step S202: Take the energy transmission efficiency between nodes as the edge weight between nodes, and draw the energy sharing path diagram of the photovoltaic-storage-charging system. For the node weights of each node, take the energy supply of the photovoltaic modules in the future time period as the node weight of each type-I node, take the sum of the energy consumption expectations of all users in the future time period as the node weight of type-II nodes, and take the energy storage amount at the initial time point of the future time period as the node weight of type-III nodes. In specific implementation, perform mathematical abstraction on the actual physical architecture of the photovoltaic-storage-charging system, extract the line transmission efficiency, which serves as the data basis for the comprehensive efficiency analysis of system energy sharing, and intuitively map the energy sharing and distribution in the distributed architecture. When analyzing the energy transmission efficiency between nodes, if the energy transmission path between nodes passes through physical lines with different specification parameters, then independently analyze each specification of physical lines between nodes and then superimpose and analyze the final efficiency of energy transmission.

[0020] In the above technical solution, the S300 includes the following analysis steps: Step S301: Obtain the route information of all users through the navigation software, and inductively store the passing historical data of the selected users at each user end. Screen the users who pass through at least one type-II node, and obtain the passing historical data of the selected users. Step S302: Extract the passing line information, energy storage battery state parameters, and charging parameters in the passing historical data, construct a user passing and charging data set, and train a user charging demand prediction model. The input of the user charging demand prediction model is the user passing line information and the user energy storage battery state parameters, and the output is the probability of the user charging at each charging station on the line in the future time period and the expected energy consumption. Multiply the charging probability and the expected energy consumption of the user at each type-II node in the photovoltaic-storage-charging system as the energy consumption expectation of the user for each type-II node. In specific implementation, with the popularization rate of current road traffic navigation technology increasing with the sinking of real-time navigation and mobile network technologies, the popularization rate of road traffic navigation software has been greatly improved. Therefore, when analyzing the user-side demand, perform a charging preference analysis at the user end. Analyze the charging probability of the user at each charging station during driving according to the line information, charging information, and vehicle battery state of charge data in the user passing historical data, analyze the user's expected energy consumption expectation, and perform statistical processing and feedback on the expected energy consumption expectations of all users passing through the charging stations in the navigation software. In terms of data processing, a multi-level distributed processing method is adopted to simplify the data processing flow of a single node, reduce the system data processing pressure, and avoid the loss of result accuracy caused by multi-parameter deviation in complex calculation and analysis; in terms of data transmission, preference analysis is carried out on the user side, and the expected energy consumption is calculated according to the user's travel route. Therefore, when the navigation software conducts data transmission statistics, it does not need to obtain the user's privacy data, but only summarizes and feeds back the user's passing information, greatly improving the privacy and security of system data processing.

[0021] In the above technical solution, the step S400 includes the following analysis steps: Step S401: Obtain the historical data of the energy supply condition parameters of each first-class node in the photovoltaic energy storage charging system; the energy supply condition parameters include the equipment state parameters and environmental parameters of the photovoltaic module of each first-class node; Step S402: For each first-class node, perform data cleaning and normalization on the historical data of the energy supply condition parameters to construct an energy supply parameter data set; Step S403: For each first-class node, use the energy supply parameter data set to train an energy supply prediction model at the node end; The input of the energy supply prediction model is the equipment state parameters of the first-class node to which it belongs and the predicted data of the environmental parameters at future time points, and the output is the energy supply data of the first-class node in the future time period; In specific implementation, parameter extraction is performed on the photovoltaic modules in each power generation station, and energy supply impact parameters such as the soiling rate of photovoltaic panels, the power generation efficiency of photovoltaic panels, and the conversion efficiency of inverters are analyzed. At the same time, environmental parameters such as irradiance, temperature, and humidity that affect power generation efficiency are analyzed for the micro-meteorological parameters of the environment where each power generation station is located. At the same time, an independent prediction model is constructed for each power generation station to ensure the personalization and accuracy of energy supply prediction for each power generation station.

[0022] In the above technical solution, the step S500 includes the following analysis steps: Step S501: Real-time monitor the functional characteristic parameters of each first-class node in the photovoltaic energy storage charging system, use the energy supply prediction model corresponding to each first-class node to predict the energy supply of each first-class node in the future time period, and update the weights of all first-class nodes in the photovoltaic energy storage charging energy sharing path map; Step S502: Real-time obtain the user line information passing through at least one second-class node through the navigation software. When the number of users or the user travel route that meets the conditions changes, use the user charging demand prediction model trained at each user end to analyze the energy consumption expectations of users for each second-class node, and then statistically calculate the sum of the energy consumption expectations of all users for each second-class node in the future time period as the weights of all second-class nodes in the photovoltaic energy storage charging energy sharing path map, and update them in real time; Step S503: Calculate the comprehensive efficiency of energy sharing among various types of nodes according to the edge weights between nodes in the optical storage and charging energy sharing path diagram; For any type-I node a and type-II node b, calculate the comprehensive efficiency of energy distribution from type-I node a to type-II node b according to the formula: η_dis(a,b)=k_dis×η(a,b); For any type-I node a and type-III node c, calculate the comprehensive efficiency of energy storage from type-I node a to type-III node c according to the formula: η_store(a,c)=k_store×η(a,c); For any type-II node b and type-III node c, calculate the comprehensive efficiency of energy sharing between type-II node b and type-III node c according to the formula: η_share=k_share×η(c,b); For any type-III node c, take the ratio of the expected self-discharge stock of type-III node c in the future time period to the energy storage amount at the initial time point as the storage comprehensive efficiency of type-III node c; In specific implementation, independently analyze all possible energy sharing behaviors among all nodes in the optical storage and charging system, such as power supply from power generation sites to charging sites, power supply from power generation sites to energy storage sites, and power supply from energy storage sites to charging sites. At the same time, combine the analysis of the self-decay of the battery power in the energy storage site to comprehensively analyze the energy loss in the optical storage and charging system; Step S504: Obtain the energy consumption deviation between the expected energy consumption of all users and the actual energy consumption data of all users at each type-II node in each historical time period, and then calculate the variance of the energy consumption deviation of each type-II node as the energy consumption volatility of each type-II node; For each type-II node, multiply the energy consumption volatility by the expected energy consumption of all users in the future time period as the energy consumption pre-stock of each type-II node. According to the comprehensive efficiency of energy sharing between type-II nodes and type-III nodes, use the dynamic programming method to make a decision on the maximum pre-storage efficiency strategy, and use the decision result as the pre-stored energy consumption demand of each type-III node in the future time period; Step S505: Further calculate the energy surplus of each type-III node in the future time period according to the energy storage amount of each type-III node at the initial time point of the future time period; If the energy surplus value is greater than or equal to 0, it means that the energy storage amount of the type-III node at the initial time point of the future time period can cover the pre-stored energy consumption demand. Take the energy surplus greater than 0 as the distributable energy of the corresponding type-III node in the future time period, and set the energy supplement demand to 0; If the energy surplus value is less than 0, it means that the energy storage amount of the type-III node at the initial time point of the future time period cannot cover the pre-stored energy consumption demand. Take the absolute value of the energy surplus less than 0 as the energy supplement demand of the corresponding type-III node in the future time period, and set the distributable energy to 0; Step S506: Use the energy supply of all type-I nodes in the future time period and the allocable energy of all type-III nodes as the energy input of the integrated photovoltaic energy storage and charging system, and use the expected energy consumption of all users and the energy replenishment requirements of all type-III nodes in the future time period of all type-II nodes as the energy output of the integrated photovoltaic energy storage and charging system. Use the dynamic programming method to make the maximum comprehensive efficiency decision on the energy sharing in the future time period of the integrated photovoltaic energy storage and charging system; In specific implementation, to avoid the concentrated and sudden power consumption situation at the charging stations, generally, while transmitting and supplying energy, the stored energy is used for replenishment to ensure the functional stability of the charging stations. In the traditional energy pre-storage strategy, generally 10% - 15% of redundant stored power is reserved at the charging stations to cope with sudden energy demands. However, due to the extremely high isolation of the redundant energy storage resources and energy dispatching strategies among stations, the traditional method lacks direct and effective interactive management of the utilization efficiency of the pre-stored energy consumption. Therefore, the self-discharge analysis of the energy storage nodes is introduced to ensure the accuracy of the energy dispatching management strategy and further improve the energy utilization efficiency in the integrated management of photovoltaic energy storage and charging.

[0023] As Figure 2 shown, the present invention also provides an integrated interactive management system for photovoltaic energy storage and charging based on a distributed architecture. The system includes: a multi-source data processing module, an energy supply and demand analysis module, and an energy management decision module; The multi-source data processing module is used to process the data of each station in the photovoltaic energy storage and charging system and draw a path map of the energy sharing of the photovoltaic energy storage and charging; the energy supply and demand analysis module predicts the expected energy consumption of each user for each type-II node according to the historical travel data of each user, trains an energy supply prediction model according to the functional characteristic parameters of the type-I nodes, conducts energy supply prediction, and also analyzes the pre-stored energy consumption requirements of each type-III node according to the energy consumption volatility of each type-II node; the energy management decision module monitors the data according to the functional characteristic parameters of each type of node, updates the node weights in the path map of the energy sharing of the photovoltaic energy storage and charging in real time, and manages and optimizes the energy circulation strategy in the photovoltaic energy storage and charging system.

[0024] In the above technical solution, the multi-source data processing module includes: a system station data processing unit, a functional characteristic parameter processing unit, and an energy sharing path analysis unit; The system station data processing unit obtains the data of each station in the photovoltaic energy storage and charging system and divides each station into nodes according to the functional attributes; the functional characteristic parameter processing unit marks the functional characteristic parameters of each type of node respectively; the energy sharing path analysis unit analyzes the energy transfer efficiency between the nodes corresponding to each station according to the energy sharing path between each station and draws a path map of the energy sharing of the photovoltaic energy storage and charging.

[0025] In the above technical solution, the energy supply and demand analysis module includes: an energy supply analysis unit, an energy demand analysis unit, and a pre-stored demand analysis unit; The energy supply analysis unit trains an energy supply prediction model based on the functional characteristic parameters of a type of node to predict the energy supply volume; the energy demand analysis unit predicts the energy consumption expectations of users for each type-two node according to the historical passage data of each user; the pre-stored demand analysis unit analyzes the pre-stored energy consumption of each type-two node according to the energy consumption volatility of each type-two node.

[0026] In the above technical solution, the energy management decision module includes: a comprehensive efficiency analysis unit, a pre-stored energy consumption demand allocation unit, and an energy integration interactive management unit; The comprehensive efficiency analysis unit calculates the comprehensive efficiency of energy sharing between various types of nodes according to the edge weights between nodes in the optical storage and charging energy sharing path diagram; the pre-stored energy consumption demand allocation unit uses the dynamic programming method to allocate the pre-stored energy consumption of each type-two node to each type-three node with the maximum pre-stored efficiency strategy; the energy integration interactive management unit uses the dynamic programming method to make the maximum comprehensive efficiency decision on energy sharing in the future time period of the optical storage and charging integrated system.

[0027] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. A method for integrated photovoltaic storage and charging interactive management based on a distributed architecture, characterized in that , the method comprises the following steps: Step S100: Acquire data of each site in the solar storage and charging system, classify each site into type I node, type II node, and type III node according to functional attributes, and annotate characteristic parameters of each type of node; Step S200: Obtain the energy sharing path between each site, analyze the energy transfer efficiency between nodes corresponding to each site, and draw a photovoltaic storage and charging energy sharing path diagram; Step S300: obtaining information of users who are expected to pass through the second-class nodes through navigation software, and predicting the energy consumption expectations of users for each of the second-class nodes based on the historical travel data of each user; Step S400: Acquire functional characteristic parameter data of each first-class node, construct an energy supply parameter data set for each first-class node, and train an energy supply prediction model; Step S500: monitor the functional characteristic parameters of each node in real time, predict the energy supply of Class I nodes and the expected energy consumption of Class II nodes in the future time period, analyze the pre-stored energy consumption demand allocation strategy of Class III nodes, and optimize the energy sharing strategy among various nodes in the solar storage and charging system.

2. According to the distributed architecture-based photovoltaic storage and charging integrated interactive management method of claim 1, it is characterized in that: The step S100 includes the following analysis steps: Step S101: Acquire the actual geographical location data and functional information of each site in the solar-storage-charging system; Step S102: dividing each site into nodes according to the functional attributes of each site, wherein the nodes include first-class nodes, second-class nodes, and third-class nodes; Among them, the first type of node refers to a power generation site that generates electricity and supplies energy through photovoltaic modules, the second type of node refers to a charging site that provides charging functions for users, and the third type of node refers to an energy storage site used to store the remaining energy after the energy circulation and distribution of the first type of node; Step S103: labeling the characteristic parameters of each type of node. For any node x, the parameter labeling is: x[Type_x, Fun_x{f_1, f_2, …, f_n}]; Among them, Type_x is the function attribute number of node x, Fun_x is the function feature parameter set of node x, and f_1, f_2, …, f_n are the function feature parameters of node x; For a type I node, the functional characteristic parameter refers to the energy supply condition parameter of the photovoltaic component for power generation; for a type II node, the functional characteristic parameter refers to the user charging condition parameter; for a type III node, the functional characteristic parameter refers to the energy storage condition parameter.

3. According to the distributed architecture-based integrated photovoltaic storage and charging interactive management method of claim 2, it is characterized in that: The step S200 includes the following analysis steps: Step S201: Acquire the actual line information of energy transmission between nodes in the solar energy storage and charging system, and analyze the energy transmission efficiency between nodes in combination with the functional characteristic parameters of each node; Step S202: Using the energy transmission efficiency between nodes as the edge weight between nodes, and drawing a photovoltaic storage and charging energy sharing path diagram; For the node weight of each node, the energy supply of the photovoltaic components in the future time period is used as the node weight of each type of node, the expected energy consumption of all users in the future time period is used as the node weight of the second type of node, and the energy storage at the initial time point of the future time period is used as the node weight of the third type of node.

4. According to the distributed architecture-based integrated photovoltaic storage and charging interactive management method of claim 2, it is characterized in that: The S300 includes the following analysis steps: Step S301: Obtain route information of all users through navigation software, summarize and store the travel history data of the selected users at each user terminal, select all users who pass through at least one second-class node, and obtain the travel history data of the selected users; Step S302: extracting the route information, energy storage battery status parameters and charging parameters from the historical data, constructing a user charging dataset, and training a user charging demand prediction model; The user charging demand prediction model inputs the user's travel route information and the user's energy storage battery status parameters, and outputs the probability and estimated energy consumption of the user charging at each charging station on the route in the future time period; The charging probability of each Class II node in the solar storage and charging system is multiplied by the expected energy consumption to obtain the user's energy consumption expectation for each Class II node.

5. The method for integrated photovoltaic storage and charging interactive management based on a distributed architecture according to claim 2 is characterized in that: The step S400 includes the following analysis steps: Step S401: Acquire historical data of energy supply condition parameters of each type of node in the solar storage and charging system; the energy supply condition parameters include device status parameters and environmental parameters of photovoltaic components of each type of node; Step S402: for each type of node, perform data cleaning and normalization on the historical data of energy supply condition parameters to construct an energy supply parameter data set; Step S403: for each type of node, using the energy supply parameter data set, training an energy supply prediction model at the node end; The energy supply prediction model inputs the device state parameters of the nodes of the first category and the predicted data of the environmental parameters at a future time point, and outputs the energy supply amount data of the nodes of the first category in a future time period.

6. The method for interactive management of integrated photovoltaic storage and charging based on a distributed architecture according to claim 2, characterized in that: The step S500 includes the following analysis steps: Step S501: monitor the functional characteristic parameters of each Class I node in the photovoltaic storage and charging system in real time, use the energy supply prediction model corresponding to each Class I node to predict the energy supply of each Class I node in the future time period, and update the weights of all Class I nodes in the photovoltaic storage and charging energy sharing path diagram; Step S502: Obtain user route information passing through at least one Class II node in real time through navigation software. When the number of users meeting the conditions or the user's driving route changes, use the user charging demand prediction model trained by each user terminal to analyze the user's energy consumption expectations for each Class II node, and then calculate the sum of all user energy consumption expectations for each Class II node in the future time period as the weight of all Class II nodes in the photovoltaic storage and charging energy sharing path diagram, and update it in real time; Step S503: Calculate the comprehensive efficiency of energy sharing between various nodes according to the edge weights between nodes in the photovoltaic storage and charging energy sharing path graph; For any type I node a and type II node b, the comprehensive efficiency of energy allocation from type I node a to type II node b is calculated according to the formula: η_dis(a,b)=k_dis×η(a,b); For any one-type node a and three-type node c, the comprehensive efficiency of energy storage from one-type node a to three-type node c is calculated according to the formula: η_store(a,c)=k_store×η(a,c); For any Class II node b and Class III node c, the comprehensive efficiency of energy sharing between Class II node b and Class III node c is calculated according to the formula: η_share=k_share×η(c,b); For any three types of nodes c, the ratio of the expected self-discharge amount of the three types of nodes c in the future time period to the energy storage amount at the initial time point is taken as the comprehensive storage efficiency of the three types of nodes c; Step S504: Obtain the energy consumption expectation and data of all users of each Class II node in each historical time period and the energy consumption deviation of all users' actual energy consumption data, and then calculate the variance of the energy consumption deviation of each Class II node as the energy consumption fluctuation rate of each Class II node; For each Class II node, the energy consumption fluctuation rate is multiplied by the expected energy consumption of all users in the future time period as the energy reserve of each Class II node. Based on the comprehensive efficiency of energy sharing between Class II nodes and Class III nodes, the dynamic programming method is used to make the maximum reserve efficiency strategy decision, and the decision result is used as the reserve energy consumption demand of each Class III node in the future time period. Step S505: further calculating the energy surplus of each of the three types of nodes in the future time period according to the energy storage amount of each of the three types of nodes at the initial time point of the future time period; If the energy surplus value is greater than or equal to 0, it means that the energy storage at the initial time point of the future time period of the three types of nodes can cover the pre-stored energy consumption demand. The energy surplus greater than 0 is used as the allocable energy for the future time period of the three types of nodes, and the energy supplement demand is set to 0; If the energy surplus value is less than 0, it means that the energy storage of the three types of nodes at the initial time point in the future time period cannot cover the pre-stored energy consumption demand. The absolute value of the energy surplus less than 0 is used as the energy supplement demand of the three types of nodes in the future time period, and the allocable energy is set to 0; Step S506: The energy supply of all Class I nodes in future time periods and the distributable energy of all Class III nodes are used as the energy input of the integrated photovoltaic storage and charging system; the energy consumption expectations of all users of all Class II nodes in future time periods and the energy replenishment needs of all Class III nodes are used as the energy output of the integrated photovoltaic storage and charging system; and the dynamic programming method is used to make the most comprehensive efficiency decision on energy sharing in the integrated photovoltaic storage and charging system in future time periods.

7. A distributed architecture-based integrated photovoltaic storage and charging interactive management system using a distributed architecture-based integrated photovoltaic storage and charging interactive management method according to any one of claims 1 to 6, characterized in that: The system includes: a multi-source data processing module, an energy supply and demand analysis module, and an energy management decision module; The multi-source data processing module is used to process the data of each site in the photovoltaic storage and charging system and draw a photovoltaic storage and charging energy sharing path diagram; the energy supply and demand analysis module predicts the user's energy consumption expectations for each Class II node based on the historical travel data of each user, trains the energy supply prediction model based on the functional characteristic parameters of the Class I nodes, and predicts the energy supply quantity. It also analyzes the pre-stored energy consumption requirements of each Class III node based on the energy consumption fluctuation rate of each Class II node; the energy management decision module updates the node weights in the photovoltaic storage and charging energy sharing path diagram in real time based on the monitoring data of the functional characteristic parameters of each type of node, and manages and optimizes the energy circulation strategy in the photovoltaic storage and charging system.

8. According to the distributed architecture-based integrated photovoltaic storage and charging interactive management system as described in claim 7, it is characterized in that: The multi-source data processing module includes: a system site data processing unit, a functional characteristic parameter processing unit, and an energy sharing path analysis unit; The system site data processing unit obtains the data of each site in the photovoltaic storage and charging system, and divides each site into nodes according to functional attributes; the functional characteristic parameter processing unit respectively labels the functional characteristic parameters of each type of node; the energy sharing path analysis unit analyzes the energy transfer efficiency between the nodes corresponding to each site according to the energy sharing path between each site, and draws a photovoltaic storage and charging energy sharing path diagram.

9. According to the distributed architecture-based integrated photovoltaic storage and charging interactive management system as described in claim 7, it is characterized in that: The energy supply and demand analysis module includes: an energy supply analysis unit, an energy demand analysis unit, and a pre-stored demand analysis unit; The energy supply analysis unit trains an energy supply prediction model based on the functional characteristic parameters of the first-class nodes to predict the energy supply; the energy demand analysis unit predicts the user's energy consumption expectations for each second-class node based on the traffic history data of each user; the pre-stored demand analysis unit analyzes the energy consumption pre-store of each second-class node based on the energy consumption volatility of each second-class node.

10. The photovoltaic storage and charging integrated interactive management system based on a distributed architecture according to claim 7, characterized in that: The energy management decision module includes: a comprehensive efficiency analysis unit, a pre-stored energy consumption demand allocation unit, and an energy integrated interactive management unit; The comprehensive efficiency analysis unit calculates the comprehensive efficiency of energy sharing between various nodes based on the edge weights between nodes in the photovoltaic, storage and charging energy sharing path diagram; the pre-stored energy consumption demand allocation unit uses a dynamic programming method to allocate the energy reserve of each Class II node to each Class III node based on the maximum pre-stored efficiency strategy decision; the energy integration interactive management unit uses a dynamic programming method to make the maximum comprehensive efficiency decision on energy sharing in the future time period of the photovoltaic, storage and charging integrated system.

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