An energy storage management system with improved demand protection algorithm

Abstract

The present invention discloses an energy storage management system with an improved demand protection algorithm, comprising: a data acquisition module for acquiring energy storage system load data, distributed energy operation data, and energy storage system operation data; a correlation feature analysis module for acquiring a correlation feature set between load and energy supply; a control strategy generation module for adjusting charge and discharge thresholds and formulating charge and discharge control strategies; a control strategy execution module for formulating charge and discharge control strategies and adjusting the energy storage converter operating mode; a collaborative execution module for dynamically optimizing power allocation strategies by coordinating distributed energy with the power grid; and a dynamic power adjustment strategy adjustment module for adjusting charge and discharge protection thresholds and power adjustment strategies through a feedback mechanism. The present invention improves the charge and discharge efficiency of the energy storage system through an intelligent control and scheduling mechanism, enhances the stability and flexibility of the power grid, and provides effective protection for the safe operation of the power grid.

Description

Technical Field

[0001] The present invention relates to the field of power load control, and in particular to an energy storage management system with an improved demand protection algorithm. Background Art

[0002] With the global transformation of the energy mix and the widespread adoption of renewable energy, power system stability faces new challenges. Renewable energy sources such as wind and solar are intermittent and volatile. This unstable power supply exacerbates grid load fluctuations, placing greater pressure on grid load balance and voltage stability. Against this backdrop, traditional grid dispatching and energy storage management methods have exposed numerous shortcomings in addressing these challenges. First, traditional energy storage system management strategies are overly static and lack flexibility. Existing energy storage systems typically rely on fixed charging and discharging strategies, failing to adapt effectively and in real time to rapidly fluctuating grid loads and energy supply. The limitations of these fixed strategies prevent energy storage systems from providing timely and effective power regulation in response to grid load fluctuations or energy supply changes, thus failing to meet the grid's requirements for stability and flexibility. Second, with the widespread deployment of distributed energy resources, grid loads and energy supply have become more complex and dynamic. The generation capacity of distributed energy resources is affected by weather and seasonal variations, often exhibiting significant fluctuations. This volatility exacerbates grid load fluctuations, making traditional grid dispatching methods difficult to adapt. While some advanced power dispatching methods attempt to integrate distributed energy resource data, in practice, efficiently dispatching these distributed energy resources and coordinating them with energy storage systems remains a pressing challenge. Furthermore, existing energy storage systems often lack precise protection mechanisms. When faced with sudden increases or decreases in load or sudden fluctuations in energy supply, energy storage systems must be able to rapidly respond and adjust their charging and discharging strategies. However, traditional protection strategies and control mechanisms fail to dynamically optimize for real-time grid conditions, rendering the energy storage system's responsiveness and protection strategies ineffective in ensuring safe grid operation during emergencies. For example, when grid load suddenly increases or fluctuates, energy storage systems may not be able to provide sufficient power within a short period of time, leading to grid imbalance and potential safety hazards such as voltage fluctuations or overloads. These issues not only impact the efficiency of energy storage systems but also hinder the stable operation of the grid in high-load, high-fluctuation environments. Therefore, improving the dynamic response capabilities of energy storage systems, optimizing load forecasting and dispatching strategies, and improving adaptive control mechanisms for energy storage systems are key challenges in current power system management and energy storage technology development. Summary of the Invention

[0003] To address the above-mentioned problems in the prior art, the present invention provides an energy storage energy management system with an improved demand protection algorithm, which mainly includes:

[0004] The data acquisition module is used to obtain energy storage system load data, distributed energy operation data, and energy storage system operation data based on load monitoring equipment and smart meters, and to clean, preprocess, standardize, and calibrate the acquired data;

[0005] The correlation feature analysis module is used to combine historical load and energy supply data, use long-term short-term memory networks to perform long-term trend forecasts, and use support vector machines to analyze the correlation between load fluctuations and energy supply fluctuations to obtain the correlation feature set and time delay information between load and energy supply;

[0006] The control strategy generation module is used to extract demand forecast features using the chi-square test, combine the ARIMA model and the long-short-term memory network model to perform demand forecasting, and adjust the charge and discharge thresholds and formulate the charge and discharge control strategy based on the grid voltage and the state of charge of the energy storage system;

[0007] The control strategy execution module is used to configure the charging, discharging, and standby modes through a signal generator, adjust control signal parameters and obtain load data in real time, transmit control instructions through a communication interface, formulate charge and discharge control strategies, and adjust the operating mode of the energy storage converter;

[0008] The collaborative execution module is used to continuously monitor voltage and current data and adjust the converter operating mode according to the grid load demand, distributed energy generation capacity and the charge and discharge status of the energy storage system. It also formulates dynamic power adjustment strategies and dynamically optimizes the power allocation strategy by coordinating distributed energy with the grid.

[0009] The dynamic power adjustment strategy adjustment module is used to dynamically update the hybrid prediction model parameters based on real-time grid load, energy storage system status and distributed energy data, and adjust the charge and discharge protection thresholds and power adjustment strategies through a feedback mechanism.

[0010] Furthermore, the data acquisition module is used to obtain energy storage system load data, distributed energy operation data, and energy storage system operation data based on the load monitoring equipment and smart meters, and perform data cleaning, preprocessing, standardization, and calibration on the obtained data, including:

[0011] According to the load monitoring equipment and smart meters, the load data of the energy storage system is obtained, including current, voltage, instantaneous power change and power factor, among which the smart meter records the load type information; by integrating with the distributed energy control system, the distributed energy operation data is obtained, including real-time output power, power change rate and energy quality; through the interface with the energy storage device, the energy storage system operation data is obtained, including voltage, current, charge state, health state and converter state; based on the obtained load data, distributed energy operation data and energy storage system operation data; using data synchronization technology, the load data, distributed energy operation data and energy storage system operation data are unified Integration is carried out in the time dimension; based on the integrated data, data cleaning and preprocessing algorithms are used to filter the data, including setting range thresholds and filtering algorithms to eliminate noise and outliers in the data acquisition process, interpolating the data, and processing null values ​​caused by equipment transmission interruptions; data standardization technology is used to standardize the preprocessed data and convert it to the same dimension; by setting several data sampling points and combining them with known standard data sources, point-by-point calibration is performed, and the weighted average method is used to smooth the errors of the acquired data. The standard data source is a known load characteristic; the calibrated data is transmitted to the central monitoring system through the communication interface.

[0012] Furthermore, the correlation feature analysis module is used to combine historical load and energy supply data, use a long short-term memory network to perform long-term trend prediction, and use a support vector machine to analyze the correlation between load fluctuations and energy supply fluctuations to obtain a correlation feature set and time delay information between load and energy supply, including:

[0013] The calibrated data is converted from the time domain to the frequency domain using the Fourier transform algorithm, and the instantaneous frequency changes of the signal are extracted through wavelet transform to identify high-frequency fluctuations of different load or supply changes; a convolutional neural network is used to extract features of short-term fluctuations to obtain the short-term fluctuation trends of load and energy supply, including their high-frequency fluctuations, instantaneous change frequency and amplitude information. The short-term fluctuation characteristics include frequency distribution, amplitude and fluctuation intensity; historical load data, historical energy supply data, and corresponding timestamp data are obtained through the central monitoring system, and a long short-term memory network is used for model training to predict long-term trend changes and seasonal fluctuations of load and energy supply; based on the short-term fluctuation trends and long-term trends of load and energy supply, the support vector machine algorithm is used to determine the correlation between load fluctuations and energy supply fluctuations, and through regression analysis and Pearson correlation coefficient calculation method, a set of correlation features between load and energy supply is obtained, including time delay, time delay correlation and time delay correlation intensity.

[0014] Furthermore, the control strategy generation module is used to extract demand forecast features using a chi-square test, perform demand forecasting in combination with an ARIMA model and a long short-term memory network model, and adjust charge and discharge thresholds and formulate a charge and discharge control strategy based on the grid voltage and the state of charge of the energy storage system, including:

[0015] Obtain historical load data, load type, energy supply data, weather data and distributed energy generation data, and use chi-square test to extract demand forecasting features related to demand forecasting, including load type, seasonal pattern, load fluctuation, and energy supply change. Seasonal pattern is the correlation feature between load and energy supply of different load types and seasons. Load types include industrial load and residential load. Based on the historical data of load demand forecasting features, a hybrid forecasting model combining ARIMA model and long short-term memory network model is used to forecast demand, including using ARIMA model to identify linear laws and seasonal patterns of demand, combining long short-term memory network model to identify nonlinear fluctuations, and predicting demand within a preset time period in the future. Obtain the current state of charge and power of the energy storage system. The system uses grid voltage data, uses the charge and discharge demand protection threshold adjustment unit to adjust the charge and discharge demand protection threshold, and formulates the charge and discharge control strategy of the energy storage system; if the difference between the grid voltage and the preset voltage upper limit threshold is less than the preset difference threshold, the charge demand protection threshold is adjusted; if the current state of charge is lower than the preset state of charge threshold, the discharge demand protection threshold is lowered; obtains sudden load changes or energy supply fluctuations in the demand forecast results to determine whether there is a risk of extreme fluctuations. If the demand increase rate is higher than the preset increase rate threshold or the energy supply decrease rate is higher than the preset speed threshold, a rapid response control strategy is generated based on real-time data and preset emergency response rules, including emergency adjustment of the charge and discharge power of the energy storage system, activation of backup energy storage resources, and emergency power interaction and coordination with the grid.

[0016] The system also includes a charge and discharge demand protection threshold adjustment unit, which is used to obtain the current state of charge of the energy storage system and the grid voltage data, adjust the charge and discharge demand protection threshold, and formulate the charge and discharge control strategy of the energy storage system. Specifically, it includes:

[0017] The state of charge of the energy storage system and the voltage of the grid are obtained based on real-time data to determine whether the charge and discharge thresholds need to be adjusted. The state of charge of the energy storage system and the voltage of the grid are obtained based on real-time data, and the grid voltage deviation and state of charge deviation are calculated. Based on the grid voltage deviation and state of charge deviation, the charging demand protection threshold adjustment formula C1 = C-(ΔV×α) is used to determine the dynamically adjusted charging demand protection threshold C1, where C is the original charging threshold, ΔV is the grid voltage deviation, and α is the sensitivity of the voltage deviation to the charging threshold. V maxis the upper limit of the grid voltage, V is the current grid voltage, P is the current grid load power, P max is the maximum load power of the power grid, γ is a constant representing the voltage stability requirement of the power grid, obtained by fitting historical data; according to the voltage deviation and state of charge deviation of the power grid, the discharge demand protection threshold adjustment formula D1 = D + (ΔS × β) is used to determine the dynamically adjusted discharge demand protection threshold D1, where D is the original discharge threshold, ΔS is the state of charge deviation, and β is the sensitivity of the state of charge deviation to the discharge threshold. is the preset target state of charge, S is the current state of charge of the energy storage system, and λ is a constant that represents the degree of influence of battery health and grid load on the discharge threshold adjustment. Based on the adjusted charging and discharging thresholds, the charge and discharge control strategy of the energy storage system is formulated.

[0018] Furthermore, the control strategy execution module is used to configure the charging, discharging and standby modes through a signal generator, adjust the control signal parameters and obtain load data in real time, transmit control instructions through a communication interface, formulate the charging and discharging control strategy and adjust the energy storage converter working mode, including:

[0019] According to the charge and discharge control strategy and the operating requirements of the energy storage system, different operating modes are configured through a signal generator and corresponding control signal parameters are determined. The operating modes include charging mode, discharging mode, and standby mode. The control signal parameters include but are limited to power size and charge and discharge rate. The current load data, energy storage system status, and grid demand data of the energy storage system are obtained, an adaptive communication protocol is configured, and control instructions are transmitted to the energy storage device in real time through a communication interface, which may include a CAN bus or Ethernet. By calculating the current load demand, the charge state of the energy storage system, and the grid load fluctuation, the power and time period required for charging and discharging are determined, and control strategy instructions are formulated, including control parameters such as charging power, discharging power, and duration. By sending the strategy instructions to the energy storage converter control unit, the execution unit controls the charging and discharging process of the energy storage device according to the instructions. Real-time status information of the energy storage converter control unit is obtained, including the current charge and discharge status, converter operating mode, and output power. The converter operating mode includes bidirectional conversion mode and constant current mode. The output of the energy storage converter is adjusted according to the set power range through the energy storage converter control unit to perform charging and discharging operations.

[0020] Furthermore, the collaborative execution module is used to continuously monitor voltage and current data and adjust the converter operating mode according to the grid load demand, distributed energy generation capacity and energy storage system charge and discharge status, formulate a dynamic power adjustment strategy, and dynamically optimize the power allocation strategy by scheduling distributed energy and working in coordination with the grid, including:

[0021] The system continuously monitors real-time voltage and current data during the charging and discharging process, and adjusts the converter operating mode in real time based on this monitoring information. It obtains information on the total grid demand, the real-time generation capacity of distributed energy resources, and the charge and discharge status of the energy storage system, determines the grid load demand information and the energy supply capacity of each distributed energy source, and dynamically adjusts the generation power of distributed energy resources by coordinating the distributed energy resources with the grid to supplement the energy storage system's energy shortage or regulate excess energy. It obtains real-time grid load information, distributed energy generation data, and the charge and discharge status of the energy storage system, and uses a dynamic programming algorithm to dynamically formulate a dynamic power adjustment strategy based on the power allocation optimization objective setting. The power allocation optimization objectives include grid load balancing, efficient utilization of the energy storage system, and maximum efficiency output of distributed energy resources, while avoiding overload or unstable fluctuations. The dynamic adjustment strategy includes increasing the discharge power of the energy storage system when the grid load fluctuation exceeds a preset fluctuation threshold, prioritizing the use of the energy storage system when the grid voltage is below a preset voltage threshold, and prioritizing the dispatch of distributed energy resources when their generation capacity exceeds a preset capacity threshold, while reducing the load on the energy storage system based on grid demand.

[0022] Furthermore, the dynamic power adjustment strategy adjustment module is used to dynamically update the hybrid prediction model parameters based on real-time grid load, energy storage system status, and distributed energy data, and adjust the charge and discharge protection thresholds and power adjustment strategy through a feedback mechanism, including:

[0023] The grid load, energy storage system status and distributed energy data are acquired in real time through the communication interface and transmitted to the central monitoring system; the real-time acquired grid load, energy storage system status and distributed energy data are aggregated into the human-computer interaction interface, and the human-computer interaction interface is used to display the operating status information of the energy storage system, load and distributed energy, the real-time forecast value of demand, the dynamically changing protection threshold and the potential demand risk warning information; the real-time data is used to update the parameters of the hybrid prediction model, and the demand within the preset time period in the future is predicted. According to the prediction results, the current state of charge value of the energy storage system and the grid voltage data, the charging demand protection threshold and the discharging demand protection threshold are dynamically adjusted; during the operation of the energy storage system, feedback data is acquired in real time and recorded All relevant control instructions and execution results are used to identify the error between demand forecast and actual demand, determine the responsiveness of the protection strategy, and transmit feedback data to the central monitoring system and the hybrid prediction model; the hybrid prediction model is optimized using an error correction algorithm, the optimized hybrid prediction model is re-run for demand forecast, and the actual operating effect of the optimized hybrid prediction model is evaluated; the parameters of the dynamic power adjustment strategy are adjusted according to the optimized demand forecast value; the actual operating status is compared with the forecast value through a real-time monitoring and feedback mechanism to determine the real-time effect of the hybrid prediction model and the dynamic power adjustment strategy, and the parameters of the hybrid prediction model and the dynamic power adjustment strategy are adjusted using the real-time monitoring data through an automatic feedback system.

[0024] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0025] The present invention provides an energy storage and energy management system with an improved demand protection algorithm. This system effectively balances grid load and voltage by adjusting the energy storage system's charge and discharge patterns in real time to address grid load fluctuations and distributed energy generation fluctuations, reducing power waste and improving grid stability and reliability. By dynamically adjusting the energy storage system's charge and discharge control strategy, it can rapidly respond to grid load peaks or sudden load fluctuations, ensuring that the grid does not fail due to excessive load or unstable voltage. Furthermore, by continuously monitoring grid load, energy storage system status, and distributed energy operating data, the system can sense and respond to changes in grid load and energy supply fluctuations in real time. Combining real-time data with dynamic scheduling strategies, the system optimizes power distribution, ensuring that the energy storage system and distributed energy resources achieve maximum efficiency during optimal times, thereby reducing reliance on traditional power grids and improving the grid's demand response capabilities. The system precisely adjusts charge and discharge thresholds and protection strategies based on varying load demands and grid voltage conditions, ensuring that the energy storage system maintains optimal operating conditions under various operating conditions. Through efficient operating strategies, the system improves the adaptability of the energy storage system, enabling it to better adapt to varying load fluctuations and energy supply conditions, thereby enhancing the overall stability and security of the power grid. Therefore, the present invention realizes the efficient operation of the energy storage system through an intelligent control and scheduling mechanism, which not only improves the charging and discharging efficiency of the energy storage system, but also enhances the stability and flexibility of the power grid, providing effective protection for the safe operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of an energy storage energy management system with an improved demand protection algorithm according to the present invention;

[0027] Figure 2 A schematic diagram of an energy storage energy management system with an improved demand protection algorithm according to the present invention;

[0028] Figure 3 This is another schematic diagram of an energy storage energy management system with an improved demand protection algorithm according to the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical strategy and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] like Figure 1-3 In this embodiment, an energy storage energy management system with an improved demand protection algorithm may specifically include:

[0031] Step S101: The data acquisition module is used to obtain energy storage system load data, distributed energy operation data and energy storage system operation data based on load monitoring equipment and smart meters, and perform data cleaning, preprocessing, standardization and calibration on the obtained data.

[0032] Energy storage system load data, including current, voltage, instantaneous power change, and power factor, is acquired through load monitoring devices and smart meters. Smart meters record load type information. Integration with distributed energy resource control systems is used to acquire distributed energy resource operating data, including real-time output power, power change rate, and energy quality. Energy storage system operating data, including voltage, current, state of charge, health status, and converter status, is acquired through interfaces with energy storage devices. Based on the acquired load data, distributed energy resource operating data, and energy storage system operating data, data synchronization technology is used to integrate the load data, distributed energy resource operating data, and energy storage system operating data on a unified time dimension. Data cleaning and preprocessing algorithms are used to filter the integrated data. These include setting range thresholds and filtering algorithms to eliminate noise and outliers during data acquisition, interpolating data, and handling null values ​​caused by device transmission interruptions. Data normalization technology is used to standardize the preprocessed data and convert it to the same dimension. By setting several data sampling points and combining them with a known standard data source, point-by-point calibration is performed, and the weighted average method is used to smooth the errors in the acquired data. The standard data source is a known load characteristic. The calibrated data is transmitted to the central monitoring system through the communication interface.

[0033] For example, an energy storage system connects a residential area's loads, multiple distributed energy units, and a central monitoring system. The energy storage system includes a smart meter that monitors the load's current, voltage, instantaneous power change, and power factor. The smart meter also records load type information. The residential area's loads include air conditioners, electric water heaters, and lighting, while the distributed energy system consists of several solar photovoltaic panels and a small wind turbine. Based on the operating information of these devices, data is transmitted to the central monitoring system via a high-speed communication interface. Data synchronization technology is used to integrate load data, distributed energy data, and energy storage system operating data on a unified time scale. Within a 10-minute sampling period, the load monitoring device and smart meter capture load data for the energy storage system, including current of 10A, voltage of 220V, power of 2200W, and power factor of 0.95. Integration with the distributed energy control system captures distributed energy operating data, including the output power of the solar photovoltaic panels of 1200W with a power variation rate of 3%, and the output power of the wind turbine of 800W with a power variation rate of 1%. Through the interface with the energy storage device, energy storage system operating data is obtained, including voltage of 48V, current of 5A, state of charge (SOC) of 60%, good energy storage system health, and normal converter operation. Data synchronization technology integrates load data, distributed energy resource data, and energy storage system operating data on a unified time scale. Data is cleaned by setting range thresholds and filtering algorithms. If the collected voltage and power data within a 10-minute period fluctuates within the normal operating range, does not exceed the preset voltage range of ±10V, and does not exhibit abnormal power fluctuations, the data is considered reliable and not filtered. If any measurement values ​​are found to be abnormal, such as current values ​​exceeding the maximum safe operating current or power factor not within the specified valid range, the set filtering algorithm automatically removes these unreasonable data. After data cleaning, the pre-processed data needs to be standardized to a standard unit. For example, current units need to be converted to standardized current values, and voltage units need to be converted to standardized voltage values. At this point, all voltage, current, and power data are converted to standard units using standardization formulas. Point-by-point calibration is performed based on known standard data sources. For example, if the standard current of an air conditioner under a certain load is known to be 9A, but the monitored current is 10A, the error in this data is smoothed and corrected using a weighted average method to obtain a more accurate current value. This calibrated and standardized data will provide the basis for subsequent load forecasting, energy storage scheduling, and grid coordination. After data cleaning, standardization, and calibration, the data will be input into the central monitoring system.

[0034] Step S102, the correlation feature analysis module is used to combine historical load and energy supply data, use the long short-term memory network to perform long-term trend prediction, and use the support vector machine to analyze the correlation between load fluctuations and energy supply fluctuations to obtain the correlation feature set and time delay information between load and energy supply.

[0035] The calibrated data is converted from the time domain to the frequency domain using a Fourier transform algorithm. Wavelet transforms are then used to extract the instantaneous frequency changes of the signal, identifying high-frequency fluctuations associated with different load or supply changes. A convolutional neural network is used to extract features from short-term fluctuations, identifying short-term fluctuation trends of load and energy supply, including high-frequency fluctuations, instantaneous frequency changes, and amplitude information. Short-term fluctuation characteristics include frequency distribution, amplitude, and fluctuation intensity. Historical load and energy supply data, along with corresponding timestamp data, are obtained from a central monitoring system. A long-short-term memory network is used for model training to predict long-term trends and seasonal fluctuations in load and energy supply. Based on the short-term and long-term trends of load and energy supply, a support vector machine algorithm is used to determine the correlation between load and energy supply fluctuations. Regression analysis and Pearson correlation coefficient calculation are used to determine the correlation feature set between load and energy supply, including time delay, time delay correlation, and time delay correlation strength.

[0036] For example, a smart grid system includes multiple load devices, including air conditioners, lighting, and electric water heaters, and a set of distributed energy systems, including solar photovoltaic and wind turbines. Smart meters and monitoring equipment record load and energy supply data in real time, including time series of current, voltage, power factor, and so on. Each data point includes a timestamp. For calibrated load and energy supply data, such as the load power time series of [2200, 2250, 2300, 2250, 2200] W over a certain period, this data is converted from the time domain to the frequency domain using a Fourier transform algorithm. Frequency domain data is then generated, and higher-frequency fluctuations in the data, such as rapid fluctuations in electricity demand, are identified. Using the Fourier transform, these instantaneous fluctuations are extracted into different frequency components, identifying possible high-frequency noise or load fluctuations. The data is further processed using a wavelet transform algorithm to extract instantaneous frequency variations in the signal and identify short-term, high-frequency variations in load or supply fluctuations. For example, if the instantaneous frequency of load power fluctuates frequently between 10 and 20 minutes within an hour, this may be due to the startup and shutdown of air conditioning equipment. The wavelet transform algorithm extracts these instantaneous frequency variations and accurately captures the frequency, amplitude, and intensity of each fluctuation. If high-frequency load power fluctuation data is extracted within a certain period, including a frequency of 10 Hz, an amplitude of 50 W, and a fluctuation intensity of 30%, a convolutional neural network is used to extract features of these short-term fluctuations in load and energy supply, generating a feature set that includes the frequency distribution and trend of these fluctuations. Historical load and energy supply data were collected from a central monitoring system. This data included load power data for the past six months, along with corresponding seasonal data, such as higher load in summer and lower load in winter. A long short-term memory (LSTM) network was used to train a model to analyze long-term trends and seasonal fluctuations in the load. The LSTM model found that load power averaged 2500W in the summer and 2000W in the winter, with annual cyclical fluctuations. Based on the short-term and long-term trends of load and energy supply fluctuations, a support vector machine (SVM) algorithm was used to determine the correlation between load and energy supply fluctuations. For example, the SVM algorithm revealed a strong correlation between load and energy supply fluctuations, particularly during the summer peak load period, when solar photovoltaic power generation output is high, and the fluctuations in both trends exhibited synchronized behavior. This correlation was further quantified using regression analysis and Pearson correlation coefficient calculation, resulting in a time lag of 2 hours and a correlation strength of 0.85, indicating that during certain periods of time, the time lag and correlation between load and energy supply fluctuations were very strong.

[0037] Step S103, a control strategy generation module is used to extract demand forecast features using a chi-square test, perform demand forecasting in combination with an ARIMA model and a long short-term memory network model, and adjust charge and discharge thresholds and formulate a charge and discharge control strategy based on the grid voltage and the state of charge of the energy storage system.

[0038] Historical load data, load type, energy supply data, weather data, and distributed energy generation data are collected. A chi-square test is used to extract demand forecasting features relevant to demand prediction, including load type, seasonal patterns, load fluctuations, and energy supply variations. Seasonal patterns are the correlations between load and energy supply across different load types and seasons. Load types include industrial and residential loads. Based on these historical data, a hybrid forecasting model combining the ARIMA model and the long-short-term memory network model is used to forecast demand. This model uses the ARIMA model to identify linear patterns and seasonal patterns in demand, and the long-short-term memory network model to identify nonlinear fluctuations, predicting demand for a preset future time period. The energy storage system's current state of charge and grid voltage data are obtained. The charge and discharge demand protection threshold adjustment unit is used to adjust the charge and discharge demand protection thresholds, and a charge and discharge control strategy for the energy storage system is formulated. If the difference between the grid voltage and the preset voltage upper threshold is less than the preset difference threshold, the charge demand protection threshold is adjusted. If the current state of charge is lower than the preset state of charge threshold, the discharge demand protection threshold is lowered. Obtain information on sudden load changes or energy supply fluctuations in the demand forecast results to determine whether there is a risk of extreme fluctuations. If the demand increase rate is higher than the preset increase rate threshold or the energy supply decrease rate is higher than the preset speed threshold, a rapid response control strategy is generated based on real-time data and preset emergency response rules, including emergency adjustment of the charging and discharging power of the energy storage system, activation of backup energy storage resources, and emergency power interaction and coordination with the power grid.

[0039] For example, in a power management system, smart meters, energy storage systems, and distributed energy devices collect real-time load data, load types, distributed energy generation data, weather data, and grid data. Load data includes current, voltage, and power factor; load types include residential and industrial loads; distributed energy generation data includes real-time solar and wind power generation; weather data includes temperature and humidity; and grid data includes voltage and frequency. Using this data, a chi-square test is used to extract features relevant to demand forecasting. For example, if the past six months of data reveal that residential loads exhibit higher volatility in the winter (December to February) and the summer (June to August), while industrial loads exhibit less volatility in the spring and autumn, these seasonal patterns can be used to identify seasonal correlations between loads and energy supply. In particular, the correlation between residential loads and temperature changes can be identified. For example, when the temperature is above 30°C, residential electricity demand increases, particularly in terms of air conditioning usage. Based on these load demand characteristics, a hybrid forecasting model combining the ARIMA model and the LSTM model was used to predict demand. The ARIMA model identified linear and seasonal patterns in demand and predicted load demand for the week ahead. The ARIMA model predicted that load demand would reach 3,000 kW in July, with peak load occurring between 2:00 PM and 5:00 PM during warm weather. Furthermore, the LSTM model further identified nonlinear fluctuations in load demand, predicting significant fluctuations during these periods, especially during extreme weather conditions. Therefore, the LSTM model provided more accurate demand forecasts, predicting that load demand could reach 3,300 kW during these periods. Real-time data for the energy storage system was obtained, including the current state of charge of the energy storage system at 40%, the grid voltage at 240 V, the preset upper voltage threshold at 250 V, the voltage difference at 10 V, and the preset voltage difference threshold at 5 V. In this case, according to system settings, the difference of 10V between the grid voltage and the upper voltage threshold is greater than the preset difference threshold of 5V. Therefore, the charging demand protection threshold needs to be adjusted to prevent further charging from potentially causing the grid voltage to overshoot. However, the energy storage system's state of charge is below the preset threshold of 50%, so the discharge demand protection threshold is lowered to prevent damage to the energy storage equipment from excessive discharge. Information about load changes and energy supply fluctuations is obtained from the demand forecast results. If, during a certain period, the load demand is monitored to increase from 2500kW to 2875kW within one hour, the rate of increase in load demand reaches 15% of the preset rate of increase threshold. At the same time, the system detects a downward trend in solar power output. Solar power generation decreases from 500kW to 410kW during the same period, representing an 18% rate of decrease, indicating that the system faces the risk of a sudden load increase and insufficient energy supply.Based on this real-time data, an emergency response control strategy was generated through preset emergency response rules, including emergency adjustment of the charging and discharging power of the energy storage system, switching from discharge mode to charging mode, and activating backup energy storage resources. At the same time, emergency power interaction was carried out with the power grid to stabilize the grid load and respond to sudden fluctuations.

[0040] Among them, the charge and discharge demand protection threshold adjustment unit is used to obtain the current charge state and grid voltage data of the energy storage system, adjust the charge and discharge demand protection threshold, and formulate the charge and discharge control strategy of the energy storage system.

[0041] The state of charge of the energy storage system and the voltage of the grid are obtained based on real-time data to determine whether the charge and discharge thresholds need to be adjusted. The state of charge of the energy storage system and the voltage of the grid are obtained based on real-time data, and the grid voltage deviation and state of charge deviation are calculated. According to the grid voltage deviation and state of charge deviation, the charging demand protection threshold adjustment formula C1 = C-(ΔV×α) is used to determine the dynamically adjusted charging demand protection threshold C1, where C is the original charging threshold, ΔV is the grid voltage deviation, and α is the sensitivity of the voltage deviation to the charging threshold. V max is the upper limit of the grid voltage, V is the current grid voltage, P is the current grid load power, P max is the maximum load power of the power grid, and γ is a constant representing the voltage stability requirement of the power grid, obtained by fitting historical data. Based on the grid voltage deviation and state of charge deviation, the discharge demand protection threshold adjustment formula D1 = D + (ΔS × β) is used to determine the dynamically adjusted discharge demand protection threshold D1, where D is the original discharge threshold, ΔS is the state of charge deviation, and β is the sensitivity of the state of charge deviation to the discharge threshold. is the preset target state of charge, S is the current state of charge of the energy storage system, and λ is a constant that represents the degree to which battery health and grid load influence the discharge threshold adjustment. Based on the adjusted charge and discharge thresholds, a charge and discharge control strategy for the energy storage system is formulated.

[0042] For example, in an actual application scenario, if the original charging threshold C of the energy storage system is 500kW, the upper limit of the grid voltage Vmax is set to 1.1kV, the current grid voltage V is 1.05kV, the grid load power P is 800MW, and the maximum load power Pmax of the grid is 1000MW. The sensitivity of voltage deviation to the charging threshold α is expressed by the formula The result is 10, which means that every 1% voltage deviation will cause the charging threshold to change by 10kW, where γ is a constant that represents the voltage stability requirement of the power grid, obtained by fitting historical data. The grid voltage deviation is calculated to be 0.05kV. Using the charging demand protection threshold adjustment formula C1 = C-(ΔV×α), the dynamically adjusted charging demand protection threshold C1 is determined to be 550kV. Therefore, the dynamically adjusted charging demand protection threshold is 550kW. If the original discharge threshold D of the energy storage system is 300kW, the target state of charge The current state of charge S of the energy storage system is 60%. The sensitivity of the state of charge deviation to the discharge threshold β is expressed by the formula The result is 20, indicating that every 1% SOC deviation results in a 20kW change in the discharge threshold. λ is a constant representing the degree to which battery health and grid load influence the discharge threshold adjustment. The SOC of the energy storage system is acquired through real-time data, and the SOC deviation is calculated to be 20%. Using the discharge demand protection threshold adjustment formula D1 = D + (ΔS × β), the dynamically adjusted discharge demand protection threshold D1 is calculated to be 620kW. With these dynamically adjusted charging and discharging thresholds, the energy storage system adjusts its charging and discharging strategy based on the current grid voltage deviation and the energy storage system's SOC. This includes increasing the charging threshold when the grid voltage deviation is large, allowing the energy storage system to charge to support grid stability. When the energy storage system's SOC is low, the discharge threshold is increased, allowing the energy storage system to discharge faster to meet load demand.

[0043] Step S104, a control strategy execution module is used to configure the charging, discharging and standby modes through a signal generator, adjust control signal parameters and obtain load data in real time, transmit control instructions through a communication interface, formulate charge and discharge control strategies and adjust the energy storage converter working mode.

[0044] Based on the charge and discharge control strategy and the energy storage system's operational requirements, a signal generator is used to configure different operating modes and determine corresponding control signal parameters. Operating modes include charging mode, discharging mode, and standby mode. Control signal parameters include, but are limited to, power level and charge and discharge rate. Current energy storage system load data, energy storage system status, and grid demand data are acquired, and an appropriate communication protocol is configured. Control instructions are then transmitted to the energy storage device in real time via a communication interface, such as a CAN bus or Ethernet. By calculating the current load demand, the energy storage system's state of charge, and grid load fluctuations, the required charging and discharging power and time periods are determined, and control strategy instructions are formulated, including control parameters for charging power, discharging power, and duration. These strategy instructions are sent to the energy storage converter control unit, and the execution unit controls the charging and discharging process of the energy storage device accordingly. Real-time status information is acquired from the energy storage converter control unit, including the current charge and discharge status, converter operating mode, and output power. Converter operating modes include bidirectional conversion mode and constant current mode. The converter control unit adjusts the converter output within the set power range to execute charging and discharging operations.

[0045] For example, in a specific application scenario, the energy storage system's current state of charge (SOC) is 70%, the grid load demand is 1200 kW, and the energy storage system's maximum charge and discharge power is 500 kW. Based on real-time load data and grid demand, the energy storage system needs to adjust its operating mode to meet grid demand and ensure stable operation. If the current load demand exceeds the expected load demand by 100 kW, and the grid load is expected to continue to increase within the next hour, the energy storage system needs to respond quickly to this increased demand. Based on the current SOC and grid demand data, an adaptive operating mode is configured. Due to significant grid load fluctuations, the energy storage system is set to discharge mode and the discharge power is set to 400 kW, with the remaining 100 kW used for backup load regulation. The system calculates that the discharge duration should be one hour to help balance the grid load. At this point, the energy storage system's charge and discharge rate is set to 400 kW, and control signals are sent to the energy storage converter control unit via a high-speed communication interface such as the CAN bus or Ethernet, instructing the converter to begin discharging at this rate. During this process, the energy storage system continuously monitors its operating status, obtaining the converter's real-time output power and operating mode. If the converter's operating mode experiences an anomaly or power output fails to meet the target, the system automatically adjusts the control signal to ensure smooth charging and discharging. If the energy storage system's state of charge drops to 40%, a determination is made as to whether the operating mode needs to be adjusted. If the grid load continues to rise, the energy storage system may need to increase discharge power or switch to charging mode for reverse compensation. Otherwise, when the grid load returns to normal, the energy storage system will switch to standby mode, maintaining a backup state to cope with future load fluctuations.

[0046] Step S105, the collaborative execution module is used to continuously monitor voltage and current data and adjust the converter operating mode according to the grid load demand, distributed energy generation capacity and the charging and discharging status of the energy storage system, formulate a dynamic power adjustment strategy, and dynamically optimize the power allocation strategy by scheduling distributed energy to work in coordination with the grid.

[0047] The system continuously monitors real-time voltage and current data during the charging and discharging process, and adjusts the converter operating mode in real time based on this information. It obtains information on the grid's total demand, the real-time generation capacity of distributed energy resources, and the charge and discharge status of the energy storage system. This information determines the grid's load demand and the energy supply capacity of each distributed energy resource. By coordinating distributed energy resources with the grid, it dynamically adjusts the generation power of distributed energy resources to supplement energy shortages or regulate excess energy in the energy storage system. By obtaining real-time grid load information, distributed energy resource generation data, and the charge and discharge status of the energy storage system, it uses a dynamic programming algorithm to dynamically formulate a dynamic power adjustment strategy based on power allocation optimization objectives. These power allocation optimization objectives include grid load balance, efficient utilization of the energy storage system, and maximum efficiency output of distributed energy resources, while avoiding overload or unstable fluctuations. The dynamic adjustment strategy includes increasing the discharge power of the energy storage system when grid load fluctuations exceed a preset fluctuation threshold, prioritizing the use of the energy storage system when the grid voltage falls below a preset voltage threshold, and prioritizing the dispatch of distributed energy resources when their generation capacity exceeds a preset capacity threshold, while reducing the load on the energy storage system based on grid demand.

[0048] For example, in a typical energy management system, the grid's real-time load demand is 3000kW, the real-time generation capacity of solar and wind power is 1000kW, and the energy storage system's current state of charge is 60%. Its charging power limit is 500kW, and its discharge power limit is 400kW. Real-time grid load information is obtained, including the grid's load demand of 3000kW and the distributed energy resource generation capacity of 1000kW, meaning the remaining load demand is 2000kW. Since the energy storage system is currently at a 60% state of charge, this information is used to determine the grid's power demand and determine that the system can provide a maximum discharge power of 400kW. A feedback mechanism monitors real-time grid voltage and current data to ensure stable converter operation during charging and discharging. If significant grid load fluctuations are detected and the voltage drops below a set voltage threshold of 210V, the energy storage system is prioritized to provide power, preventing further drops in the grid voltage and ensuring grid stability. If the grid load fluctuation exceeds the preset fluctuation threshold of 500kW, a dynamic programming algorithm is used to optimize power allocation, adjusting it according to the predetermined power allocation target. Since the grid load demand is still high at 2000kW, and the distributed energy resources' generation capacity is only 1000kW, the dynamic power adjustment strategy increases the energy storage system's discharge power, for example, to 350kW, to meet the remaining power demand and prevent over-discharge of the energy storage system. If the solar power generation capacity exceeds 800kW and the wind power output is 200kW, this total of 1000kW reaches the maximum efficiency output of the distributed energy resources. To optimize energy use, these distributed energy resources are prioritized, reducing the load demand on the energy storage system, thereby maintaining effective utilization of the energy storage system and preventing over-discharge. The energy storage system's discharge power is adjusted to 150kW to ensure it does not exceed its maximum discharge power limit. By effectively coordinating the output of distributed energy resources, the grid load is balanced and the grid voltage remains within a stable range.

[0049] Step S106, a dynamic power adjustment strategy adjustment module is used to dynamically update the hybrid prediction model parameters according to the real-time acquired grid load, energy storage system status and distributed energy data, and adjust the charge and discharge protection threshold and power adjustment strategy through a feedback mechanism.

[0050] Real-time grid load, energy storage system status, and distributed energy resource data are acquired through a communication interface and transmitted to a central monitoring system. This real-time data is aggregated on a human-computer interface, which displays the operating status of the energy storage system, load, and distributed energy resources, along with real-time demand forecasts, dynamically changing protection thresholds, and potential demand risk warnings. Real-time data is used to update the parameters of the hybrid prediction model and predict demand for a preset time period. The charging and discharging demand protection thresholds are dynamically adjusted based on the forecast results, the energy storage system's current state of charge, and grid voltage data. During energy storage system operation, real-time feedback data is acquired and all relevant control commands and execution results are recorded. Errors between demand forecasts and actual demand are identified, and the responsiveness of the protection strategy is determined. This feedback data is then transmitted to the central monitoring system and the hybrid prediction model. An error correction algorithm is used to optimize the hybrid prediction model, which is then rerun for demand forecasting and evaluated for its performance. The parameters of the dynamic power adjustment strategy are adjusted based on the optimized demand forecasts. Through real-time monitoring and feedback mechanisms, the actual operating status is compared with the predicted value to determine the real-time effects of the hybrid prediction model and the dynamic power adjustment strategy. Through an automatic feedback system, the parameters of the hybrid prediction model and the dynamic power adjustment strategy are adjusted using real-time monitoring data.

[0051] For example, in a smart grid system, the real-time grid load is 3200 kW, the energy storage system's current state of charge is 75%, the energy storage device's charging power limit is 500 kW, the discharge power limit is 400 kW, and the real-time generating capacity of distributed energy resources, including solar and wind power, is 1500 kW. This data is transmitted to a central monitoring system via a high-speed communication interface. The interface displays the operating status of the energy storage system, load, and distributed energy resources, as well as real-time demand forecasts and dynamically changing protection thresholds. At this point, the hybrid prediction model predicts a demand value of 3500 kW for the next hour. Comparing this with real-time data reveals a 300 kW error between the predicted demand and actual load demand, indicating a significant error in the prediction model. Based on this information, all relevant control commands are recorded, and actual demand data is transmitted to the central monitoring system and the hybrid prediction model through a feedback mechanism to update model parameters and perform error correction. During this process, an error correction algorithm is used to optimize the hybrid prediction model. The optimized forecast shows a demand value of 3400 kW for the next hour, which is more accurate than the initial forecast. Based on the optimized demand forecast, the charging and discharging demand protection thresholds are dynamically adjusted. For example, the charging demand protection threshold is adjusted from 2500kW to 2700kW, while the discharging demand protection threshold is adjusted from 2000kW to 2100kW to accommodate the upcoming load peak. The power allocation strategy is dynamically adjusted based on the new demand forecast, the energy storage system's state of charge of 75%, and a grid voltage of 220V. To balance grid load and energy use in the energy storage system, the energy storage system provides 300kW of discharge power, ensuring grid stability based on grid voltage and load fluctuations. When the grid load exceeds 3000kW, distributed energy resources are prioritized. When the grid voltage shows a downward trend, such as below 215V, the energy storage system is prioritized. Through real-time monitoring, the system compares the actual operating status with the predicted value. If a new prediction error is found, the automatic feedback mechanism is activated. Based on the new error, the parameters of the hybrid prediction model and dynamic power adjustment strategy are automatically adjusted to further optimize the grid load scheduling and the charging and discharging process of the energy storage system, ensuring the reliability and stability of the grid power supply.

[0052] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the concept of this application. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. An energy storage energy management system with an improved demand protection algorithm, characterized in that: The system comprises: The data acquisition module is used to obtain energy storage system load data, distributed energy operation data, and energy storage system operation data based on load monitoring equipment and smart meters, and to clean, preprocess, standardize, and calibrate the acquired data; The correlation feature analysis module is used to combine historical load and energy supply data, use long-term short-term memory networks to perform long-term trend forecasts, and use support vector machines to analyze the correlation between load fluctuations and energy supply fluctuations to obtain the correlation feature set and time delay information between load and energy supply; The control strategy generation module is used to extract demand forecast features using the chi-square test, combine the ARIMA model and the long-short-term memory network model to perform demand forecasting, and adjust the charge and discharge thresholds and formulate the charge and discharge control strategy based on the grid voltage and the state of charge of the energy storage system; The control strategy execution module is used to configure the charging, discharging, and standby modes through a signal generator, adjust control signal parameters and obtain load data in real time, transmit control instructions through a communication interface, formulate charge and discharge control strategies, and adjust the operating mode of the energy storage converter; The collaborative execution module is used to continuously monitor voltage and current data and adjust the converter operating mode according to the grid load demand, distributed energy generation capacity and the charge and discharge status of the energy storage system. It also formulates dynamic power adjustment strategies and dynamically optimizes the power allocation strategy by coordinating distributed energy with the grid. The dynamic power adjustment strategy adjustment module is used to dynamically update the hybrid prediction model parameters based on real-time grid load, energy storage system status and distributed energy data, and adjust the charge and discharge protection thresholds and power adjustment strategies through a feedback mechanism.

2. The system according to claim 1, wherein: The data acquisition module is used to obtain energy storage system load data, distributed energy operation data, and energy storage system operation data based on load monitoring equipment and smart meters, and to perform data cleaning, preprocessing, standardization, and calibration on the obtained data, including: According to the load monitoring equipment and smart meters, the load data of the energy storage system is obtained, including current, voltage, instantaneous power change and power factor, among which the smart meter records the load type information; by integrating with the distributed energy control system, the distributed energy operation data is obtained, including real-time output power, power change rate and energy quality; through the interface with the energy storage device, the energy storage system operation data is obtained, including voltage, current, charge state, health state and converter state; based on the obtained load data, distributed energy operation data and energy storage system operation data; using data synchronization technology, the load data, distributed energy operation data and energy storage system operation data are unified Integration is carried out in the time dimension; based on the integrated data, data cleaning and preprocessing algorithms are used to filter the data, including setting range thresholds and filtering algorithms to eliminate noise and outliers in the data acquisition process, interpolating the data, and processing null values ​​caused by equipment transmission interruptions; data standardization technology is used to standardize the preprocessed data and convert it to the same dimension; by setting several data sampling points and combining them with known standard data sources, point-by-point calibration is performed, and the weighted average method is used to smooth the errors of the acquired data. The standard data source is a known load characteristic; the calibrated data is transmitted to the central monitoring system through the communication interface.

3. The system according to claim 1, wherein: The correlation feature analysis module is used to combine historical load and energy supply data, use a long-term short-term memory network to perform long-term trend prediction, and use a support vector machine to analyze the correlation between load fluctuations and energy supply fluctuations to obtain a correlation feature set and time delay information between load and energy supply, including: The calibrated data is converted from the time domain to the frequency domain using the Fourier transform algorithm, and the instantaneous frequency changes of the signal are extracted through wavelet transform to identify high-frequency fluctuations of different load or supply changes; a convolutional neural network is used to extract features of short-term fluctuations to obtain the short-term fluctuation trends of load and energy supply, including their high-frequency fluctuations, instantaneous change frequency and amplitude information. The short-term fluctuation characteristics include frequency distribution, amplitude and fluctuation intensity; historical load data, historical energy supply data, and corresponding timestamp data are obtained through the central monitoring system, and a long short-term memory network is used for model training to predict long-term trend changes and seasonal fluctuations of load and energy supply; based on the short-term fluctuation trends and long-term trends of load and energy supply, the support vector machine algorithm is used to determine the correlation between load fluctuations and energy supply fluctuations, and through regression analysis and Pearson correlation coefficient calculation method, a set of correlation features between load and energy supply is obtained, including time delay, time delay correlation and time delay correlation intensity.

4. The system according to claim 1, wherein: The control strategy generation module is used to extract demand forecast features using the chi-square test, combine the ARIMA model and the long short-term memory network model to perform demand forecasting, and adjust the charge and discharge thresholds and formulate the charge and discharge control strategy based on the grid voltage and the energy storage system state of charge, including: Obtain historical load data, load type, energy supply data, weather data and distributed energy generation data, and use chi-square test to extract demand forecasting features related to demand forecasting, including load type, seasonal pattern, load fluctuation, and energy supply change. Seasonal pattern is the correlation feature between load and energy supply of different load types and seasons. Load types include industrial load and residential load. Based on the historical data of load demand forecasting features, a hybrid forecasting model combining ARIMA model and long short-term memory network model is used to forecast demand, including using ARIMA model to identify linear laws and seasonal patterns of demand, combining long short-term memory network model to identify nonlinear fluctuations, and predicting demand within a preset time period in the future. Obtain the current state of charge and power of the energy storage system. The system uses grid voltage data, uses the charge and discharge demand protection threshold adjustment unit to adjust the charge and discharge demand protection threshold, and formulates the charge and discharge control strategy of the energy storage system; if the difference between the grid voltage and the preset voltage upper limit threshold is less than the preset difference threshold, the charge demand protection threshold is adjusted; if the current state of charge is lower than the preset state of charge threshold, the discharge demand protection threshold is lowered; obtains sudden load changes or energy supply fluctuations in the demand forecast results to determine whether there is a risk of extreme fluctuations. If the demand increase rate is higher than the preset increase rate threshold or the energy supply decrease rate is higher than the preset speed threshold, a rapid response control strategy is generated based on real-time data and preset emergency response rules, including emergency adjustment of the charge and discharge power of the energy storage system, activation of backup energy storage resources, and emergency power interaction and coordination with the grid.

5. The system according to claim 4, wherein: The charge and discharge demand protection threshold adjustment unit is used to obtain the current state of charge of the energy storage system and the grid voltage data, adjust the charge and discharge demand protection threshold, and formulate the charge and discharge control strategy of the energy storage system, including: The state of charge of the energy storage system and the voltage of the grid are obtained based on real-time data to determine whether the charge and discharge thresholds need to be adjusted. The state of charge of the energy storage system and the voltage of the grid are obtained based on real-time data, and the grid voltage deviation and state of charge deviation are calculated. Based on the grid voltage deviation and state of charge deviation, the charging demand protection threshold adjustment formula C1 = C-(ΔV×α) is used to determine the dynamically adjusted charging demand protection threshold C1, where C is the original charging threshold, ΔV is the grid voltage deviation, and α is the sensitivity of the voltage deviation to the charging threshold. V max is the upper limit of the grid voltage, V is the current grid voltage, P is the current grid load power, P max is the maximum load power of the power grid, γ is a constant representing the voltage stability requirement of the power grid, obtained by fitting historical data; according to the voltage deviation and state of charge deviation of the power grid, the discharge demand protection threshold adjustment formula D1 = D + (ΔS × β) is used to determine the dynamically adjusted discharge demand protection threshold D1, where D is the original discharge threshold, ΔS is the state of charge deviation, and β is the sensitivity of the state of charge deviation to the discharge threshold. is the preset target state of charge, S is the current state of charge of the energy storage system, and λ is a constant that represents the degree of influence of battery health and grid load on the discharge threshold adjustment. Based on the adjusted charging and discharging thresholds, the charge and discharge control strategy of the energy storage system is formulated.

6. The system according to claim 1, wherein: The control strategy execution module is used to configure the charging, discharging and standby modes through a signal generator, adjust the control signal parameters and obtain load data in real time, transmit control instructions through a communication interface, formulate the charging and discharging control strategy and adjust the energy storage converter operating mode, including: According to the charge and discharge control strategy and the operating requirements of the energy storage system, different operating modes are configured through a signal generator and corresponding control signal parameters are determined. The operating modes include charging mode, discharging mode, and standby mode. The control signal parameters include but are limited to power size and charge and discharge rate. The current load data, energy storage system status, and grid demand data of the energy storage system are obtained, an adaptive communication protocol is configured, and control instructions are transmitted to the energy storage device in real time through a communication interface, which may include a CAN bus or Ethernet. By calculating the current load demand, the charge state of the energy storage system, and the grid load fluctuation, the power and time period required for charging and discharging are determined, and control strategy instructions are formulated, including control parameters such as charging power, discharging power, and duration. By sending the strategy instructions to the energy storage converter control unit, the execution unit controls the charging and discharging process of the energy storage device according to the instructions. Real-time status information of the energy storage converter control unit is obtained, including the current charge and discharge status, converter operating mode, and output power. The converter operating mode includes bidirectional conversion mode and constant current mode. The output of the energy storage converter is adjusted according to the set power range through the energy storage converter control unit to perform charging and discharging operations.

7. The system according to claim 1, wherein: The collaborative execution module is used to continuously monitor voltage and current data and adjust the converter operating mode according to the grid load demand, distributed energy generation capacity, and the charge and discharge status of the energy storage system, formulate a dynamic power adjustment strategy, and dynamically optimize the power allocation strategy by scheduling distributed energy and working in coordination with the grid, including: The system continuously monitors real-time voltage and current data during the charging and discharging process, and adjusts the converter operating mode in real time based on this monitoring information. It obtains information on the total grid demand, the real-time generation capacity of distributed energy resources, and the charge and discharge status of the energy storage system, determines the grid load demand information and the energy supply capacity of each distributed energy source, and dynamically adjusts the generation power of distributed energy resources by coordinating the distributed energy resources with the grid to supplement the energy storage system's energy shortage or regulate excess energy. It obtains real-time grid load information, distributed energy generation data, and the charge and discharge status of the energy storage system, and uses a dynamic programming algorithm to dynamically formulate a dynamic power adjustment strategy based on the power allocation optimization objective setting. The power allocation optimization objectives include grid load balancing, efficient utilization of the energy storage system, and maximum efficiency output of distributed energy resources, while avoiding overload or unstable fluctuations. The dynamic adjustment strategy includes increasing the discharge power of the energy storage system when the grid load fluctuation exceeds a preset fluctuation threshold, prioritizing the use of the energy storage system when the grid voltage is below a preset voltage threshold, and prioritizing the dispatch of distributed energy resources when their generation capacity exceeds a preset capacity threshold, while reducing the load on the energy storage system based on grid demand.

8. The system according to claim 1, wherein: The dynamic power adjustment strategy adjustment module is used to dynamically update the hybrid prediction model parameters based on the real-time grid load, energy storage system status and distributed energy data, and adjust the charge and discharge protection thresholds and power adjustment strategy through a feedback mechanism, including: The grid load, energy storage system status and distributed energy data are acquired in real time through the communication interface and transmitted to the central monitoring system; the real-time acquired grid load, energy storage system status and distributed energy data are aggregated into the human-computer interaction interface, and the human-computer interaction interface is used to display the operating status information of the energy storage system, load and distributed energy, the real-time forecast value of demand, the dynamically changing protection threshold and the potential demand risk warning information; the real-time data is used to update the parameters of the hybrid prediction model, and the demand within the preset time period in the future is predicted. According to the prediction results, the current state of charge value of the energy storage system and the grid voltage data, the charging demand protection threshold and the discharging demand protection threshold are dynamically adjusted; during the operation of the energy storage system, feedback data is acquired in real time and recorded All relevant control instructions and execution results are used to identify the error between demand forecast and actual demand, determine the responsiveness of the protection strategy, and transmit feedback data to the central monitoring system and the hybrid prediction model; the hybrid prediction model is optimized using an error correction algorithm, the optimized hybrid prediction model is re-run for demand forecast, and the actual operating effect of the optimized hybrid prediction model is evaluated; the parameters of the dynamic power adjustment strategy are adjusted according to the optimized demand forecast value; the actual operating status is compared with the forecast value through a real-time monitoring and feedback mechanism to determine the real-time effect of the hybrid prediction model and the dynamic power adjustment strategy, and the parameters of the hybrid prediction model and the dynamic power adjustment strategy are adjusted using the real-time monitoring data through an automatic feedback system.

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