A distributed power supply storage and charging integrated system

Abstract

The present invention discloses a distributed power supply storage and charging integrated system, which belongs to the field of power system management technology. The present invention includes a clean energy access module, an energy storage control and scheduling module, an intelligent charging control module, an energy management and scheduling module, a communication network interconnection module, and an equipment status monitoring module. It dynamically evaluates the battery status from multiple dimensions, calls the matching strategy through a real-time environmental perception mechanism, and makes the charging and discharging path and rate more consistent with the actual load demand and energy supply and demand status of the system. While reducing battery cycle loss, it maximizes energy utilization, dynamically guides users to reduce power of non-critical loads, and dynamically predicts users' specific charging needs in the future time period. This enables the system to have the ability to allocate energy resources in advance, centrally coordinate multiple charging piles, avoid the imbalance in the spatial distribution of energy resources, improve the flexibility of overall system scheduling, and enhance the system's adaptability to complex power consumption behaviors.

Description

Technical Field

[0001] The present invention relates to the technical field of power system management, and in particular to a distributed power supply storage and charging integrated system. Background Art

[0002] The traditional power system, primarily based on centralized power supply, is facing a profound transformation driven by multi-energy complementarity, two-way interaction, and distributed collaboration. Existing distributed energy systems often operate in isolation, lacking effective linkage between clean energy access, energy storage scheduling, and charging control. This makes it difficult to flexibly adjust to actual load demand and energy supply conditions, leading to frequent energy waste and supply-demand imbalances. Furthermore, energy storage equipment operation and management relies on fixed strategies, unable to dynamically optimize charging and discharging paths based on battery health, environmental changes, or grid operation, severely limiting the lifespan and efficiency of energy storage devices. Regarding electric vehicle charging, existing charging systems generally lack user behavior modeling and policy matching mechanisms, hindering on-demand power supply and coordinated control of peak load shifting, which can easily lead to local grid overload and even system instability. Furthermore, some systems lack a comprehensive energy management platform and status monitoring system, lacking comprehensive awareness and unified scheduling capabilities for equipment operating status, communication quality, and health indicators, reducing system operational safety and maintainability. Summary of the Invention

[0003] The purpose of the present invention is to provide a distributed power supply storage and charging integrated system to solve the problems raised in the above background technology.

[0004] To achieve the above objectives, the present invention provides the following technical solutions: a distributed power supply storage and charging integrated system, comprising:

[0005] Clean energy access module, configured to access distributed clean energy and perform energy conversion in multiple scenarios;

[0006] The energy storage control and scheduling module is configured to manage the system's energy storage devices, schedule the storage and release of energy, connect to the DC bus and perform load regulation, obtain the current state of charge of the battery pack and dynamically adjust the charge and discharge rate, and adjust the charge and discharge strategy under different climate conditions;

[0007] An intelligent charging control module is configured to control the dynamic operating status of electric vehicle charging piles, predict, guide and optimize user charging behavior by automatically identifying vehicles and matching them with user-defined charging strategies;

[0008] Energy management scheduling module, configured to coordinate energy flow, information flow and control commands among modules;

[0009] A communication network interconnection module configured to establish a communication path for completing device status synchronization and command response;

[0010] The equipment status monitoring module is configured to collect the operating status parameters of the power equipment in real time through the sensor network, generate the health index of each power equipment based on the operating status parameters, and report the equipment information whose health index is lower than the preset threshold.

[0011] Furthermore, the clean energy access module includes a photovoltaic component array, a wind energy conversion device, a DC bus device, a maximum power tracking unit, and an energy conversion interface;

[0012] The photovoltaic module array and wind energy conversion device are respectively arranged outside the system, and directly coupled with natural resources to obtain energy;

[0013] The DC power output by the photovoltaic array is collected by a DC busbar, and the maximum power tracking unit monitors the light intensity and temperature changes in real time. The AC power signal output by the wind energy conversion device is converted into stable DC power by a frequency conversion rectifier and connected to the energy conversion interface of the DC busbar.

[0014] The energy conversion interface is used to uniformly convert direct current into a system direct current bus standard voltage.

[0015] Furthermore, the energy storage control and scheduling module includes:

[0016] A state of charge detection unit configured to obtain current state of charge data of the energy storage battery pack in real time;

[0017] The state-of-charge detection unit includes a voltage, current, and temperature acquisition component connected to the energy storage battery pack, which continuously acquires the voltage, current, and temperature parameters of the energy storage battery pack and estimates the state of charge of the battery pack based on a multi-parameter fusion model; the multi-parameter fusion model dynamically weights the voltage, current, and temperature parameters according to the open circuit voltage method, the coulomb counter method, and the temperature compensation coefficient, and outputs the state of charge data;

[0018] The dynamic charge and discharge regulation unit is configured to build a charge and discharge control model and set a multi-dimensional strategy library. According to the state of charge data of the energy storage battery group and the current load conditions, clean energy power generation capacity and user-side power requirements, the corresponding strategy in the multi-dimensional strategy library is called in combination with the real-time operating environment. With minimizing the number of battery cycles and maximizing energy utilization efficiency as the objective function, the unit outputs charge and discharge instructions in real time to schedule the charge and discharge path and control the charge and discharge rate in segments.

[0019] Furthermore, the execution step of the state of charge detection unit further includes:

[0020] Extract the collected voltage data parameters in real time;

[0021] Obtaining a voltage change rate according to the collected voltage data parameters;

[0022] Extract the collected current data parameters in real time;

[0023] Obtaining a current change rate according to the collected current data parameters;

[0024] Normalizing the voltage change rate and the current change rate to obtain the normalized voltage change rate and current change rate;

[0025] performing difference processing on the normalized voltage change rate and current change rate to obtain an absolute value of the difference between the voltage change rate and the current change rate;

[0026] When the absolute value of the difference between the voltage change rate and the current change rate does not exceed the preset difference threshold, the preset initial weight values corresponding to the voltage, current and temperature parameters are used for dynamic weighting to output the state of charge data;

[0027] When the absolute value of the difference between the voltage change rate and the current change rate exceeds a preset difference threshold, the weight values corresponding to the voltage, current and temperature parameters are set, and dynamic weighting is performed according to the set weight values to output the charge state data.

[0028] Furthermore, the weight values corresponding to the voltage, current and temperature parameters are set, including:

[0029] When the absolute value of the difference between the voltage change rate and the current change rate exceeds a preset difference threshold, the current data parameters, voltage data parameters and temperature parameters collected and extracted in real time are retrieved;

[0030] Obtaining voltage standard deviation based on voltage data parameters extracted and collected in real time;

[0031] Obtaining the current standard deviation based on the current data parameters collected and extracted in real time;

[0032] Obtaining the standard deviation of the temperature parameters according to the temperature parameters collected and extracted in real time;

[0033] Normalizing the voltage standard deviation, the current standard deviation, and the temperature parameter standard deviation to obtain the normalized voltage standard deviation, the current standard deviation, and the temperature parameter standard deviation;

[0034] The weight values corresponding to the voltage, current and temperature parameters are set using the normalized voltage standard deviation, current standard deviation and temperature parameter standard deviation.

[0035] Furthermore, the energy storage control and scheduling module further includes:

[0036] The load-side participation regulation unit is configured to obtain the power curve of the user load in real time and evaluate the short-term load change trend based on the power curve; based on the short-term load change trend, when the energy storage load is too heavy or the energy input is insufficient, it outputs a load regulation signal to notify the energy management scheduling module to guide non-critical loads to reduce operating power or perform short-term unloading.

[0037] Furthermore, the intelligent charging control module includes:

[0038] a user behavior recognition unit configured to model user charging behavior based on vehicle access information and historical usage records;

[0039] Authenticating the connected electric vehicle through wireless communication, extracting the vehicle's unique identification code, and associating it with the user's account; receiving behavioral characteristic data from the user terminal, the behavioral characteristic data including the user's daily departure time, average driving distance, permanent location, and previous charging frequency and charging time; inputting the behavioral characteristic data into a regression prediction model to establish a fitting model for the user's daily travel load, forming a short-term predicted charging demand curve; and generating a prediction result based on the predicted charging demand curve, the prediction result including the required charging power and duration for each time period within the day;

[0040] a charging strategy matching unit configured to preset and maintain a typical charging strategy model, wherein the typical charging strategy model includes a valley charging strategy based on time-based electricity prices, a fast charging strategy based on user emergency travel priorities, and a flexible adjustment strategy based on grid load balancing;

[0041] The prediction results output by the user behavior recognition unit are fitted and compared with typical charging strategy models. The cost function and execution constraints for each strategy are calculated based on the adjustable capacity of the energy storage system, clean energy output forecast, and local grid load constraints.

[0042] Select and issue the optimal control strategy with the lowest comprehensive cost from the strategies that meet the conditions.

[0043] Furthermore, the intelligent charging control module further includes:

[0044] The power distribution optimization unit is configured to access the system communication network, collect information on the current power status of each charging pile, the number of connected vehicles, the battery charging status, and the charging strategy being executed, and send the optimal control strategy to the corresponding charging pile, dynamically adjusting the charging power, start-stop sequence, and rotation strategy of each charging pile.

[0045] Furthermore, the energy management scheduling module includes:

[0046] A status information aggregation unit is configured to establish communication links with each module, connect system status information from the clean energy access module, energy storage control and scheduling module, intelligent charging control module, communication network interconnection module, and equipment status monitoring module. The system status information includes power generation power, energy storage charge state, charging load requirements, power equipment health indicators, and communication status information, thereby constructing a system-level operating status view;

[0047] Perform timestamp alignment and structural integration on the connected system status information to build a unified data frame. By establishing key indicator association mapping relationships, perform semantic analysis on various status data and extract core indicators, including system load capacity, regulation margin, and power supply and demand matching.

[0048] an optimization strategy generation unit configured to construct an optimization model that jointly considers energy supply, load demand, energy storage utilization, and a cost function; the optimization model is configured based on a multi-objective optimization objective function, wherein the multi-objectives include minimizing electricity purchase costs, reducing peak loads, extending battery life, and improving clean energy utilization;

[0049] Real-time system status information in the rolling time domain is used to deduce short-term future states and dynamically update scheduling strategies. Scheduling strategies are encapsulated into executable commands and sent to each distributed node device via the IP-based edge communication network.

[0050] Furthermore, the energy management scheduling module further includes:

[0051] The operation mode switching unit is configured to detect the main grid connection status, grid voltage, current fluctuation and frequency deviation in real time, determine whether the system maintains a stable connection with the main grid, set multi-level judgment thresholds, and determine whether the island operation startup conditions are met. The island operation startup conditions include grid faults, voltage drops and current interruptions. When it is determined that the island or grid-connected status has switched, the corresponding strategy template is automatically called to adjust the power generation, energy storage and load control methods within the system.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The present invention realizes the accurate acquisition and evaluation of the state of charge of the energy storage system by constructing a multi-unit collaborative working mechanism, thereby improving the intelligence level of the system in energy allocation and load response. It adopts a multi-parameter fusion model to dynamically evaluate the battery status from multiple dimensions, and constructs a multi-dimensional strategy library for different operating scenarios. The matching strategy is called through the real-time environmental perception mechanism to make the charging and discharging path and rate more consistent with the actual load demand and energy supply and demand status of the system. While reducing battery cycle loss, it maximizes energy utilization. When the energy storage pressure is large or the energy supply is insufficient, it can dynamically guide the user's non-critical load to reduce power, effectively alleviating the system pressure and improving the system's adaptability in peak load or sudden energy shortage scenarios. It significantly improves the control accuracy, response efficiency and operating life of the energy storage system.

[0054] 2. Based on the user's personalized charging characteristic data, the present invention constructs a regression model for short-term prediction, which can dynamically predict the user's specific charging needs in the future period, enabling the system to allocate energy resources in advance and reduce the load pressure caused by charging peaks. Through the built-in multi-category typical strategy library, considering multi-dimensional constraints such as energy storage capacity, grid load and clean energy output, the cost functions of different strategies are calculated and the optimal strategy is automatically selected to minimize the system operating cost and maximize energy efficiency. According to the status of each pile and vehicle demand, the charging power and start-stop strategy are dynamically adjusted. It has the ability to centrally coordinate multiple charging piles, avoid the imbalance in the spatial distribution of energy resources, improve the flexibility and robustness of the overall system scheduling, improve user experience, reduce the risk of grid peak load, and enhance the system's adaptability to complex electricity consumption behaviors.

[0055] 3. The present invention establishes a cross-module communication link to obtain key data such as power generation, energy storage, charging and equipment health in real time, and performs structured integration and core indicator extraction on them to form a system-level operating status view, breaking the limitations of traditional system information islands and providing data support for subsequent optimization strategy formulation. Based on a multi-objective function optimization model, the strategy is dynamically updated using a rolling time domain deduction method to ensure that the system operates in an optimal balance between economy and safety. Based on real-time detection of the main power grid status and multi-level judgment logic, it automatically switches to island operation mode in abnormal situations such as system disconnection or power grid failure, ensuring the system's continuous power supply capability, achieving seamless switching and high-reliability operation of the system, and improving the system's autonomous management capability, abnormal response capability and global energy efficiency optimization capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a schematic diagram of the distributed power supply storage and charging integrated system module of the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] See also Figure 1 , the present invention provides the following technical solutions:

[0059] A distributed power supply storage and charging integrated system, comprising:

[0060] Clean energy access module, configured to access distributed clean energy and perform energy conversion in multiple scenarios;

[0061] The energy storage control and scheduling module is configured to manage the system's energy storage devices, schedule the storage and release of energy, connect to the DC bus and perform load regulation, obtain the current state of charge of the battery pack and dynamically adjust the charge and discharge rate, and adjust the charge and discharge strategy under different climate conditions;

[0062] An intelligent charging control module is configured to control the dynamic operating status of electric vehicle charging piles, predict, guide and optimize user charging behavior by automatically identifying vehicles and matching them with user-defined charging strategies;

[0063] Energy management scheduling module, configured to coordinate energy flow, information flow and control commands among modules;

[0064] A communication network interconnection module configured to establish a communication path for completing device status synchronization and command response;

[0065] The equipment status monitoring module is configured to collect the operating status parameters of the power equipment in real time through the sensor network, generate the health index of each power equipment based on the operating status parameters, and report the equipment information whose health index is lower than the preset threshold.

[0066] The clean energy access module includes a photovoltaic panel array, a wind energy conversion device, a DC bus equipment, a maximum power point tracking unit, and an energy conversion interface;

[0067] The photovoltaic module array and wind energy conversion device are respectively installed outside the system, directly coupling with natural resources to obtain energy; the DC power output by the photovoltaic module array is collected by the DC bus device, and the maximum power tracking unit monitors the light intensity and temperature changes in real time; the AC power signal output by the wind energy conversion device is converted into stable DC power by the frequency conversion rectifier device, and is uniformly connected to the energy conversion interface of the DC bus device; the energy conversion interface is used to uniformly convert DC power into the system DC bus standard voltage.

[0068] In the above-described embodiment, by constructing a clean energy access module comprising a photovoltaic array and a wind energy conversion device, the integrated access of multiple distributed renewable energy sources is achieved, effectively improving the system's energy acquisition capabilities in different scenarios. Real-time monitoring of light intensity and temperature is performed, and variable frequency rectification technology is combined to stabilize wind power output, ensuring the continuity and stability of energy input. Furthermore, the energy conversion interface achieves a unified and standardized output for various DC inputs, enabling the system to efficiently connect to the DC bus for energy, thereby improving overall energy conversion efficiency. This makes the system independent of a single energy source and enhances energy supply reliability under resource fluctuations or sudden weather changes. This is particularly suitable for areas with significant differences in natural conditions such as light intensity and wind speed. Furthermore, the system's modular design provides excellent scalability, allowing for flexible deployment based on actual application needs, facilitating subsequent maintenance and upgrades, and improving the energy utilization efficiency, response speed, and operational stability of the distributed energy access system.

[0069] Energy storage control and scheduling module, including:

[0070] A state of charge detection unit configured to obtain current state of charge data of the energy storage battery pack in real time;

[0071] The state-of-charge detection unit includes voltage, current, and temperature acquisition components connected to the energy storage battery pack. It continuously collects the voltage, current, and temperature parameters of the energy storage battery pack and estimates the battery pack's state of charge based on a multi-parameter fusion model. The multi-parameter fusion model dynamically weights the voltage, current, and temperature parameters using the open-circuit voltage method, coulomb counter method, and temperature compensation coefficient to output the state-of-charge data.

[0072] The dynamic charge and discharge regulation unit is configured to build a charge and discharge control model and set a multi-dimensional strategy library. Based on the state of charge data of the energy storage battery pack, the current load conditions, the clean energy generation capacity and the user-side power demand, the corresponding strategy in the multi-dimensional strategy library is called in combination with the real-time operating environment. With minimizing the number of battery cycles and maximizing energy utilization efficiency as the objective function, the unit outputs charge and discharge instructions in real time to schedule the charge and discharge path and control the charge and discharge rate in segments.

[0073] The load-side participation regulation unit is configured to obtain the power curve of the user load in real time and evaluate the short-term load change trend based on the power curve; based on the short-term load change trend, when the energy storage load is too heavy or the energy input is insufficient, it outputs a load regulation signal to notify the energy management scheduling module to guide non-critical loads to reduce operating power or perform short-term unloading.

[0074] In the above embodiment, the energy storage control and scheduling module, by establishing a multi-unit collaborative working mechanism, not only achieves accurate acquisition and assessment of the energy storage system's state of charge, but also further enhances the system's intelligence level in energy allocation and load response. It adopts a multi-parameter fusion model, effectively combining the open-circuit voltage method, coulomb counter method, and temperature compensation technology to dynamically assess the battery status from multiple dimensions. Compared with traditional single detection methods, it has significant improvements in accuracy and robustness. It constructs a multi-dimensional strategy library for different operating scenarios and calls matching strategies through a real-time environmental perception mechanism to make the charging and discharging paths and rates more consistent with the system's actual load requirements and energy supply and demand status. While reducing battery cycle losses, it maximizes energy utilization. When energy storage pressure is high or energy supply is insufficient, it can dynamically guide users' non-critical loads to reduce power, effectively alleviating system pressure and improving the system's adaptability in peak load or sudden energy shortage scenarios, significantly improving the control accuracy, response efficiency, and operating life of the energy storage system.

[0075] Specifically, the execution steps of the state of charge detection unit further include:

[0076] Extract the collected voltage data parameters in real time;

[0077] Obtaining a voltage change rate according to the collected voltage data parameters;

[0078] Extract the collected current data parameters in real time;

[0079] Obtaining a current change rate according to the collected current data parameters;

[0080] Normalizing the voltage change rate and the current change rate to obtain the normalized voltage change rate and current change rate;

[0081] performing difference processing on the normalized voltage change rate and current change rate to obtain an absolute value of the difference between the voltage change rate and the current change rate;

[0082] When the absolute value of the difference between the voltage change rate and the current change rate does not exceed the preset difference threshold, the preset initial weight values corresponding to the voltage, current and temperature parameters are used for dynamic weighting to output the state of charge data;

[0083] When the absolute value of the difference between the voltage change rate and the current change rate exceeds a preset difference threshold, the weight values corresponding to the voltage, current and temperature parameters are set, and dynamic weighting is performed according to the set weight values to output the charge state data.

[0084] The technical solution described above achieves the following: by extracting collected voltage and current data parameters in real time and calculating their rate of change, dynamic changes in the battery's operating state can be captured promptly. This allows state-of-charge (SOC) detection to be based on the latest data, improving the real-time nature of detection results. Normalizing the voltage and current rate of change eliminates the impact of different dimensions, enabling comparisons on the same scale, further improving the accuracy of subsequent difference processing and thus more accurately reflecting the battery's actual state. When the absolute value of the difference between the voltage and current rate of change does not exceed a preset difference threshold, dynamic weighting is performed using a preset initial weight. This approach enables rapid and efficient calculation of SOC data based on established empirical weights when the battery's operating state is relatively stable, ensuring detection efficiency and a certain degree of accuracy. When the absolute value of the difference exceeds a preset difference threshold, the weights corresponding to the voltage, current, and temperature parameters are set, and dynamic weighting is performed according to the set weights. This enables the system to adaptively adjust weights based on changes in the battery's operating state to more accurately reflect the battery's actual state of charge (SOC). This is particularly useful in situations where the battery's operating state fluctuates significantly, improving the system's adaptability and robustness. When dynamically weighting SOC data, not only voltage and current parameters are considered, but also temperature. Temperature significantly impacts battery performance and SOC. Comprehensively considering these parameters can more comprehensively reflect the battery's actual operating state, thereby improving the accuracy and reliability of SOC detection and optimizing the performance indicators of the battery management system. By setting a difference threshold and adopting different weighting strategies, this technical solution maintains good performance both when the battery's operating state is stable and fluctuating. This avoids the problem of excessive SOC detection errors caused by changes in the operating state, improves the stability and reliability of the entire system, and reduces the possibility of misjudgment, which is of great significance for ensuring the normal operation of the battery and the safety of the equipment.

[0085] Specifically, the weight values corresponding to the voltage, current, and temperature parameters are set, including:

[0086] When the absolute value of the difference between the voltage change rate and the current change rate exceeds a preset difference threshold, the current data parameters, voltage data parameters and temperature parameters collected and extracted in real time are retrieved;

[0087] Obtaining voltage standard deviation based on voltage data parameters extracted and collected in real time;

[0088] Obtaining the current standard deviation based on the current data parameters collected and extracted in real time;

[0089] Obtaining the standard deviation of the temperature parameters according to the temperature parameters collected and extracted in real time;

[0090] Normalizing the voltage standard deviation, the current standard deviation, and the temperature parameter standard deviation to obtain the normalized voltage standard deviation, the current standard deviation, and the temperature parameter standard deviation;

[0091] The weight values corresponding to the voltage, current and temperature parameters are set using the normalized voltage standard deviation, current standard deviation and temperature parameter standard deviation.

[0092] The weight values corresponding to the voltage, current and temperature parameters are obtained by the following formula:

[0093]

[0094] Among them, w v 、w I and w t Respectively represent the weight values corresponding to voltage, current and temperature parameters; k v 、k I and k t Respectively represent the reference coefficients corresponding to the voltage, current and temperature parameters, and the value ranges of the reference coefficients corresponding to the voltage, current and temperature parameters are 0.5-1.2, 0.3-0.8 and 0.1-0.5; D represents the absolute value of the difference between the voltage change rate and the current change rate; σ v , σ I and σ t Respectively represent the voltage standard deviation, current standard deviation and temperature parameter standard deviation after normalization of voltage, current and temperature parameters; μ represents the current amplitude adjustment factor, and its value range is 0.1A - ¹-0.5A - ¹; λ represents the voltage and current change attenuation factor, with a value range of 0.05-0.2. Specifically, This reflects the physical logic that voltage's contribution to the state of charge is relatively reduced when the battery state is complex. The absolute value of the difference is to highlight the difference between the current and voltage fluctuations. If the current fluctuation has a large impact on the voltage fluctuation, the numerator is large and the current weight w I will increase, reflecting that current is more important for the state of charge calculation in this case. Middle K t It is the temperature reference coefficient, which reflects the basic importance of temperature contribution to the state of charge. The value range is 0.1-0.5, which limits its basic weight. t ) The temperature standard deviation σ t Mapped to the (−1,1) interval, the larger the temperature fluctuation (σ t The larger the temperature is, the greater the value is, that is, when the temperature fluctuates greatly, its weight contribution to the state of charge calculation increases. The denominator comprehensively considers the fluctuation characteristics of voltage, current and temperature parameters by combining these three items. Under different battery working conditions, the fluctuation of each parameter is different, and the value of the corresponding item in the denominator will change accordingly, thereby dynamically adjusting w v 、w I 、w t For example, when the battery is rapidly charged and discharged, the current fluctuation is large, and the current fluctuation influence term increases in the denominator, which will increase the current weight w I Increase, which reasonably reflects that the current parameter is more important for the charge state calculation at this time.

[0095] The technical effect of the above-mentioned technical solution is that when the absolute value of the difference between the voltage and current rate of change exceeds a threshold, real-time current, voltage, and temperature parameters are retrieved and their respective standard deviations are calculated, enabling a more detailed characterization of these parameter fluctuations. The standard deviation reflects the degree of data dispersion and can capture subtle characteristics of parameter changes, providing a more comprehensive and accurate description of the battery state than simply considering the rate of change. Normalizing these standard deviations eliminates the influence of different parameter dimensions, allowing comparisons and calculations to be performed on the same scale. This provides a basis for accurately setting weights and thus improves the accuracy of state-of-charge detection. Using the normalized standard deviations to set the corresponding weights for voltage, current, and temperature parameters dynamically adjusts the importance of each parameter in the state-of-charge calculation based on the fluctuation characteristics of the current real-time data. This allows the state-of-charge calculation to more effectively emphasize the impact of the currently fluctuating parameters and better adapt to the complex and changing operating conditions of the battery. This solution comprehensively considers the fluctuations of multiple parameters to set weights, rather than relying on a single or fixed weight setting. This allows the system to more reasonably allocate the weights of various parameters when faced with various complex operating conditions, such as parameter changes caused by factors such as different charge and discharge rates, ambient temperature changes, and battery aging. This reduces the state of charge calculation error caused by abnormal parameters or changes in operating conditions, and improves the system's anti-interference ability and robustness. Incorporating the standard deviation of the temperature parameter into the weight setting fully considers the significant impact of temperature on battery performance and state of charge. Temperature changes can affect the battery's internal resistance, chemical reaction rate, and other factors, which in turn affect the relationship between voltage and current and state of charge. In this way, multiple key factors affecting the battery's state of charge and their interrelationships are fully considered, making the state of charge calculation more scientific and reasonable, and optimizing the performance indicators of the battery management system.

[0096] Intelligent charging control module, including:

[0097] a user behavior recognition unit configured to model user charging behavior based on vehicle access information and historical usage records;

[0098] The system authenticates the connected electric vehicle through wireless communication, extracts the vehicle's unique identification code, and associates it with the user's account; receives behavioral characteristic data from the user terminal, including the user's daily departure time, average driving distance, permanent location, and previous charging frequency and charging time; inputs the behavioral characteristic data into a regression prediction model to establish a fitting model for the user's daily travel load, forming a short-term predicted charging demand curve; and generates prediction results based on the predicted charging demand curve, including the required charging power and duration for each time period within the day;

[0099] a charging strategy matching unit configured to preset and maintain typical charging strategy models, the typical charging strategy models including a valley charging strategy based on time-based electricity prices, a fast charging strategy based on user emergency travel priorities, and a flexible adjustment strategy based on grid load balancing;

[0100] The prediction results output by the user behavior recognition unit are fitted and compared with typical charging strategy models. The cost function and execution constraints for each strategy are calculated based on the adjustable capacity of the energy storage system, clean energy output forecast, and local grid load constraints.

[0101] Select and issue the optimal control policy with the lowest comprehensive cost from the strategies that meet the conditions;

[0102] The power distribution optimization unit is configured to access the system communication network, collect information on the current power status of each charging pile, the number of connected vehicles, the battery charging status, and the charging strategy being executed, and send the optimal control strategy to the corresponding charging pile, dynamically adjusting the charging power, start-stop sequence, and rotation strategy of each charging pile.

[0103] In the above-described embodiment, by constructing multiple collaborative sub-units, including user behavior recognition, charging strategy matching, and power allocation optimization, the system significantly enhances its intelligent control over the electric vehicle charging process. Based on personalized user charging characteristic data, a regression model for short-term forecasting is constructed, which can dynamically predict users' specific charging needs in future time periods. This enables the system to proactively allocate energy resources and reduce the load pressure caused by peak charging periods. Through a built-in library of multiple typical strategies, the cost functions of different strategies are calculated and the optimal strategy is automatically selected, taking into account multi-dimensional constraints such as energy storage capacity, grid load, and clean energy output. This minimizes system operating costs and maximizes energy efficiency while ensuring user charging needs. Charging power and start-stop strategies are dynamically adjusted based on the status of each charging station and vehicle demand. The system provides the ability to centrally coordinate multiple charging stations, avoiding uneven spatial distribution of energy resources, improving the flexibility and robustness of overall system scheduling, enhancing user experience, reducing the risk of grid peak loads, and enhancing the system's adaptability to complex electricity usage behaviors.

[0104] Energy management scheduling module, including:

[0105] The status information aggregation unit is configured to establish communication links with each module and connect system status information from the clean energy access module, energy storage control and scheduling module, intelligent charging control module, communication network interconnection module, and equipment status monitoring module. System status information includes power generation power, energy storage charge state, charging load demand, power equipment health indicators, and communication status information, thus constructing a system-level operating status view;

[0106] Perform timestamp alignment and structural integration on the connected system status information to build a unified data frame. By establishing key indicator association mapping relationships, perform semantic analysis on various status data and extract core indicators, including system load capacity, regulation margin, and power supply and demand matching.

[0107] An optimization strategy generation unit configured to construct an optimization model that jointly considers energy supply, load demand, energy storage utilization, and cost functions; the optimization model is configured based on a multi-objective optimization objective function, including minimizing electricity purchase costs, reducing peak loads, extending battery life, and improving clean energy utilization;

[0108] Using real-time system status information in the rolling time domain, the system deduces short-term future states and dynamically updates scheduling policies. The scheduling policies are encapsulated into executable commands and sent to distributed node devices via the IP-based edge communication network.

[0109] The operation mode switching unit is configured to detect the main grid connection status, grid voltage, current fluctuation and frequency deviation in real time, determine whether the system maintains a stable connection with the main grid, set multi-level judgment thresholds, and determine whether the island operation startup conditions are met. The island operation startup conditions include grid faults, voltage drops and current interruptions. When it is determined that the island or grid-connected status has switched, the corresponding strategy template is automatically called to adjust the power generation, energy storage and load control methods within the system.

[0110] In the above embodiment, by constructing three core units: status information aggregation, optimization strategy generation, and operation mode switching, intelligent scheduling and comprehensive coordination of the entire distributed power storage and charging integrated system are achieved. By establishing cross-module communication links, key data such as power generation, energy storage, charging, and equipment health are acquired in real time, and structured integration and core indicators are extracted to form a system-level operation status view, breaking the limitations of traditional system information silos and providing data support for subsequent optimization strategy formulation. Based on a multi-objective function optimization model, multiple factors such as supply and demand matching, grid electricity prices, energy storage lifespan, and clean energy output are fully considered. The strategy is dynamically updated using a rolling time domain deduction method to ensure that the system operates in an optimal balance between economy and safety. Based on real-time detection of the main grid status and multi-level decision logic, the system can automatically switch to island operation mode in abnormal situations such as system disconnection or grid failure to ensure the system's continuous power supply capability. After grid connection is restored, it can quickly switch to grid-connected operation and optimize energy exchange behavior, achieving seamless switching and high-reliability operation of the system, improving the system's autonomous management capabilities, abnormal response capabilities, and global energy efficiency optimization capabilities.

[0111] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A distributed power supply storage and charging integrated system, characterized in that: include: Clean energy access module, configured to access distributed clean energy and perform energy conversion in multiple scenarios; The energy storage control and scheduling module is configured to manage the system's energy storage devices, schedule the storage and release of energy, connect to the DC bus and perform load regulation, obtain the current state of charge of the battery pack and dynamically adjust the charge and discharge rate, and adjust the charge and discharge strategy under different climate conditions; An intelligent charging control module is configured to control the dynamic operating status of electric vehicle charging piles, predict, guide and optimize user charging behavior by automatically identifying vehicles and matching them with user-defined charging strategies; Energy management scheduling module, configured to coordinate energy flow, information flow and control commands among modules; A communication network interconnection module configured to establish a communication path for completing device status synchronization and command response; An equipment status monitoring module is configured to collect operating status parameters of power equipment in real time through a sensor network, generate a health index for each power equipment based on the operating status parameters, and report information about equipment whose health index is below a preset threshold; The energy storage control and scheduling module includes: A state of charge detection unit configured to obtain current state of charge data of the energy storage battery pack in real time; The state-of-charge detection unit includes a voltage, current, and temperature acquisition component connected to the energy storage battery pack, which continuously acquires the voltage, current, and temperature parameters of the energy storage battery pack and estimates the state of charge of the battery pack based on a multi-parameter fusion model; the multi-parameter fusion model dynamically weights the voltage, current, and temperature parameters according to the open circuit voltage method, the coulomb counter method, and the temperature compensation coefficient, and outputs the state of charge data; The execution steps of the state of charge detection unit further include: Extract the collected voltage data parameters in real time; Obtaining a voltage change rate according to the collected voltage data parameters; Extract the collected current data parameters in real time; Obtaining a current change rate according to the collected current data parameters; Normalizing the voltage change rate and the current change rate to obtain the normalized voltage change rate and current change rate; performing difference processing on the normalized voltage change rate and current change rate to obtain an absolute value of the difference between the voltage change rate and the current change rate; When the absolute value of the difference between the voltage change rate and the current change rate does not exceed the preset difference threshold, the preset initial weight values corresponding to the voltage, current and temperature parameters are used for dynamic weighting to output the state of charge data; When the absolute value of the difference between the voltage change rate and the current change rate exceeds a preset difference threshold, weight values corresponding to the voltage, current and temperature parameters are set, and dynamically weighted according to the set weight values to output the state of charge data; Set the weight values corresponding to the voltage, current and temperature parameters, including: When the absolute value of the difference between the voltage change rate and the current change rate exceeds a preset difference threshold, the current data parameters, voltage data parameters and temperature parameters collected and extracted in real time are retrieved; Obtaining voltage standard deviation based on voltage data parameters extracted and collected in real time; Obtaining the current standard deviation based on the current data parameters collected and extracted in real time; Obtaining the standard deviation of the temperature parameters according to the temperature parameters collected and extracted in real time; Normalizing the voltage standard deviation, the current standard deviation, and the temperature parameter standard deviation to obtain the normalized voltage standard deviation, the current standard deviation, and the temperature parameter standard deviation; The weight values corresponding to the voltage, current and temperature parameters are set using the normalized voltage standard deviation, current standard deviation and temperature parameter standard deviation.

2. A distributed power supply storage and charging integrated system according to claim 1, characterized in that: The clean energy access module includes a photovoltaic module array, a wind energy conversion device, a DC bus device, a maximum power tracking unit and an energy conversion interface; The photovoltaic module array and wind energy conversion device are respectively arranged outside the system, and directly coupled with natural resources to obtain energy; The DC power output by the photovoltaic array is collected by a DC busbar, and the maximum power tracking unit monitors the light intensity and temperature changes in real time. The AC power signal output by the wind energy conversion device is converted into stable DC power by a frequency conversion rectifier and connected to the energy conversion interface of the DC busbar. The energy conversion interface is used to uniformly convert direct current into a system direct current bus standard voltage.

3. The distributed power supply storage and charging integrated system according to claim 1, characterized in that: The energy storage control and scheduling module includes: The dynamic charge and discharge regulation unit is configured to build a charge and discharge control model and set a multi-dimensional strategy library. According to the state of charge data of the energy storage battery group and the current load conditions, clean energy power generation capacity and user-side power requirements, the corresponding strategy in the multi-dimensional strategy library is called in combination with the real-time operating environment. With minimizing the number of battery cycles and maximizing energy utilization efficiency as the objective function, the unit outputs charge and discharge instructions in real time to schedule the charge and discharge path and control the charge and discharge rate in segments.

4. A distributed power supply storage and charging integrated system according to claim 3, characterized in that: The energy storage control and scheduling module further includes: The load-side participation regulation unit is configured to obtain the power curve of the user load in real time and evaluate the short-term load change trend based on the power curve; based on the short-term load change trend, when the energy storage load is too heavy or the energy input is insufficient, it outputs a load regulation signal to notify the energy management scheduling module to guide non-critical loads to reduce operating power or perform short-term unloading.

5. The distributed power supply storage and charging integrated system according to claim 1, characterized in that: The intelligent charging control module includes: a user behavior recognition unit configured to model user charging behavior based on vehicle access information and historical usage records; Authenticating the connected electric vehicle through wireless communication, extracting the vehicle's unique identification code, and associating it with the user's account; receiving behavioral characteristic data from the user terminal, the behavioral characteristic data including the user's daily departure time, average driving distance, permanent location, and previous charging frequency and charging time; inputting the behavioral characteristic data into a regression prediction model to establish a fitting model for the user's daily travel load, forming a short-term predicted charging demand curve; and generating a prediction result based on the predicted charging demand curve, the prediction result including the required charging power and duration for each time period within the day; a charging strategy matching unit configured to preset and maintain a typical charging strategy model, wherein the typical charging strategy model includes a valley charging strategy based on time-based electricity prices, a fast charging strategy based on user emergency travel priorities, and a flexible adjustment strategy based on grid load balancing; The prediction results output by the user behavior recognition unit are fitted and compared with typical charging strategy models. The cost function and execution constraints for each strategy are calculated based on the adjustable capacity of the energy storage system, clean energy output forecast, and local grid load constraints. Select and issue the optimal control strategy with the lowest comprehensive cost from the strategies that meet the conditions.

6. A distributed power supply storage and charging integrated system according to claim 5, characterized in that: The intelligent charging control module further includes: The power distribution optimization unit is configured to access the system communication network, collect information on the current power status of each charging pile, the number of connected vehicles, the battery charging status, and the charging strategy being executed, and send the optimal control strategy to the corresponding charging pile, dynamically adjusting the charging power, start-stop sequence, and rotation strategy of each charging pile.

7. The distributed power supply storage and charging integrated system according to claim 1, characterized in that: The energy management scheduling module includes: A status information aggregation unit is configured to establish communication links with each module, connect system status information from the clean energy access module, energy storage control and scheduling module, intelligent charging control module, communication network interconnection module, and equipment status monitoring module. The system status information includes power generation power, energy storage charge state, charging load requirements, power equipment health indicators, and communication status information, thereby constructing a system-level operating status view; Perform timestamp alignment and structural integration on the connected system status information to build a unified data frame. By establishing key indicator association mapping relationships, perform semantic analysis on various status data and extract core indicators, including system load capacity, regulation margin, and power supply and demand matching. an optimization strategy generation unit configured to construct an optimization model that jointly considers energy supply, load demand, energy storage utilization, and a cost function; the optimization model is configured based on a multi-objective optimization objective function, wherein the multi-objectives include minimizing electricity purchase costs, reducing peak loads, extending battery life, and improving clean energy utilization; Real-time system status information in the rolling time domain is used to deduce short-term future states and dynamically update scheduling strategies. Scheduling strategies are encapsulated into executable commands and sent to each distributed node device via the IP-based edge communication network.

8. A distributed power supply storage and charging integrated system according to claim 7, characterized in that: The energy management scheduling module further includes: The operation mode switching unit is configured to detect the main grid connection status, grid voltage, current fluctuation and frequency deviation in real time, determine whether the system maintains a stable connection with the main grid, set multi-level judgment thresholds, and determine whether the island operation startup conditions are met. The island operation startup conditions include grid faults, voltage drops and current interruptions. When it is determined that the island or grid-connected status has switched, the corresponding strategy template is automatically called to adjust the power generation, energy storage and load control methods within the system.

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