Resource allocation and scheduling method and system for intelligent fixed storage and charging station

Abstract

The invention discloses an intelligent scheduling method and system for a battery energy storage system, and the method comprises the steps: generating a multi-dimensional operation state matrix through obtaining the charge state, charge-discharge rate and temperature data of a battery unit, and combining the power grid load and power market price information; according to the multi-dimensional operation state matrix, calculating an electric energy distribution proportion of each battery unit by adopting a preset state-of-charge threshold and charge-discharge rate constraint, and generating an initial electric energy distribution scheme; if a battery unit whose charge-discharge rate exceeds a safety range exists in the initial electric energy distribution scheme, predicting a performance attenuation trend based on historical operation data, and adjusting a distribution proportion to generate a corrected electric energy distribution scheme; according to the corrected electric energy distribution scheme, real-time temperature data are obtained, the local overheating risk is judged, and a temperature adjustment instruction set containing heat dissipation power adjustment parameters is generated. The operation efficiency and the economic benefit of the battery energy storage system can be effectively improved, and meanwhile the safety and the stability of the system are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy energy storage, and particularly discloses a method and system for resource allocation and scheduling of an intelligent fixed energy storage and charging station. Background Art

[0002] As a key component in the field of new energy energy storage, the intelligent fixed energy storage and charging station is of great significance for promoting energy transformation and improving power grid stability.

[0003] With the wide application of renewable energy, energy storage systems play an irreplaceable role in balancing supply and demand and optimizing energy utilization.

[0004] However, existing energy storage scheduling methods have obvious limitations in practical applications and are difficult to meet the high-efficiency operation requirements in complex scenarios.

[0005] Many traditional methods rely on static allocation strategies and lack the ability to dynamically respond to battery status, temperature changes, and real-time loads, resulting in low energy utilization efficiency and shortened equipment life.

[0006] In addition, some systems lack sufficient consideration in multi-objective optimization and are difficult to balance energy loss, energy storage efficiency, and economic benefits simultaneously. Especially in the electricity market-based trading, the scheduling flexibility is insufficient to cope with price fluctuations and grid demand changes.

[0007] In this context, the resource allocation and scheduling of intelligent fixed energy storage and charging stations face several core challenges.

[0008] Firstly, the dynamic management of battery status becomes a difficult problem. How to achieve differential electric energy allocation according to parameters such as charge and discharge rates and state of charge directly affects energy storage efficiency and system life.

[0009] Secondly, the accuracy of temperature control is crucial. If the local overheating problem is not properly solved, it may cause safety hazards and reduce battery performance.

[0010] Finally, the real-time requirement of multi-objective optimization requires the system to quickly adjust strategies in a complex environment, but existing algorithms still have bottlenecks in prediction accuracy and response speed.

[0011] The unresolved of these technical factors makes it difficult for energy storage systems to achieve efficient collaborative operation in a dynamic market and diverse scenarios.

[0012] Therefore, how to design an intelligent scheduling method that can comprehensively consider dynamic allocation of battery status, temperature management, and multi-objective real-time optimization to achieve efficient, safe, and economic operation of energy storage systems has become a key issue in the field of resource allocation and scheduling of intelligent fixed energy storage and charging stations. Summary of the Invention

[0013] The present invention provides a resource allocation and scheduling method and system for an intelligent fixed energy storage and charging station, aiming to solve at least one defect existing in the above-mentioned existing resource allocation and scheduling methods for intelligent fixed energy storage and charging stations.

[0014] One aspect of the present invention relates to a resource allocation and scheduling method for an intelligent fixed energy storage and charging station, including the following steps: Obtain the state of charge, charge and discharge rate, and temperature data of the battery units, and combine the grid load and electricity market price information to generate a multi-dimensional operation state matrix; According to the multi-dimensional operation state matrix, adopt the preset state of charge threshold and charge and discharge rate constraints to calculate the power distribution ratio of each battery unit, and generate an initial power distribution plan; If there are battery units with charge and discharge rates exceeding the safe range in the initial power distribution plan, then predict the performance degradation trend based on historical operation data, and adjust the distribution ratio to generate a corrected power distribution plan; According to the corrected power distribution plan, obtain real-time temperature data and judge the risk of local overheating, and generate a temperature adjustment instruction set including heat dissipation power adjustment parameters; Dynamically adjust the cooling system using the temperature adjustment instruction set, generate optimized temperature distribution data, and determine the stable operation state of the battery; Input the optimized temperature distribution data and the corrected power distribution plan into a multi-objective optimization algorithm, and combine the real-time grid demand to generate a comprehensive scheduling plan including energy storage efficiency and economic benefit indicators.

[0015] Further, the step of obtaining the state of charge, charge and discharge rate, and temperature data of the battery units, and combining the grid load and electricity market price information to generate a multi-dimensional operation state matrix includes: Obtain the state of charge, charge and discharge rate, and temperature data from the battery management system, obtain the load data from the grid dispatching system through the interface, and obtain the market price data from the power trading platform to obtain the original data set; Process the original data set using a data cleaning method. If data is missing, complete it by interpolation. If there are outliers, remove them through a preset threshold to obtain the cleaned data set; According to the cleaned data set, normalize the state of charge, charge and discharge rate, temperature data, grid load data, and electricity market price using a normalization method to obtain a normalized data set; Through the normalized data set, use the principal component analysis method to extract the correlation features between the state of charge, charge and discharge rate, temperature data and grid load data, and electricity market price to obtain a feature data set; If the associated features in the feature dataset meet the preset correlation threshold, the state of charge, charge and discharge rate, temperature data, grid load data, and electricity market price are combined through a weighted fusion algorithm to obtain an initial multi-dimensional operation state matrix; According to the initial multi-dimensional operation state matrix, a dynamic update mechanism is adopted. If real-time data changes are detected, the matrix parameters are adjusted through an incremental update algorithm to obtain an updated multi-dimensional operation state matrix; Through the updated multi-dimensional operation state matrix, a matrix decomposition method is used to analyze the dynamic association relationship between the state of charge, charge and discharge rate, temperature data and grid load data, and electricity market price to obtain the operation state analysis result.

[0016] Furthermore, according to the multi-dimensional operation state matrix, using the preset state of charge threshold and charge and discharge rate constraint, the steps of calculating the power distribution ratio of each battery unit and generating an initial power distribution plan include: Obtain the state of charge data and charge and discharge rate data of the battery unit from the multi-dimensional operation state matrix. Through a logical judgment method, using the preset state of charge threshold and charge and discharge rate constraint, if the state of charge is higher than the threshold and the charge and discharge rate meets the constraint, it is marked as an available battery unit to obtain a set of available battery units; According to the set of available battery units, obtain the state of charge value and charge and discharge rate value of each battery unit. Through a weighted calculation method, using the preset weight ratio of the state of charge and charge and discharge rate, calculate the power distribution coefficient of each battery unit to obtain a set of power distribution coefficients; Obtain the grid load data and price fluctuation data from the multi-dimensional operation state matrix. Using a normalization processing method, standardize and fuse the set of power distribution coefficients with the grid load data and price fluctuation data. If the grid load is higher than the preset threshold, it is preferentially allocated to the high-coefficient battery units to obtain a preliminary distribution ratio set; According to the preliminary distribution ratio set, use an iterative calculation method to optimize and adjust the power distribution ratio of each battery unit. Analyze the dynamic association between the preliminary distribution ratio set and the matrix data through a linear regression algorithm to obtain an initial power distribution plan.

[0017] Furthermore, if there are battery units in the initial power distribution plan whose charge and discharge rates exceed the safe range, the steps of predicting the performance decay trend based on historical operation data and adjusting the distribution ratio to generate a corrected power distribution plan include: Obtain the operation state of each battery unit through a data collection tool, collect the charge and discharge rate and operation time of the battery unit to obtain a historical operation dataset; If the charge and discharge rate in the historical operation dataset exceeds the preset safe range, use a linear regression algorithm to analyze the historical operation dataset, predict the performance decay trend of each battery unit, and determine the performance decay curve; According to the performance degradation curve, adjust the distribution ratio in the initial power distribution, and recalculate the power distribution of each battery cell through an optimization adjustment tool to obtain a corrected power distribution scheme; For the corrected power distribution scheme, verify whether the charge-discharge rate of each battery cell is within the preset safe range through a data acquisition tool, and judge the feasibility of the corrected power distribution scheme.

[0018] Further, according to the corrected power distribution scheme, the steps of obtaining real-time temperature data and judging the local overheating risk, and generating a temperature adjustment instruction set including heat dissipation power adjustment parameters are as follows: Obtain real-time temperature data from an environmental sensor, and dynamically adjust the acquisition frequency by using a data acquisition module. If the temperature change rate exceeds the preset threshold, increase the acquisition frequency to obtain a high-precision temperature data set; According to the high-precision temperature data set, analyze the local area temperature distribution by using a logical judgment method. If the temperature of a certain area is continuously higher than the average value and exceeds the preset overheating threshold, determine the local overheating risk area; For the local overheating risk area, obtain the current operating parameters of the cooling device, calculate the heat dissipation power adjustment amount through a support vector machine algorithm, and generate a control signal set including power adjustment parameters; Through an instruction generation module, convert the control signal set into a temperature adjustment instruction set, send the instruction set to the cooling device by using a communication protocol, and judge the update of the device state after the instruction is executed.

[0019] Further, the steps of dynamically adjusting the cooling system by using the temperature adjustment instruction set, generating optimized temperature distribution data, and determining the stable operating state of the battery are as follows: Obtain the preset dynamic adjustment parameters from the temperature adjustment instruction set, decompose the instruction content through an instruction set parsing tool, generate an initial parameter set including the adjustment amplitude of the cooling system, and determine the initial temperature adjustment scheme; Adjust the cooling system by using the initial parameter set, and generate real-time temperature distribution data through a temperature field simulation tool to obtain a temperature distribution data set reflecting the battery operating environment; If the maximum value in the temperature distribution data set exceeds the preset stable state threshold, optimize the dynamic adjustment parameters through a data iteration update tool, generate a new cooling system adjustment parameter set, and determine the updated temperature adjustment scheme; Adjust the cooling system according to the updated temperature adjustment scheme, and analyze the temperature distribution data through an operating state judgment tool to obtain the stable operating state of the battery.

[0020] Further, the steps of inputting the optimized temperature distribution data and the corrected power distribution scheme into a multi-objective optimization algorithm and generating a comprehensive scheduling scheme including energy storage efficiency and economic benefit indicators in combination with real-time grid demands are as follows: Obtain temperature distribution data and power distribution data from the acquisition device, determine whether the operating state is abnormal through a preset threshold. If the temperature distribution data exceeds the threshold, correct the power distribution data to obtain the corrected distribution data; According to the corrected distribution data, combined with the real-time acquired grid demand data, use a linear programming tool to calculate the charge and discharge ratio of the energy storage device to obtain the energy storage operation parameters; Process the energy storage operation parameters and grid demand data through a multi-objective optimization algorithm, calculate the economic benefit indicators when the energy storage efficiency constraint is met, and obtain the optimized scheduling parameters; Generate a comprehensive scheduling scheme including energy storage operation instructions and power distribution ratio according to the optimized scheduling parameters, determine whether the comprehensive scheduling scheme meets the requirements of the comprehensive indicators, and output the final scheduling scheme.

[0021] Another aspect of the present invention relates to a resource allocation and scheduling system for an intelligent fixed energy storage and charging station, which is used to implement the resource allocation and scheduling method of the above intelligent fixed energy storage and charging station, including: A first generation module, used to obtain the state of charge, charge and discharge rate, and temperature data of the battery unit, and generate a multi-dimensional operation state matrix in combination with grid load and electricity market price information; A calculation module, used to calculate the power distribution ratio of each battery unit according to the multi-dimensional operation state matrix, using a preset state of charge threshold and charge and discharge rate constraint, and generate an initial power distribution scheme; An adjustment module, used to if there are battery units with charge and discharge rates exceeding the safe range in the initial power distribution scheme, predict the performance degradation trend based on historical operation data, and adjust the distribution ratio to generate a corrected power distribution scheme; A second generation module, used to obtain real-time temperature data according to the corrected power distribution scheme and judge the local overheating risk, and generate a temperature regulation instruction set including heat dissipation power adjustment parameters; A third generation module, used to dynamically adjust the cooling system using the temperature regulation instruction set, generate optimized temperature distribution data and determine the stable operation state of the battery; A fourth generation module, used to input the optimized temperature distribution data and the corrected power distribution scheme into a multi-objective optimization algorithm, and generate a comprehensive scheduling scheme including energy storage efficiency and economic benefit indicators in combination with real-time grid demands; A fifth generation module, used to if the fluctuation range of the electricity market price in the comprehensive scheduling scheme exceeds the preset range, recalculate the scheduling priority based on the prediction model, and generate the final scheduling scheme.

[0022] Furthermore, the first generation module includes: The first acquisition unit is used to acquire the state of charge, charge and discharge rate, and temperature data from the battery management system, acquire the load data from the power grid dispatching system through the interface, and acquire the market price data from the power trading platform to obtain the original data set; The second acquisition unit is used to process the original data set by using a data cleaning method. If data is missing, it is complemented by interpolation. If there are outliers, they are removed through a preset threshold to obtain the cleaned data set; The third acquisition unit is used to perform normalization processing on the state of charge, charge and discharge rate, temperature data, grid load data, and electricity market price according to the cleaned data set by using a normalization method to obtain the normalized data set; The fourth acquisition unit is used to extract the correlation features between the state of charge, charge and discharge rate, temperature data and the grid load data, and electricity market price through the normalized data set by using the principal component analysis method to obtain the feature data set; The fifth acquisition unit is used to combine the state of charge, charge and discharge rate, temperature data, grid load data, and electricity market price through a weighted fusion algorithm if the correlation features in the feature data set meet the preset correlation threshold to obtain the initial multi-dimensional operation state matrix; The sixth acquisition unit is used to adopt a dynamic update mechanism according to the initial multi-dimensional operation state matrix. If real-time data changes are detected, the matrix parameters are adjusted through an incremental update algorithm to obtain the updated multi-dimensional operation state matrix; The seventh acquisition unit is used to analyze the dynamic correlation relationship between the state of charge, charge and discharge rate, temperature data and the grid load data, and electricity market price through the updated multi-dimensional operation state matrix by using the matrix decomposition method to obtain the operation state analysis result.

[0023] Furthermore, the calculation module includes: The eighth acquisition unit is used to acquire the state of charge data and charge and discharge rate data of the battery unit from the multi-dimensional operation state matrix. Through a logical judgment method, using the preset state of charge threshold and charge and discharge rate constraint, if the state of charge is higher than the threshold and the charge and discharge rate meets the constraint, it is marked as an available battery unit to obtain the set of available battery units; The ninth acquisition unit is used to acquire the state of charge value and charge and discharge rate value of each battery unit according to the set of available battery units. Through a weighted calculation method, using the preset weight ratio of the state of charge and charge and discharge rate, calculate the power distribution coefficient of each battery unit to obtain the set of power distribution coefficients; The tenth acquisition unit is used to obtain grid load data and price fluctuation data from the multi-dimensional operation status matrix. Using the normalization processing method, it standardizes and fuses the power distribution coefficient set with the grid load data and price fluctuation data. If the grid load is higher than the preset threshold, it is preferentially allocated to the high-coefficient battery units to obtain the preliminary allocation ratio set. The eleventh acquisition unit is used to optimize and adjust the power distribution ratio of each battery unit according to the preliminary allocation ratio set by using the iterative calculation method. It analyzes the dynamic correlation between the preliminary allocation ratio set and the matrix data through the linear regression algorithm to obtain the initial power distribution scheme.

[0024] The beneficial effects achieved by the present invention are as follows: The present invention discloses an intelligent scheduling method and system for a battery energy storage system. By obtaining the operation status data of battery units and grid information, a multi-dimensional operation status matrix is generated, and a power distribution scheme is calculated. According to the charge and discharge rate and temperature data, the distribution scheme and the cooling system are dynamically adjusted to ensure the safe and stable operation of the battery. Combining with the real-time grid demand, a multi-objective optimization algorithm is used to generate a comprehensive scheduling scheme, and it is adjusted according to the power market price fluctuation to achieve the balance of energy storage efficiency and economic benefits. The present invention can effectively improve the operation efficiency and economic benefits of the battery energy storage system, while ensuring the safety and stability of the system, providing a new solution for the intelligent scheduling of large-scale energy storage systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic flow chart of an embodiment of an intelligent scheduling method for a battery energy storage system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0026] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the specification drawings and specific embodiments.

[0027] As Figure 1 shown, the first embodiment of the present invention proposes a resource allocation and scheduling method for an intelligent fixed charging and storage station, including the following steps: Step S100: Obtain the state of charge, charge and discharge rate, and temperature data of the battery unit, and generate a multi-dimensional operation status matrix in combination with the grid load and power market price information.

[0028] The multi-dimensional operation status matrix is a high-dimensional data structure used to describe the complex operation status of the battery system. By integrating multi-dimensional parameters, time series, and spatial information, it realizes the comprehensive monitoring and quantitative analysis of the operation status of battery monomers or battery packs.

[0029] Step S200: According to the multi-dimensional operating state matrix, using the preset state of charge threshold and charge-discharge rate constraint, calculate the power distribution ratio of each battery cell, and generate an initial power distribution plan.

[0030] The charge-discharge rate constraint refers to the limiting conditions for the charge-discharge rate of lithium batteries. Its core purpose is to ensure that the battery operates within a safe range while optimizing performance and lifespan.

[0031] The power distribution ratio of battery cells refers to the ratio of the actual power borne or released by each single battery during the charge-discharge process in a battery pack (composed of multiple single batteries connected in series or parallel) to the total power of the battery pack. This ratio is jointly determined by battery consistency, capacity difference, internal resistance characteristics, and management system strategies, and directly affects the efficiency and lifespan of the battery pack.

[0032] Step S300: If there are battery cells in the initial power distribution plan whose charge-discharge rates exceed the safe range, then based on historical operation data, predict the performance degradation trend and adjust the distribution ratio to generate a corrected power distribution plan.

[0033] The charge-discharge rate represents the current intensity required for the battery to release or absorb its rated capacity within a specified time, and numerically equals the ratio of the charge-discharge current to the battery's rated capacity.

[0034] The performance degradation trend refers to the dynamic change law in which the core performance indicators (such as capacity, energy efficiency, internal resistance, etc.) of the battery gradually deteriorate over time or with the increase in the number of cycles during use. This trend shows a non-linear decline and is jointly affected by material aging, environmental conditions, and usage patterns. It is the core basis for evaluating the lifespan and reliability of the battery. Step S400: According to the corrected power distribution plan, obtain real-time temperature data and judge the risk of local overheating, and generate a temperature regulation instruction set containing heat dissipation power adjustment parameters.

[0035] The risk of local overheating refers to the potential danger that the temperature of a certain area in the system or device rises abnormally (significantly higher than other parts), resulting in the deterioration of material performance, functional failure, or safety accidents. Its essence is the dynamic imbalance state of heat concentration and heat dissipation imbalance, which is common in batteries, electronic devices, and mechanical systems.

[0036] The temperature regulation instruction set is a set of predefined standardized operation commands in the battery management system (BMS) or intelligent thermal management system, used to dynamically adjust the operating state of the battery or device according to real-time temperature data to ensure that it operates within a safe temperature range. Its core functions include temperature acquisition, threshold determination, heat dissipation / heating execution, and multi-mode switching, and it realizes thermal safety closed-loop control through algorithm drive and hardware linkage.

[0037] Step S500: Dynamically adjust the cooling system using a temperature regulation instruction set, generate optimized temperature distribution data, and determine the stable operating state of the battery.

[0038] The stable operating state of the battery refers to the state in which, during the charging and discharging process of the battery, its core parameters (such as voltage, current, temperature, internal resistance, and state of charge SOC, etc.) remain relatively constant or fluctuate periodically, and the system is in a dynamically balanced working state. This state needs to meet the requirements of thermodynamic and electrochemical stability to ensure that the battery operates efficiently within the safety threshold and delays performance degradation.

[0039] Step S600: Input the optimized temperature distribution data and the corrected power distribution plan into a multi-objective optimization algorithm, and generate a comprehensive scheduling plan including energy storage efficiency and economic benefit indicators in combination with the real-time grid demand.

[0040] Multi-Objective Optimization Algorithms (MOOA) are a class of computational methods used to solve problems with multiple conflicting objective functions, aiming to find a set of trade-off solutions (i.e., Pareto optimal solution set) from the feasible solution set to achieve an optimal balance among all objective functions as a whole.

[0041] Furthermore, for the resource allocation and scheduling method of the intelligent fixed energy storage and charging station provided in this embodiment, step S100 includes: Step S110: Obtain the state of charge, charge and discharge rate, and temperature data from the battery management system, obtain the load data through the interface from the power grid dispatching system, and obtain the market price data from the power trading platform to obtain the original data set.

[0042] Obtain the state of charge, charge and discharge rate, and temperature data from the battery management system. Exemplarily, the battery management system monitors the operation of the lithium battery pack in real time. The state of charge reflects the percentage of the remaining capacity of the battery, such as 80%. The charge and discharge rate represents the current intensity, such as 1C represents the current of 1 times the rated capacity. The temperature data, such as 35 degrees Celsius, reflects the operating environment. These data are collected by sensors and transmitted to the central processor. It should be noted that data collection needs to ensure high frequency to capture dynamic changes and improve the accuracy of subsequent analysis.

[0043] Obtain the load data through the interface from the power grid dispatching system. For example, the hourly load curve of a certain area, with a peak load of 500 megawatts, reflects the fluctuation of electricity demand.

[0044] Obtain the market price data from the power trading platform, such as the day-ahead market electricity price of 300 yuan per megawatt-hour. These data constitute the original data set. Specifically, the original data set may be missing or abnormal due to equipment failures or network delays, affecting the accuracy of analysis.

[0045] Step S120: Process the original dataset using a data cleaning method. If data is missing, complete it through interpolation. If there are outliers, remove them using a preset threshold to obtain a cleaned dataset.

[0046] Process the original dataset using a data cleaning method. In a possible implementation, if the state of charge data is missing at a certain moment, the intermediate value can be estimated through linear interpolation based on the data at the previous and subsequent moments, such as 78% and 82%, to approximately 80%.

[0047] If the temperature data has an outlier, such as a sudden increase to 100 degrees Celsius, exceeding the preset threshold of 50 degrees Celsius, then remove it and replace it with a neighboring value.

[0048] The cleaned dataset has higher integrity, providing a reliable basis for subsequent analysis.

[0049] Step S130: According to the cleaned dataset, use a standardization method to normalize the state of charge, charge and discharge rate, temperature data, grid load data, and electricity market price to obtain a normalized dataset.

[0050] According to the cleaned dataset, use a standardization method for normalization processing.

[0051] Preferably, the state of charge, charge and discharge rate, temperature, grid load, and electricity price data are respectively mapped to the interval from 0 to 1. For example, the temperature data ranges from 20 to 40 degrees Celsius, and 35 degrees Celsius is normalized to 0.75.

[0052] Normalization eliminates the dimension difference, facilitating multi-dimensional feature comparison and improving the analysis consistency.

[0053] Step S140: Through the normalized dataset, use the principal component analysis method to extract the correlation features between the state of charge, charge and discharge rate, temperature data, grid load data, and electricity market price to obtain a feature dataset.

[0054] Through the normalized dataset, use the principal component analysis to extract the correlation features. For example, analysis finds that the state of charge is positively correlated with the grid load. When the load increases to 550 MW, the state of charge may drop to 75%.

[0055] The feature dataset reflects the internal relationship between variables and reduces the redundant dimensions.

[0056] It can be understood that extracting features helps to focus on the key influencing factors and optimize the calculation efficiency.

[0057] Step S150: If the correlation features in the feature dataset meet the preset correlation threshold, then use a weighted fusion algorithm to combine the state of charge, charge and discharge rate, temperature data, grid load data, and electricity market price to obtain an initial multi-dimensional operation state matrix.

[0058] If the correlation features in the feature dataset meet the correlation threshold, such as the correlation coefficient exceeding 0.7, the data is combined through a weighted fusion algorithm. In one embodiment, the state of charge weight is set to 0.4 and the grid load weight is 0.3, and an initial multi-dimensional operation state matrix is generated through fusion, which characterizes the interaction state between the battery and the grid. The matrix synthesizes multi-source information and reflects the overall picture of the system operation.

[0059] Step S160: According to the initial multi-dimensional operation state matrix, adopt a dynamic update mechanism. If real-time data changes are detected, the matrix parameters are adjusted through an incremental update algorithm to obtain an updated multi-dimensional operation state matrix.

[0060] According to the initial matrix, adopt a dynamic update mechanism. When the electricity price is detected to rise to 350 yuan in real time, the incremental update algorithm adjusts the matrix parameters to generate an updated multi-dimensional operation state matrix to ensure that the matrix reflects the latest operation trend. The dynamic update improves the timeliness of the matrix and supports real-time decision-making.

[0061] Step S170: Through the updated multi-dimensional operation state matrix, adopt a matrix decomposition method to analyze the dynamic correlation relationship between the state of charge, charge and discharge rate, temperature data and grid load data, and electricity market price to obtain an operation state analysis result.

[0062] Through the updated matrix, adopt a matrix decomposition method to analyze the dynamic correlation relationship. For example, the decomposition result shows that the charge and discharge rate changes with the load fluctuation. When the load drops suddenly to 450 MW, the rate may drop to 0.8C.

[0063] The operation state analysis result reveals the dynamic influence between variables and provides a basis for optimizing battery scheduling and reducing operation costs.

[0064] The decomposition method refines the causal relationship and enhances the analysis depth. For example, the analysis result can guide the battery to charge during low electricity price periods and discharge during high load periods, saving about 10% in costs. The multi-dimensional analysis ensures that the scheduling strategy takes into account both efficiency and economy and supports the stable operation of the power grid.

[0065] Furthermore, for the resource allocation and scheduling method of the intelligent fixed storage and charging station provided in this embodiment, step S200 includes: Step S210: Obtain the state of charge data and charge and discharge rate data of the battery unit from the multi-dimensional operation state matrix. Through a logical judgment method, using a preset state of charge threshold and charge and discharge rate constraint, if the state of charge is higher than the threshold and the charge and discharge rate meets the constraint, it is marked as an available battery unit to obtain a set of available battery units.

[0066] Exemplarily, when extracting the state of charge and charge and discharge rate data of the battery unit from the multi-dimensional operation state matrix, assume that the matrix records the operation information of a certain lithium battery pack.

[0067] The state of charge represents the percentage of the remaining capacity of the battery, and the charge and discharge rate reflects the multiple of the current relative to the rated capacity. The logical judgment method filters available battery cells based on preset conditions. For example, the state of charge threshold is set to 70%, and the charge and discharge rate constraint is from 0.5C to 2C.

[0068] The state of charge of a certain battery cell is 85% and the charge and discharge rate is 1.2C, which meets the conditions and is marked as an available cell. If the state of charge of another cell is 65%, it will be excluded. Through screening, a set containing multiple available cells is obtained, which reflects the cells in the battery pack that can participate in scheduling.

[0069] Step S220: According to the set of available battery cells, obtain the state of charge value and the charge and discharge rate value of each battery cell. Through a weighted calculation method, using the preset weight ratio of the state of charge and the charge and discharge rate, calculate the power distribution coefficient of each battery cell to obtain a set of power distribution coefficients.

[0070] In a possible implementation, the power distribution coefficient is calculated based on the set of available battery cells.

[0071] The state of charge and the charge and discharge rate value of each cell are extracted. For example, the state of charge of cell A is 90% and the rate is 1.5C; the state of charge of cell B is 80% and the rate is 1C. The preset state of charge weight is 0.6 and the rate weight is 0.4. When performing weighted calculation, the coefficient of cell A is combined by two weights, and the higher value indicates its higher priority.

[0072] It should be noted that the weight ratio can be adjusted according to the battery type. For example, lithium iron phosphate batteries may pay more attention to the state of charge.

[0073] It can be understood that the set of power distribution coefficients provides a quantitative basis for subsequent distribution, ensuring more accurate power scheduling.

[0074] Step S230: Obtain the grid load data and price fluctuation data from the multi-dimensional operating state matrix. Using the normalization processing method, standardize and fuse the set of power distribution coefficients with the grid load data and price fluctuation data. If the grid load is higher than the preset threshold, it is preferentially allocated to the high-coefficient battery cells to obtain a preliminary allocation ratio set.

[0075] Specifically, when obtaining the grid load and price fluctuation data from the matrix, assume that the load at a certain time period is 600 megawatts and the price fluctuation shows that the electricity price is 320 yuan per megawatt-hour.

[0076] Normalization maps the power distribution coefficient, load, and price data to the range of 0 to 1. For example, for a load range of 500 to 700 MW, 600 MW is normalized to 0.5. During fusion, if the load exceeds the threshold of 550 MW, it is preferentially allocated to the unit with a higher coefficient. For example, the allocation ratio of unit A is 60% and that of unit B is 40%, forming a preliminary allocation ratio set.

[0077] Preferably, normalization ensures that data with different dimensions can be compared and the allocation is more reasonable.

[0078] Step S240: According to the preliminary allocation ratio set, adopt an iterative calculation method to optimize and adjust the power distribution ratio of each battery unit. Analyze the dynamic correlation between the preliminary allocation ratio set and the matrix data through a linear regression algorithm to obtain an initial power distribution plan.

[0079] In one embodiment, the iterative calculation optimizes the allocation ratio. The preliminary ratio set may not be accurate enough due to sudden load changes. For example, when the load drops to 520 MW, it needs to be readjusted. Linear regression analyzes the correlation between the ratio set and the matrix data and finds that the unit with a higher coefficient can reduce the allocation ratio at low loads. After adjustment, the ratio of unit A drops to 55% and that of unit B rises to 45%, generating an initial power distribution plan. The plan takes into account dynamic changes and has stronger adaptability. For example, the analysis shows that preferentially allocating the unit with a higher coefficient can balance the use of batteries and extend the overall life.

[0080] It can be understood that the above method ensures the rationality of power distribution through layer-by-layer screening and optimization.

[0081] From screening available units to optimizing the allocation plan, each link is based on matrix data and is logically rigorous. Exemplarily, a certain battery pack can quickly respond to the grid demand by preferentially allocating high-coefficient units at high loads; at low electricity price periods, the allocation is adjusted to take into account economy. Such a plan can flexibly respond to grid fluctuations and improve the battery scheduling efficiency during actual operation.

[0082] Furthermore, for the resource allocation and scheduling method of the intelligent fixed charge and storage station provided in this embodiment, step S300 includes: Step S310: Obtain the operating status of each battery unit through a data acquisition tool, collect the charge and discharge rates and operating times of the battery units to obtain a historical operating data set.

[0083] Exemplarily, when obtaining the operating status of each battery unit through a data acquisition tool, it is necessary to ensure the comprehensiveness and real-time nature of the data.

[0084] The operating state includes parameters such as the voltage, current, and temperature of the battery cells, and the charge-discharge rate and operating time are the core indicators. Suppose a battery pack contains 10 cells, namely Cell 1, Cell 2, Cell 3... Cell 10. The data acquisition tool records data once every minute, and the obtained data shows that the charge-discharge rate of Cell 1 is 0.5C and the operating time is 200 hours, while the rate of Cell 2 reaches 2C and the operating time is 150 hours.

[0085] This acquisition method relies on high-precision sensors and a data recording system to ensure the reliability of the historical operating data set.

[0086] Step S320: If the charge-discharge rate in the historical operating data set exceeds the preset safety range, use the linear regression algorithm to analyze the historical operating data set, predict the performance degradation trend of each battery cell, and determine the performance degradation curve.

[0087] In a possible implementation, if the charge-discharge rate in the historical operating data set exceeds the preset safety range, for example, the safety range is set from 0.2C to 1.5C and the 2C of Cell 2 has exceeded the standard, further analysis is required.

[0088] The linear regression algorithm can be used to analyze historical data and predict the performance degradation trend.

[0089] Suppose by analyzing the historical data of Cell 2, it is found that its capacity has decreased by 5% after 150 hours of operation, while the normal degradation should be 2%.

[0090] By fitting the data, it is predicted that its capacity may further decrease by 4% in the next 100 hours, forming a performance degradation curve. This prediction is based on historical trends and can reflect the health status of the battery cell.

[0091] Step S330: According to the performance degradation curve, adjust the allocation ratio in the initial power distribution, and recalculate the power allocation of each battery cell through an optimization adjustment tool to obtain a corrected power distribution plan.

[0092] Specifically, when adjusting the initial power distribution ratio according to the performance degradation curve, the allocation ratio of high-degradation cells can be preferentially reduced.

[0093] Suppose the allocation ratio of Cell 2 in the initial plan is 20%, and the degradation curve shows that its performance has decreased significantly. Then, through the optimization adjustment tool, its ratio is reduced to 10%, and the excess power is allocated to Cell 1 with less degradation.

[0094] This adjustment is based on the quantitative results of the degradation curve to ensure more reasonable power distribution.

[0095] Step S340: For the corrected power distribution scheme, verify whether the charge and discharge rates of each battery unit are within the preset safe range through a data acquisition tool, and determine the feasibility of the corrected power distribution scheme.

[0096] Preferably, after generating the corrected power distribution scheme, its feasibility needs to be verified.

[0097] The data acquisition tool can collect the charge and discharge rate data of each unit again to determine whether it meets the safe range. For example, after adjustment, the rate of unit 2 drops to 1.2C, and the rate of unit 1 remains at 0.6C, both within the range of 0.2C to 1.5C, indicating that the scheme is feasible. If a certain unit still exceeds the standard, the ratio needs to be readjusted until the requirements are met. This verification method ensures the actual applicability of the scheme through real-time data feedback.

[0098] It should be noted that the above process needs to consider the dynamic demand of the power grid. For example, during the peak load of the power grid, it may be required that the battery pack provide greater power, and the correction scheme needs to take into account both safety and power supply capacity.

[0099] Assume that during verification, it is found that the rate of unit 3 is close to the upper limit, then its distribution ratio can be further fine-tuned to ensure the stability of the overall performance.

[0100] In one embodiment, the generation of the performance decay curve and the adjustment of the distribution ratio can be combined with the real-time monitoring function of the battery management system. For example, the system can automatically mark the high-decay units and recommend the optimized ratio, reducing manual intervention. This method improves the efficiency and accuracy of scheme generation.

[0101] It can be understood that the above examples are all centered around the optimization of the operating state of the battery unit, focusing on the logical chain of data acquisition, decay prediction, and distribution adjustment. Through multi-dimensional analysis and verification, the generated correction scheme is more in line with the actual needs and can dynamically adapt to the changes in the operating environment.

[0102] Furthermore, for the resource allocation and scheduling method of the intelligent fixed storage and charging station provided in this embodiment, step S400 includes: Step S410: Obtain real-time temperature data from the environmental sensor, and use the data acquisition module to dynamically adjust the acquisition frequency. If the temperature change rate exceeds the preset threshold, increase the acquisition frequency to obtain a high-precision temperature data set.

[0103] Exemplarily, during the process of obtaining real-time temperature data from the environmental sensor, the environmental sensor is usually deployed in the battery management system to monitor the temperature change around the battery unit.

[0104] The sensor can be a thermocouple or an infrared thermometer, placed at key positions of the battery module, such as the battery surface or the inlet of the heat dissipation channel.

[0105] The acquisition of real-time temperature data depends on the sampling frequency of the sensor, usually in seconds. For example, under normal conditions, temperature data is collected every 5 seconds, and the accuracy can reach ±0.5 degrees Celsius.

[0106] This method ensures the real-time and reliability of the data, providing a basis for subsequent analysis.

[0107] In a possible implementation, the core of the data acquisition module's dynamic adjustment of the acquisition frequency lies in the monitoring of the temperature change rate.

[0108] The temperature change rate refers to the increase or decrease amplitude of the temperature per unit time, and the preset threshold can be set to 2 degrees Celsius per minute.

[0109] If the temperature rises from 25 degrees Celsius to 28 degrees Celsius at a certain moment and only takes 1 minute, the change rate reaches 3 degrees Celsius per minute, exceeding the threshold.

[0110] The data acquisition module will increase the acquisition frequency from once every 5 seconds to once every 2 seconds, thereby generating a high-precision temperature data set.

[0111] This dynamic adjustment mechanism can capture the rapidly changing temperature trend and avoid data distortion caused by insufficient sampling.

[0112] Step S420: According to the high-precision temperature data set, use a logical judgment method to analyze the temperature distribution in the local area. If the temperature in a certain area is continuously higher than the average value and exceeds the preset overheat threshold, then determine the local overheat risk area.

[0113] Specifically, when the high-precision temperature data set is used to analyze the temperature distribution in the local area, the logical judgment method will divide the battery module into multiple areas, such as the top, middle, and bottom.

[0114] Suppose the data set shows that the temperature of a certain area in 10 consecutive acquisitions is higher than the overall average value by 30 degrees Celsius and reaches 35 degrees Celsius, exceeding the overheat threshold of 33 degrees Celsius, then determine that this area is a local overheat risk area.

[0115] This method accurately locates the overheated area through multi-point data comparison, reducing the possibility of misjudgment.

[0116] Step S430: For the local overheat risk area, obtain the current operating parameters of the cooling device, calculate the cooling power adjustment amount through the support vector machine algorithm, and generate a control signal set containing power adjustment parameters.

[0117] Preferably, when obtaining the operating parameters of the heat dissipation device for the local overheating risk area, the parameters include the fan speed, coolant flow rate, etc. For example, the current fan speed is 2000 revolutions per minute, and the coolant flow rate is 5 liters per minute. The support vector machine algorithm will calculate based on historical data and the current temperature that the fan speed needs to be increased to 2500 revolutions per minute and the flow rate increased to 6 liters per minute to generate a set of control signals.

[0118] The support vector machine algorithm finds the optimal heat dissipation power adjustment amount through classification and regression analysis to ensure the maximization of heat dissipation efficiency.

[0119] Step S440: Through the instruction generation module, convert the set of control signals into a set of temperature adjustment instructions, send the set of instructions to the heat dissipation device using a communication protocol, and judge the update of the device state after the execution of the instructions.

[0120] In one embodiment, the process of the instruction generation module converting the set of control signals into a set of temperature adjustment instructions involves signal encoding and protocol adaptation. For example, the speed adjustment parameter in the set of control signals will be encoded into an instruction in the Modbus protocol format, and the content includes the device address, function code, and adjustment value.

[0121] The instruction generation module will generate an instruction, such as "adjust the speed of fan No. 1 to 2500 revolutions per minute". These instructions are sent to the heat dissipation device through the RS485 or CAN communication protocol to ensure the stability and compatibility of the instruction transmission.

[0122] It can be understood that judging the update of the device state after the execution of the instructions is mainly to verify the heat dissipation effect through a feedback mechanism.

[0123] After receiving the instructions, the heat dissipation device performs the speed or flow rate adjustment and transmits the actual operating parameters back. For example, the actual fan speed reaches 2500 revolutions per minute, and the temperature sensor shows that the temperature in the overheating area drops from 35 degrees Celsius to 31 degrees Celsius, which is lower than the overheating threshold. The state update confirms the effectiveness of the instructions and provides a basis for the closed-loop control of the system.

[0124] This feedback mechanism improves the robustness of the system. For example, the combination of dynamically adjusting the acquisition frequency and high-precision data analysis makes the temperature monitoring more accurate and can detect potential overheating risks in advance.

[0125] The automated process of heat dissipation power adjustment and instruction execution ensures the fast response ability and reduces the cost of manual intervention.

[0126] These mechanisms together constitute an efficient temperature management solution, providing technical guarantee for the stable operation of the battery system.

[0127] Further, the resource allocation and scheduling method of the intelligent fixed charging and storage station provided in this embodiment, step S500 includes: Step S510, obtain the preset dynamic adjustment parameters from the temperature adjustment instruction set, decompose the instruction content through the instruction set parsing tool, generate an initial parameter set including the adjustment amplitude of the cooling system, and determine the initial temperature adjustment plan.

[0128] Exemplarily, when obtaining the preset dynamic adjustment parameters from the temperature adjustment instruction set, the instruction content can be decomposed through the instruction set parsing tool.

[0129] The instruction set usually includes information such as the operating mode, adjustment frequency, and amplitude of the cooling system.

[0130] The parsing tool will extract this information as structured data. For example, the instruction is decomposed into parameters such as the rotation speed of the cooling fan and the flow rate of the liquid cooling pump. Suppose a battery system instruction set stipulates that the rotation speed of the cooling fan needs to be dynamically adjusted between 1000 and 3000 revolutions per minute. The parsing tool can generate an initial parameter set and set the fan speed to 1500 revolutions per minute as the initial plan.

[0131] This method ensures that the cooling system can quickly respond to the instruction content.

[0132] Step S520, adjust the cooling system using the initial parameter set, generate real-time temperature distribution data through the temperature field simulation tool, and obtain a temperature distribution data set reflecting the battery operating environment.

[0133] In a possible implementation, after adjusting the cooling system using the initial parameter set, real-time temperature distribution data can be generated through the temperature field simulation tool.

[0134] The temperature field simulation tool will simulate the heat distribution based on the battery operating environment. For example, in a certain battery module, the initial fan speed is 1500 revolutions per minute, and the simulation tool shows that the temperature in the core area of the module is 45 degrees Celsius and the edge area is 38 degrees Celsius, generating a data set reflecting the overall temperature distribution.

[0135] This method is convenient for intuitively understanding the thermal state of the battery operating environment and provides a basis for subsequent optimization.

[0136] Step S530, if the maximum value in the temperature distribution data set exceeds the preset steady-state threshold, optimize the dynamic adjustment parameters through the data iteration update tool, generate a new cooling system adjustment parameter set, and determine the updated temperature adjustment plan.

[0137] It should be noted that if the maximum value in the temperature distribution dataset exceeds the preset steady-state threshold, for example, it is stipulated that the temperature in the core area shall not exceed 50 degrees Celsius, while the simulation shows that the temperature in a certain area reaches 52 degrees Celsius, then the dynamic adjustment parameters need to be optimized.

[0138] The data iterative update tool can analyze the temperature distribution, combine historical operation data, and adjust the fan speed to 2000 revolutions per minute, or increase the liquid cooling pump flow rate to 2 liters per minute.

[0139] Preferably, the new parameter set will generate an updated temperature regulation plan to ensure that the cooling system can respond more precisely to overheated areas.

[0140] This iterative method improves the adaptability of the system.

[0141] Step S540: Adjust the cooling system according to the updated temperature regulation plan, and analyze the temperature distribution data through the operation status judgment tool to obtain the stable operation status of the battery.

[0142] Specifically, after adjusting the cooling system according to the updated temperature regulation plan, the temperature distribution data can be analyzed through the operation status judgment tool. For example, after adjusting the fan speed to 2000 revolutions per minute, the simulation shows that the temperature in the core area drops to 48 degrees Celsius, and the temperature in the edge area stabilizes at 36 degrees Celsius.

[0143] The operation status judgment tool will further compare with historical data to confirm whether the battery is in a stable operation status.

[0144] Assume that the simulation shows that the temperature is lower than 50 degrees Celsius for three consecutive times, then it can be determined that the battery operation environment is stable.

[0145] This analysis method helps to timely detect potential temperature fluctuation problems.

[0146] In one embodiment, the design of the instruction set parsing tool needs to consider compatibility. For example, a certain battery system may use both air cooling and liquid cooling at the same time. The parsing tool needs to extract the parameters of the two cooling methods respectively and ensure that the adjustment ranges do not interfere with each other.

[0147] Assume that the air cooling parameter is a rotational speed of 1500 revolutions per minute, and the liquid cooling parameter is a flow rate of 1.5 liters per minute. The parsing tool will generate a coordinated initial parameter set to avoid system imbalance caused by single adjustment. This design improves the flexibility of the plan.

[0148] It can be understood that when the temperature field simulation tool generates data, it needs to combine the actual operating conditions of the battery. For example, when the battery is operating at high load, the heat is concentrated in the core area. The simulation tool will give priority to the temperature changes in this area and generate a more detailed dataset.

[0149] This targeted simulation method ensures the reliability of the data. For example, when analyzing data, the operating state judgment tool can combine the time dimension.

[0150] Suppose that after adjustment, the temperature of a certain battery system remains stable below 48 degrees Celsius for two consecutive hours, and the tool will record it as a stable state; if the temperature suddenly increases to 51 degrees Celsius within one hour, a warning will be triggered to prompt further optimization of the parameters. This multi-dimensional analysis method enhances the robustness of the system.

[0151] Furthermore, the resource allocation and scheduling method of the intelligent fixed energy storage and charging station provided in this embodiment, step S600 includes: Step S610: Obtain the temperature distribution data and power distribution data from the acquisition device, and judge whether the operating state is abnormal through a preset threshold. If the temperature distribution data exceeds the threshold, correct the power distribution data to obtain the corrected distribution data.

[0152] Exemplarily, when obtaining the temperature distribution data and power distribution data from the acquisition device, the operating state of the energy storage device can be monitored in real time through a sensor network.

[0153] The temperature distribution data reflects the heat changes in different areas of the battery module, and the power distribution data shows the power output of each module. For example, a certain energy storage system contains 10 battery modules. The acquisition device shows that the temperature of the core module is 42 degrees Celsius and the edge module is 35 degrees Celsius. The power distribution data indicates that the output power of the core module accounts for 40% of the total power. These data provide a basis for subsequent analysis.

[0154] In a possible implementation, judge whether the operating state is abnormal through a preset threshold. Assume that the temperature threshold is set at 50 degrees Celsius. If the temperature of the core module exceeds this value, it is regarded as abnormal.

[0155] Combined with the power distribution data, if it is found that the temperature of the module with high power output is on the high side, it can be inferred that the load distribution is uneven. For example, if the power output of a certain module accounts for 50% and the temperature reaches 52 degrees Celsius, it indicates that the load needs to be reduced to relieve overheating.

[0156] By adjusting the power distribution, generate the corrected distribution data. The power ratio of the core module is reduced to 30%, and the edge module is increased to 25% to balance the heat distribution.

[0157] Step S620: According to the corrected distribution data, combined with the grid demand data collected in real time, use a linear programming tool to calculate the charge and discharge ratio of the energy storage device to obtain the energy storage operation parameters.

[0158] Specifically, based on the corrected allocation data and combined with the power grid demand data, a linear programming tool is used to calculate the charge-discharge ratio of the energy storage device. The power grid demand data may show that additional discharge is required during peak electricity consumption periods.

[0159] Suppose the power grid demand during a certain period is 1000 kWh, and the corrected allocation data indicates that the available energy storage capacity is 1200 kWh. The linear programming tool can set the discharge ratio to 80%, and reserve 20% of the capacity to cope with sudden demands, generating energy storage operation parameters. This method ensures that the energy storage device can respond efficiently to the power grid demand.

[0160] Step S630: Process the energy storage operation parameters and the power grid demand data through a multi-objective optimization algorithm. When the energy storage efficiency constraint is met, calculate the economic benefit index to obtain the optimized scheduling parameters.

[0161] Preferably, process the energy storage operation parameters and the power grid demand data through a multi-objective optimization algorithm to calculate the economic benefit index.

[0162] The multi-objective optimization algorithm needs to balance the energy storage efficiency and the maximization of benefits. For example, for a certain energy storage system, under the constraint of meeting 80% discharge efficiency, it preferentially chooses to charge during low electricity price periods and discharge during high electricity price periods.

[0163] Suppose the electricity price at night is 0.3 yuan per kWh and during the day is 0.8 yuan. The optimization algorithm can adjust the charge-discharge timing sequence to generate scheduling parameters, and it is expected that the daily benefit will increase by 10%.

[0164] This process realizes the optimal allocation of resources through data-driven means.

[0165] Step S640: Generate a comprehensive scheduling plan including energy storage operation instructions and the electricity distribution ratio according to the optimized scheduling parameters, determine whether the comprehensive scheduling plan meets the requirements of the comprehensive indicators, and output the final scheduling plan.

[0166] In one embodiment, a comprehensive scheduling plan is generated according to the optimized scheduling parameters, including energy storage operation instructions and the electricity distribution ratio.

[0167] Suppose the scheduling parameters require the discharge power of the core module to be 200 kW and that of the edge module to be 150 kW, and the operation instructions specify the start and stop times of each module.

[0168] The scheduling plan needs to meet the requirements of the comprehensive indicators, such as the temperature being lower than 50 degrees Celsius and the benefit being higher than the benchmark value. For example, after operation, the temperature stabilizes at 48 degrees Celsius and the benefit reaches the expectation, then the plan is the final output. This design ensures the comprehensiveness and executability of the scheduling plan.

[0169] It is understandable that when judging whether the integrated scheduling scheme meets the requirements, it can be verified by simulation operation. Suppose that after a certain scheme is run, the temperature drops to 46 degrees Celsius, the power distribution is balanced, and the profit exceeds the benchmark value by 5%, then the scheme is confirmed to be effective. If the temperature still exceeds the standard, it is necessary to trace back and optimize the parameters and adjust the charge-discharge ratio. This verification method improves the reliability of the scheme.

[0170] Another aspect of the present invention relates to a resource allocation and scheduling system for an intelligent fixed charge and storage station, which is used to implement the resource allocation and scheduling method of the above-mentioned intelligent fixed charge and storage station, including a first generation module, a calculation module, an adjustment module, a second generation module, a third generation module, and a fourth generation module. Among them, the first generation module is used to obtain the state of charge, charge-discharge rate, and temperature data of the battery unit, and generate a multi-dimensional operation state matrix by combining the grid load and power market price information; the calculation module is used to calculate the power distribution ratio of each battery unit according to the multi-dimensional operation state matrix by using a preset state of charge threshold and charge-discharge rate constraint, and generate an initial power distribution scheme; the adjustment module is used to, if there is a battery unit with a charge-discharge rate exceeding the safe range in the initial power distribution scheme, predict the performance decay trend based on historical operation data, and adjust the distribution ratio to generate a corrected power distribution scheme; the second generation module is used to obtain real-time temperature data according to the corrected power distribution scheme and judge the local overheating risk, and generate a temperature adjustment instruction set including heat dissipation power adjustment parameters; the third generation module is used to dynamically adjust the cooling system by using the temperature adjustment instruction set, generate optimized temperature distribution data, and determine the stable operation state of the battery; the fourth generation module is used to input the optimized temperature distribution data and the corrected power distribution scheme into a multi-objective optimization algorithm, and generate an integrated scheduling scheme including energy storage efficiency and economic benefit indicators in combination with the real-time grid demand; the fifth generation module is used to, if the power market price fluctuation range in the integrated scheduling scheme exceeds the preset range, recalculate the scheduling priority based on the prediction model, and generate a final scheduling scheme.

[0171] Furthermore, the resource allocation and scheduling system of the intelligent fixed charging and storage station involved in this embodiment, the first generation module includes a first acquisition unit, a second acquisition unit, a third acquisition unit, a fourth acquisition unit, a fifth acquisition unit, a sixth acquisition unit, and a seventh acquisition unit. Among them, the first acquisition unit is used to obtain the state of charge, charge and discharge rate, and temperature data from the battery management system, obtain the load data from the power grid dispatching system through the interface, and obtain the market price data from the power trading platform to obtain the original data set; the second acquisition unit is used to process the original data set by using the data cleaning method, if the data is missing, it is completed by the interpolation method, and if there are outliers, it is removed by the preset threshold to obtain the cleaned data set; the third acquisition unit is used to normalize the state of charge, charge and discharge rate, temperature data, grid load data, and electricity market price according to the cleaned data set by using the normalization method to obtain the normalized data set; the fourth acquisition unit is used to extract the correlation features between the state of charge, charge and discharge rate, temperature data and grid load data, and electricity market price by using the principal component analysis method through the normalized data set to obtain the feature data set; the fifth acquisition unit is used to combine the state of charge, charge and discharge rate, temperature data, grid load data, and electricity market price by using the weighted fusion algorithm if the correlation features in the feature data set meet the preset correlation threshold to obtain the initial multi-dimensional operation state matrix; the sixth acquisition unit is used to adopt the dynamic update mechanism according to the initial multi-dimensional operation state matrix, and if the real-time data change is detected, the matrix parameters are adjusted by the incremental update algorithm to obtain the updated multi-dimensional operation state matrix; the seventh acquisition unit is used to analyze the dynamic correlation relationship between the state of charge, charge and discharge rate, temperature data and grid load data, and electricity market price by using the matrix decomposition method through the updated multi-dimensional operation state matrix to obtain the operation state analysis result.

[0172] Furthermore, the resource allocation and scheduling system of the intelligent fixed charging and discharging station involved in this embodiment, the calculation module includes an eighth acquisition unit, a ninth acquisition unit, a tenth acquisition unit, and an eleventh acquisition unit. Among them, the eighth acquisition unit is used to obtain the state of charge data and charge and discharge rate data of the battery unit from the multi-dimensional operation state matrix. Through a logical judgment method, using a preset state of charge threshold and charge and discharge rate constraint, if the state of charge is higher than the threshold and the charge and discharge rate meets the constraint, it is marked as an available battery unit, and a set of available battery units is obtained; the ninth acquisition unit is used to obtain the state of charge value and charge and discharge rate value of each battery unit according to the set of available battery units. Through a weighted calculation method, using a preset weight ratio of the state of charge and the charge and discharge rate, calculate the power distribution coefficient of each battery unit to obtain a set of power distribution coefficients; the tenth acquisition unit is used to obtain the grid load data and price fluctuation data from the multi-dimensional operation state matrix, and adopt a normalization processing method to standardize and fuse the set of power distribution coefficients with the grid load data and price fluctuation data. If the grid load is higher than the preset threshold, it is preferentially allocated to the high coefficient battery unit to obtain a preliminary allocation ratio set; the eleventh acquisition unit is used to optimize and adjust the power distribution ratio of each battery unit according to the preliminary allocation ratio set by using an iterative calculation method, and analyze the dynamic correlation between the preliminary allocation ratio set and the matrix data through a linear regression algorithm to obtain an initial power distribution plan.

[0173] This embodiment discloses an intelligent scheduling method and system for a battery energy storage system. Compared with the prior art, by obtaining the operation state data of the battery unit and grid information, a multi-dimensional operation state matrix is generated, and a power distribution plan is calculated. According to the charge and discharge rate and temperature data, the distribution plan and the cooling system are dynamically adjusted to ensure the safe and stable operation of the battery. Combining with the real-time grid demand, a multi-objective optimization algorithm is used to generate a comprehensive scheduling plan, and it is adjusted according to the power market price fluctuation to achieve the balance of energy storage efficiency and economic benefits. This embodiment can effectively improve the operation efficiency and economic benefits of the battery energy storage system, while ensuring the safety and stability of the system, and provides a new solution for the intelligent scheduling of large-scale energy storage systems.

[0174] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A resource allocation and scheduling method for an intelligent fixed storage and charging station, characterized in that: The following steps are involved: Obtain the state of charge, charge and discharge rate and temperature data of the battery unit, combine the grid load and electricity market price information to generate a multi-dimensional operating status matrix; According to the multi-dimensional operating state matrix, the preset state of charge threshold and charge and discharge rate constraint are used to calculate the power distribution ratio of each battery unit and generate an initial power distribution plan; If there are battery cells whose charge and discharge rates exceed the safe range in the initial power distribution plan, the performance degradation trend is predicted based on historical operation data, and the distribution ratio is adjusted to generate a revised power distribution plan; According to the modified power distribution scheme, real-time temperature data is acquired and local overheating risk is determined, and a temperature adjustment instruction set including heat dissipation power adjustment parameters is generated; Dynamically adjust the cooling system using the temperature adjustment instruction set to generate optimized temperature distribution data and determine the stable operating state of the battery; The optimized temperature distribution data and the modified power distribution plan are input into a multi-objective optimization algorithm, and a comprehensive dispatching plan including energy storage efficiency and economic benefit indicators is generated in combination with real-time power grid demand.

2. The resource allocation and scheduling method of the intelligent fixed storage and charging station according to claim 1, characterized in that: The step of acquiring the state of charge, charge and discharge rate and temperature data of the battery unit and combining the grid load and power market price information to generate a multi-dimensional operation state matrix includes: Obtain the state of charge, charge and discharge rate, and temperature data from the battery management system, obtain load data from the power grid dispatching system through the interface, and obtain market price data from the power trading platform to obtain the original data set; The original data set is processed using data cleaning methods. If the data is missing, it is supplemented by interpolation. If there are outliers, they are removed by a preset threshold to obtain a cleaned data set. According to the cleaned data set, the state of charge, charge and discharge rate, temperature data, grid load data, and electricity market price are normalized using a standardized method to obtain a normalized data set; By normalizing the data set, principal component analysis is used to extract the correlation features between the state of charge, charge and discharge rate, temperature data and grid load data, and electricity market price, and obtain a feature data set. If the associated features in the feature data set meet the preset correlation threshold, the state of charge, charge and discharge rate, temperature data, grid load data, and electricity market price are combined through a weighted fusion algorithm to obtain an initial multi-dimensional operating state matrix; According to the initial multi-dimensional operation state matrix, a dynamic update mechanism is adopted. If a real-time data change is detected, the matrix parameters are adjusted through an incremental update algorithm to obtain an updated multi-dimensional operation state matrix; Through the updated multi-dimensional operating status matrix, the matrix decomposition method is used to analyze the dynamic correlation between the state of charge, charge and discharge rate, temperature data and grid load data, and electricity market price to obtain the operating status analysis results.

3. The resource allocation and scheduling method of the intelligent fixed storage and charging station according to claim 1, characterized in that: The steps of calculating the power distribution ratio of each battery unit according to the multi-dimensional operating state matrix and using the preset state of charge threshold and charge and discharge rate constraint to generate an initial power distribution plan include: Acquire the state of charge data and charge and discharge rate data of the battery cell from the multi-dimensional operation state matrix, and through a logic judgment method, use a preset state of charge threshold and charge and discharge rate constraint, if the state of charge is higher than the threshold and the charge and discharge rate satisfies the constraint, mark it as an available battery cell, and obtain an available battery cell set; According to the set of available battery cells, a state of charge value and a charge-discharge rate value of each battery cell are obtained, and a power distribution coefficient of each battery cell is calculated by a weighted calculation method using a preset weight ratio of the state of charge and the charge-discharge rate to obtain a set of power distribution coefficients; Obtaining grid load data and price fluctuation data from the multi-dimensional operation state matrix, using a normalization processing method to standardize and fuse the electric energy allocation coefficient set with the grid load data and price fluctuation data, and if the grid load is higher than a preset threshold, it is allocated to the high-coefficient battery unit first, and a preliminary allocation ratio set is obtained; According to the preliminary allocation ratio set, an iterative calculation method is used to optimize and adjust the power allocation ratio of each battery unit, and the dynamic relationship between the preliminary allocation ratio set and the matrix data is analyzed by a linear regression algorithm to obtain an initial power allocation plan.

4. The resource allocation and scheduling method of the intelligent fixed storage and charging station according to claim 1, characterized in that: If there are battery cells with charge and discharge rates exceeding the safe range in the initial power distribution plan, the steps of predicting the performance degradation trend based on historical operation data and adjusting the distribution ratio to generate a revised power distribution plan include: Acquire the operating status of each battery unit through a data acquisition tool, collect the charge and discharge rate and operating time of the battery unit, and obtain a historical operating data set; If the charge and discharge rate in the historical operation data set exceeds a preset safety range, a linear regression algorithm is used to analyze the historical operation data set to predict the performance degradation trend of each battery cell and determine a performance degradation curve; According to the performance attenuation curve, the allocation ratio in the initial electric energy allocation is adjusted, and the power allocation of each battery unit is recalculated by an optimization adjustment tool to obtain a revised electric energy allocation plan; With respect to the modified power distribution plan, a data acquisition tool is used to verify whether the charge and discharge rate of each battery unit is within a preset safety range, so as to determine the feasibility of the modified power distribution plan.

5. The resource allocation and scheduling method of the intelligent fixed storage and charging station according to claim 1, characterized in that: According to the modified power distribution scheme, the steps of acquiring real-time temperature data and determining the risk of local overheating, and generating a temperature adjustment instruction set including heat dissipation power adjustment parameters include: Acquire real-time temperature data from environmental sensors and use the data acquisition module to dynamically adjust the acquisition frequency. If the temperature change rate exceeds the preset threshold, increase the acquisition frequency to obtain a high-precision temperature data set. According to the high-precision temperature data set, a logic judgment method is used to analyze the temperature distribution of the local area. If the temperature of a certain area is continuously higher than the average value and exceeds the preset overheating threshold, the local overheating risk area is determined; For the local overheating risk area, obtain the current heat dissipation equipment operating parameters, calculate the heat dissipation power adjustment amount through the support vector machine algorithm, and generate a control signal set including the power adjustment parameters; The control signal set is converted into a temperature adjustment instruction set through an instruction generation module, the instruction set is sent to the heat dissipation device using a communication protocol, and the device status update after the execution of the instruction is determined.

6. The resource allocation and scheduling method of the intelligent fixed storage and charging station according to claim 1, characterized in that: The steps of dynamically adjusting the cooling system using the temperature adjustment instruction set, generating optimized temperature distribution data and determining the stable operating state of the battery include: Obtaining preset dynamic adjustment parameters from the temperature adjustment instruction set, decomposing the instruction content through an instruction set parsing tool, generating an initial parameter set including the adjustment range of the cooling system, and determining an initial temperature adjustment plan; The cooling system is adjusted using the initial parameter set, and real-time temperature distribution data is generated by a temperature field simulation tool to obtain a temperature distribution data set reflecting the battery operating environment; If the maximum value in the temperature distribution data set exceeds a preset stable state threshold, the dynamic adjustment parameters are optimized by a data iteration update tool, a new cooling system adjustment parameter set is generated, and an updated temperature adjustment scheme is determined; The cooling system is adjusted according to the updated temperature adjustment scheme, and the temperature distribution data is analyzed by an operation status judgment tool to obtain a stable operation status of the battery.

7. The resource allocation and scheduling method of the intelligent fixed storage and charging station according to claim 1, characterized in that: The steps of inputting the optimized temperature distribution data and the modified power distribution plan into a multi-objective optimization algorithm and generating a comprehensive dispatching plan including energy storage efficiency and economic benefit indicators in combination with real-time grid demand include: Acquire temperature distribution data and electric energy distribution data from a collection device, determine whether the operating state is abnormal by a preset threshold value, and if the temperature distribution data exceeds the threshold value, correct the electric energy distribution data to obtain corrected distribution data; According to the corrected allocation data, combined with the real-time collected grid demand data, a linear programming tool is used to calculate the charge and discharge ratio of the energy storage device to obtain energy storage operation parameters; Processing the energy storage operation parameters and the grid demand data through a multi-objective optimization algorithm, calculating the economic benefit index when satisfying the energy storage efficiency constraint, and obtaining the optimized dispatching parameters; According to the optimized dispatching parameters, a comprehensive dispatching plan including energy storage operation instructions and electric energy distribution ratio is generated, and it is determined whether the comprehensive dispatching plan meets the comprehensive index requirements, and a final dispatching plan is output.

8. A resource allocation and scheduling system for an intelligent fixed storage and charging station, used to implement the resource allocation and scheduling method for an intelligent fixed storage and charging station according to any one of claims 1 to 7, characterized in that: include: The first generation module is used to obtain the state of charge, charge and discharge rate and temperature data of the battery unit, and generate a multi-dimensional operation state matrix in combination with the grid load and power market price information; A calculation module, used to calculate the power distribution ratio of each battery unit according to the multi-dimensional operation state matrix, using a preset state of charge threshold and charge and discharge rate constraint, and generate an initial power distribution plan; An adjustment module, configured to adjust the allocation ratio to generate a revised power distribution plan based on the historical operation data to predict the performance degradation trend if there are battery cells with charge and discharge rates exceeding the safe range in the initial power distribution plan; A second generation module is used to obtain real-time temperature data and determine the risk of local overheating according to the modified power distribution plan, and generate a temperature adjustment instruction set including heat dissipation power adjustment parameters; A third generation module is used to dynamically adjust the cooling system using the temperature adjustment instruction set, generate optimized temperature distribution data and determine the stable operation state of the battery; A fourth generation module is used to input the optimized temperature distribution data and the modified power distribution plan into a multi-objective optimization algorithm, and generate a comprehensive dispatching plan including energy storage efficiency and economic benefit indicators in combination with real-time power grid demand; The fifth generation module is used to recalculate the scheduling priority based on the prediction model and generate a final scheduling plan if the fluctuation range of the electricity market price in the comprehensive scheduling plan exceeds a preset range.

9. The resource allocation and dispatching system of the intelligent fixed storage and charging station as claimed in claim 8, characterized in that: The first generation module comprises: The first acquisition unit is used to obtain the state of charge, charge and discharge rate, and temperature data from the battery management system, obtain load data from the power grid dispatching system through an interface, and obtain market price data from the power trading platform to obtain an original data set; The second acquisition unit is used to process the original data set by using a data cleaning method. If the data is missing, it is supplemented by an interpolation method. If there are outliers, they are removed by a preset threshold to obtain a cleaned data set; A third acquisition unit is used to normalize the state of charge, charge and discharge rate, temperature data, grid load data, and power market price according to the cleaned data set by using a standardized method to obtain a normalized data set; The fourth acquisition unit is used to extract the correlation characteristics between the state of charge, charge and discharge rate, temperature data and the power grid load data and the power market price by normalizing the data set and adopting the principal component analysis method to obtain a characteristic data set; A fifth acquisition unit is used to combine the state of charge, charge and discharge rate, temperature data, grid load data, and power market price through a weighted fusion algorithm to obtain an initial multi-dimensional operating state matrix if the associated features in the feature data set meet a preset correlation threshold; A sixth acquisition unit is used to adopt a dynamic update mechanism according to the initial multi-dimensional operation state matrix, and if a real-time data change is detected, adjust the matrix parameters through an incremental update algorithm to obtain an updated multi-dimensional operation state matrix; The seventh acquisition unit is used to analyze the dynamic correlation between the state of charge, charge and discharge rate, temperature data and grid load data, and electricity market price through the updated multi-dimensional operation state matrix by using the matrix decomposition method to obtain the operation state analysis result.

10. The resource allocation and dispatching system of the intelligent fixed storage and charging station according to claim 8, characterized in that: The calculation module comprises: an eighth acquisition unit, configured to acquire the state of charge data and the charge and discharge rate data of the battery cell from the multidimensional operation state matrix, and to mark the battery cell as an available battery cell if the state of charge is higher than the threshold and the charge and discharge rate satisfies the constraint by using a logic judgment method and a preset state of charge threshold and charge and discharge rate constraint, thereby obtaining a set of available battery cells; a ninth acquisition unit, configured to acquire the state of charge value and the charge and discharge rate value of each battery cell according to the set of available battery cells, and calculate the electric energy distribution coefficient of each battery cell by a weighted calculation method and a preset weight ratio of the state of charge and the charge and discharge rate to obtain an electric energy distribution coefficient set; a tenth acquisition unit, configured to acquire grid load data and price fluctuation data from the multidimensional operation state matrix, and to perform standardized fusion of the electric energy allocation coefficient set with the grid load data and the price fluctuation data by a normalization processing method; if the grid load is higher than a preset threshold, the electric energy allocation coefficient set is allocated to the high-coefficient battery unit first, and a preliminary allocation ratio set is obtained; The eleventh acquisition unit is used to optimize and adjust the power distribution ratio of each battery cell according to the preliminary distribution ratio set by adopting an iterative calculation method, and to analyze the dynamic relationship between the preliminary distribution ratio set and the matrix data by a linear regression algorithm to obtain an initial power distribution plan.

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