Energy storage system and intelligent control method thereof

Abstract

The invention provides an energy storage system and an intelligent control method thereof, belongs to the field of energy storage technologies, and is used for solving the problems that an energy storage system in related technologies controls underestimated materials and equipment to accumulate attenuation, is difficult to adapt to multi-scale fluctuation and random disturbance, and cannot give consideration to long-term cost and efficiency stability. According to the method, a double-attenuation coefficient and a multi-cycle optimization target are obtained through a control module, a heat storage / release instruction is generated based on multi-scale signal decomposition and a fractional order random optimal control model, the instruction is corrected in combination with the environment temperature and the load type and then sent to all modules, and the system comprises energy storage, cold and heat source, control and other modules to support execution of the method. And the minimization of long-term energy consumption cost and the stabilization of full-period energy efficiency are realized.

Description

Technical Field

[0001] This application relates to the field of energy storage technology, and in particular to an energy storage system and its intelligent control method. Background Technology

[0002] Currently, energy storage technology is widely used in building heating and cooling, industrial process energy use, and other scenarios. The core demand revolves around peak-valley electricity price arbitrage and stable efficiency throughout the entire life cycle. As the operating time of energy storage systems increases, the cumulative degradation of energy storage materials (such as phase change materials) and the performance degradation of related equipment (such as cold and heat sources and hydraulic conversion modules) (collectively referred to as "double degradation") become key factors affecting long-term efficiency. The industry has begun to pay attention to the coupled impact of double degradation on cost and efficiency.

[0003] In existing technologies, dual-decay optimization often uses linear fitting models to calculate the decay coefficient (e.g., deriving the annual decay rate linearly from the number of material cycles), while multi-cycle optimization relies on discrete reinforcement learning (e.g., Q-Learning) to adjust the heat storage / release strategy, while adapting to multi-scale load fluctuations through a fixed period (e.g., 7 days). For example, one scheme predicts the decay rate of phase change materials using a linear formula, and combines it with single-cycle reinforcement learning to optimize heat storage / release, attempting to balance decay and cost.

[0004] However, existing technologies have significant drawbacks: First, linear fitting cannot capture the historical cumulative memory of dual decay, resulting in a 15%-20% error in predicting the annual decay rate, thus underestimating long-term decay. Second, discrete reinforcement learning struggles to continuously cope with random fluctuations in electricity prices and loads, leading to multi-cycle cost fluctuations of ±8%-10%. Third, single-cycle optimization fails to separate daily / weekly / monthly scale signals, making weekly load fluctuations prone to interfering with monthly decay optimization, resulting in long-term efficiency fluctuations exceeding ±5%. These shortcomings prevent existing technologies from meeting the core requirements of "stable long-term efficiency and optimal cost" under dual decay across the entire system. Summary of the Invention

[0005] This application provides an energy storage system and its intelligent control method, which can minimize long-term multi-cycle energy consumption costs and stabilize energy efficiency throughout the entire cycle through dual attenuation coefficient quantization and multi-cycle optimization.

[0006] Firstly, this application provides an intelligent control method for an energy storage system. Specifically, the method includes: a control module acquiring the dual attenuation coefficient and multi-cycle optimization objectives of the energy storage module; the control module generating heat storage / heat release control commands for the energy storage module based on the dual attenuation coefficient and multi-cycle optimization objectives; and the control module sending the heat storage / heat release control commands to the energy storage module, the heat source module, and the hydraulic conversion module to control the heat storage / heat release process of the energy storage module. The heat storage process involves heating the energy storage material using the heat source module during off-peak hours and storing it in the energy storage module, while the heat release process involves releasing the heat from the energy storage material during peak hours to reduce grid power consumption. The dual attenuation coefficient characterizes the cumulative attenuation degree of the energy storage material and associated equipment in the energy storage module, and the multi-cycle optimization objectives include minimizing long-term multi-cycle energy consumption costs and stabilizing energy efficiency throughout the entire cycle.

[0007] By adopting the above technical solutions, the cumulative attenuation effects of materials and equipment can be accurately captured with the help of dual attenuation coefficients. Combined with multi-cycle optimization objectives, heat storage / release strategies can be formulated to avoid the short-sightedness of single-cycle control. From a long-term perspective, energy consumption costs and energy efficiency can be balanced, and the problems of underestimating attenuation and strategy mismatch in existing methods can be solved.

[0008] Furthermore, the process of the control module obtaining the dual attenuation coefficients includes: the control module obtaining the basic parameters of the energy storage material and the basic parameters of the associated equipment; the control module using a fractional calculus model to calculate the material attenuation coefficient based on the basic parameters of the energy storage material; the control module using a fractional calculus model to calculate the equipment attenuation coefficient based on the basic parameters of the associated equipment; and the control module weightedly coupling the material attenuation coefficient and the equipment attenuation coefficient to obtain the dual attenuation coefficients.

[0009] By adopting the above technical solution and utilizing the memory characteristics of the fractional calculus model, the attenuation coefficients of materials and equipment can be accurately calculated. Then, through weighted coupling, a system-level dual attenuation coefficient can be obtained. Compared with the linear model, this better reflects the cumulative characteristics of attenuation and improves the accuracy of attenuation quantification.

[0010] Furthermore, the basic parameters of the energy storage material include the number of heat storage / heat release cycles, the highest historical operating temperature of the material, and the rated attenuation benchmark value of the material; the basic parameters of the associated equipment include the cumulative usage time of the equipment, the historical operating temperature of the equipment, and the rated attenuation benchmark value of the equipment; when the control module performs weighted coupling of the material attenuation coefficient and the equipment attenuation coefficient, it first sets the material attenuation weight and the equipment attenuation weight respectively according to the degree of influence of the energy storage material and the associated equipment on the energy storage efficiency, then multiplies the material attenuation coefficient by the material attenuation weight to obtain the material attenuation contribution value, multiplies the equipment attenuation coefficient by the equipment attenuation weight to obtain the equipment attenuation contribution value, and finally sums the material attenuation contribution value and the equipment attenuation contribution value to obtain the double attenuation coefficient.

[0011] By adopting the above technical solution, the basic parameters required for attenuation calculation are clarified, and the differences in the impact of materials and equipment on efficiency are distinguished by weight setting, making the calculation of dual attenuation coefficients more targeted and accurate, and providing reliable data support for subsequent control strategies.

[0012] Furthermore, the process of the control module generating heat storage / heat release control commands includes: the control module acquiring load signals and dual-attenuation signals; the control module using a multi-scale signal decomposition algorithm to process the load signals and dual-attenuation signals, separating celestial, weekly, and monthly signal components; the control module generating corresponding sub-control logic based on the characteristics of each scale signal component; and the control module fusing the sub-control logic at each scale to generate the final heat storage / heat release control commands.

[0013] By adopting the above technical solution, the multi-scale signal decomposition algorithm can separate the fluctuation characteristics of different time dimensions, generate sub-control logic for each scale and fuse them, so that the heat storage / heat release strategy can adapt to the multi-scale changes of load and decay, and avoid strategy lag or mismatch caused by single-scale control.

[0014] Furthermore, the control module performs multi-scale decomposition on the load signal and the dual-attenuation signal, and calculates the energy proportion of each signal component. The control module optimizes the intraday heat storage / release rhythm based on the characteristics of the daily signal component, adapts to the intraweek load fluctuations based on the characteristics of the weekly signal component, and optimizes the long-term attenuation compensation strategy based on the characteristics of the monthly signal component.

[0015] By adopting the above technical solutions, the influence weight of signals at each scale is clarified based on the energy ratio, and the control focus of different periods is optimized in a targeted manner to achieve precise adaptation of intraday, intraweek, and long-term attenuation compensation, thereby further improving the matching degree between strategy and demand.

[0016] Furthermore, the process of the control module generating thermal storage / heat release control commands also includes: the control module constructing a fractional-order stochastic optimal control model; the control module acquiring real-time electricity price data and real-time load data, extracting electricity price fluctuation characteristics and load fluctuation characteristics, and converting the fluctuation characteristics into random disturbance parameters and incorporating them into the fractional-order stochastic optimal control model; the control module adjusting the thermal storage power and heat release rate of the energy storage module in real time by solving the continuous optimization equation corresponding to the fractional-order stochastic optimal control model; and the control module refining the thermal storage / heat release control commands based on the adjusted thermal storage power and heat release rate.

[0017] By adopting the above technical solutions, the fractional-order stochastic optimal control model can incorporate the stochastic disturbances of electricity price and load, and adjust the heat storage / heat release parameters in real time through continuous optimization equations. Compared with discrete control, it is better able to cope with dynamic changes and improves the real-time performance and robustness of the strategy.

[0018] Furthermore, when constructing a fractional-order stochastic optimal control model, the control module aims to minimize the long-term expected cost and constructs an objective function that includes an energy charging cost term and an efficiency fluctuation penalty term. Based on the principle of fractional calculus, the control module constructs a continuous mathematical model of system state evolution to describe the dynamic relationship between the energy storage module capacity, the dual attenuation coefficient, and the random disturbance parameters. When solving the continuous optimization equation, the control module outputs the thermal power and heat release rate that adapt to random disturbances in real time.

[0019] By adopting the above technical solutions, the objective function takes into account both cost and efficiency, the system state evolution model fits the actual dynamic relationship, and the solution results can accurately adapt to random disturbances, ensuring that the long-term expected cost is minimized while controlling efficiency fluctuations and improving the stability of system operation.

[0020] Furthermore, the process of the control module acquiring data includes: the control module acquiring external grid signals through the grid interface module, the external grid signals including peak-valley electricity price time periods, real-time electricity price data, and demand response subsidy signals; the control module adjusting the trigger threshold and optimization priority of thermal storage / heat release control based on the external grid signals; and the control module incorporating the adjusted trigger threshold and optimization priority into multi-cycle optimization logic for subsequent generation of thermal storage / heat release control commands.

[0021] By adopting the above technical solutions, access to external power grid signals can be adapted in real time to electricity pricing policies and demand response requirements, adjusting trigger thresholds and optimizing priorities, making the control strategy more in line with the power grid environment, and improving the system's economy and power grid interaction capabilities.

[0022] Furthermore, when the control module controls the heat storage / release process of the energy storage module, it also includes: the control module acquiring the current ambient temperature and the type of the end load; the control module adjusting the operating parameters of the cold and heat source modules based on the current ambient temperature; the control module adjusting the heat storage / release rate and energy storage capacity adaptation logic of the energy storage module based on the type of the end load; and the control module correcting the heat storage / release control command according to the above adjustment results and sending it to each module.

[0023] By adopting the above technical solutions and combining them with dynamic correction of control commands based on ambient temperature and end load type, the strategy can be adapted to different climate conditions and energy consumption scenarios, thereby improving the system's environmental adaptability and scenario compatibility.

[0024] Secondly, this application provides an energy storage system. The system includes an energy storage module, a heat source / cold source module, a hydraulic conversion module, a control module, a grid interface module, and an end-user adaptation module. The energy storage module is configured to store heat using energy storage materials as the energy storage medium, reflecting the energy storage capacity through temperature changes in the energy storage materials, and outputting decay state parameters including energy storage material temperature, energy storage material state, and energy storage material cycle count to the control module. The heat source / cold source module is configured to provide the cold or heat energy required for energy storage and output real-time energy efficiency parameters and operating temperature parameters to the control module. The hydraulic conversion module is configured to transmit cold or heat and output transmission loss parameters and cycle count parameters to the control module. The grid interface module is configured to acquire external grid signals and transmit the external grid signals to the control module. The end-user adaptation module is configured to connect different types of end loads and output real-time load parameters to the control module. The control module is configured to execute the intelligent control method described in any of the first aspects above, receiving parameters and signals output by each module, generating and sending heat storage / heat release control commands to the energy storage module, the heat source / cold source module, and the hydraulic conversion module.

[0025] By adopting the above technical solution, the system modules have clear division of labor and data interconnection. The control module can execute the aforementioned intelligent control method based on the parameters of each module, providing hardware support for dual-attenuation quantization, multi-scale optimization, and random disturbance response, and ensuring the achievement of long-term multi-cycle cost and efficiency goals.

[0026] In summary, this application has at least the following beneficial effects:

[0027] 1. An energy storage system and its intelligent control method are provided to minimize long-term, multi-cycle energy consumption costs and stabilize efficiency;

[0028] 2. Improve the accuracy of double-attenuation quantization by using a fractional-order calculus model to adapt to multi-scale fluctuations and random disturbances;

[0029] 3. The system hardware and control methods work together to improve environmental adaptability and scenario compatibility.

[0030] It should be understood that the description in the Summary Section is not intended to limit the key or essential features of the embodiments of this application, nor is it intended to restrict the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0031] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0032] Figure 1A schematic diagram of an energy storage system according to an embodiment of this application is shown.

[0033] Figure 2 A flowchart of an intelligent control method for an energy storage system according to an embodiment of this application is shown. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0035] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0036] This application provides an energy storage system and its intelligent control method, which adapts to multi-scale fluctuations and random disturbances through dual attenuation coefficient quantization and multi-cycle optimization, and balances the minimization of long-term multi-cycle energy consumption costs with the stabilization of energy efficiency throughout the entire cycle, thereby improving the system's economy, robustness and scenario compatibility.

[0037] In a first aspect, embodiments of this application disclose an energy storage system and a macroscopic equipment environment support system for its intelligent control.

[0038] Figure 1 A schematic diagram of an energy storage system according to an embodiment of this application is shown.

[0039] Reference Figure 1 The system comprises three major hardware support systems: an external power grid environment unit, a physical equipment cluster unit, and a climate and load monitoring unit. Each system is connected to the control center through a data interaction link, jointly providing the hardware foundation and environmental adaptability for the realization of "an energy storage system and its intelligent control method".

[0040] The external power grid environment unit serves as the system's energy and policy signal source, including a power grid marketing signal interface, a demand response (DR) platform access module, and a voltage stabilization device. The power grid marketing signal interface is used to acquire peak-valley electricity price time periods and real-time floating price data; the DR platform access module receives DR subsidy signals; and the voltage stabilization device adapts to ±10% power grid voltage fluctuations to prevent equipment shutdowns due to voltage anomalies. This unit transmits power grid signals to the control center via an encrypted communication link, providing core input for the economic optimization of thermal energy storage / release strategies.

[0041] The physical equipment cluster unit is the core carrier of the system's energy storage, transmission, and control. It includes an energy storage module, a cold / heat source module, a hydraulic conversion module, a control module, and auxiliary support modules. The energy storage module is equipped with phase change materials, an energy storage tank, temperature sensors, and a capacity monitor to store cold / heat and output decay status parameters. The cold / heat source module mainly uses an air source heat pump with an electric boiler as a backup, supports power regulation, and outputs real-time energy efficiency and operating temperature. The hydraulic conversion module includes a variable frequency water pump, electric valves, and flow / pressure sensors to dynamically adjust the energy transmission rate and output transmission loss data. The control module is equipped with a multi-scale signal processing unit, a fractional-order computing chip, and an HJB equation solver to realize data processing and command generation. The auxiliary support modules include a grid interface module, a terminal adaptation module, and a data storage module, which are responsible for grid signal interaction, terminal load connection, and historical data storage, respectively. Each module communicates bidirectionally with the control center through standardized communication protocols (such as Modbus and BACnet) to receive control commands and feedback operating parameters.

[0042] The climate and load monitoring unit is designed to adapt to different environmental and scenario requirements, including a climate environment monitoring module and a load environment monitoring module. The climate environment monitoring module is equipped with temperature and humidity sensors to collect environmental temperature and humidity data within the range of -10℃ to 40℃, which is used to correct the energy efficiency of cold and heat sources and the heat loss rate of energy storage modules. The load environment monitoring module connects to different types of terminals such as building air conditioning and industrial process equipment through a terminal adaptation module, and collects real-time load data and load type identification, which is used to adjust the heat storage / heat release rate and energy storage capacity adaptation logic. This unit transmits the monitoring data to the control center in real time, providing a basis for optimizing the environmental adaptability of control strategies.

[0043] The three major hardware support systems mentioned above work together: the external power grid environment unit provides policy and energy foundations, the physical equipment cluster unit realizes the core processing of energy and data, and the climate and load monitoring unit ensures that the system is adaptable to multiple scenarios. Through the overall scheduling of the control center, the three form a complete macro-equipment environment support system, ensuring the stable realization of core functions such as dual attenuation quantization, multi-scale optimization, and random disturbance response in "an intelligent control method for an energy storage system".

[0044] Secondly, embodiments of this application disclose an intelligent control method for an energy storage system.

[0045] Figure 2 A flowchart of an intelligent control method for an energy storage system according to an embodiment of this application is shown.

[0046] Reference Figure 2 The method specifically includes the following steps:

[0047] S1: The control module obtains the dual attenuation coefficient and multi-cycle optimization target of the energy storage module.

[0048] In this method, the dual attenuation coefficient characterizes the cumulative attenuation degree of energy storage materials and associated equipment in the energy storage module. The multi-cycle optimization objectives include minimizing long-term multi-cycle energy consumption costs and stabilizing energy efficiency throughout the entire cycle. The process of the control module obtaining the dual attenuation coefficient includes the control module acquiring the basic parameters of the energy storage materials and the associated equipment. The control module uses a fractional calculus model to calculate the material attenuation coefficient based on the basic parameters of the energy storage materials. The control module also uses a fractional calculus model to calculate the equipment attenuation coefficient based on the basic parameters of the associated equipment. The control module performs weighted coupling of the material attenuation coefficient and the equipment attenuation coefficient to obtain the dual attenuation coefficient. The fractional calculus model uses a Caputo-type fractional derivative operator, which can accurately capture the "historical memory" of attenuation—that is, the current attenuation state depends on all historical usage processes, rather than just the instantaneous state, which aligns with the cumulative effect of energy storage material and equipment attenuation. Its core formula is as follows:

[0049]

[0050] In the formula:

[0051] For Caputo fractional derivative operators, (Memory coefficient), derived from the fitting of 1000 energy storage material charge-discharge cycle experiments, with a value of 0.6, because the material's decay memory is at a moderate level;

[0052] Let t be the energy storage material attenuation coefficient. (Indicates no attenuation), sourced from the solution of the above formula, and updated in real time during operation;

[0053] This is the material cyclic degradation coefficient (the negative sign indicates that the degradation decreases with increasing cycle number), derived from regression analysis of experimental data on paraffin-based phase change materials, with values ​​ranging from [value missing]. Second-rate;

[0054] The cumulative number of charge-discharge cycles of the energy storage material at time t (one "storage-discharge" cycle is counted as one cycle), is generated by the real-time counting of the control module, and is automatically incremented by 1 for each peak-valley cycle completed.

[0055] This is the material temperature decay coefficient (the negative sign indicates that the decay decreases as the overtemperature magnitude increases), derived from regression of experimental data on the material temperature gradient, and its value is... ;

[0056] The value at time t represents the highest operating temperature of the energy storage material on that day, sourced from the temperature sensor built into the energy storage module (accuracy). );

[0057] The rated phase change temperature of the energy storage material is a parameter provided by the material manufacturer (e.g., the value for paraffin-based materials). ).

[0058] For calculating the equipment attenuation coefficient, taking the air source heat pump in the associated equipment as an example, the formula is:

[0059]

[0060]

[0061] In the above two formulas:

[0062] For Caputo fractional derivative operators, (Equipment memory coefficient), derived from fitting 500 days of air source heat pump operation data, with a value of 0.5, because the equipment's degradation memory is weaker than that of the material;

[0063] The real-time energy efficiency of the air source heat pump at time t (unitless) is derived from feedback data from the heat pump controller and the solution results of the first equation above.

[0064] This is the equipment's time-related degradation coefficient (the negative sign indicates that the COP decreases with increasing usage time), derived from regression analysis of long-term operation experiments of air source heat pumps, with values... sky;

[0065] The cumulative usage time of the air source heat pump at time t (in days) is derived from the real-time timing of the control module starting from the date the equipment was put into use.

[0066] The coefficient of performance (COP) is the temperature decay factor of the equipment (the negative sign indicates that the COP decreases as the average operating temperature increases). It originates from the regression analysis of the effects of heat pump temperature on the equipment, and its value is... ;

[0067] The average daily operating temperature of the air source heat pump at time t (unit: The data source is the average value of data collected every 15 minutes by the heat pump temperature sensor.

[0068] This refers to the initial energy efficiency (unitless) of the air source heat pump, sourced from standard operating parameters provided by the equipment manufacturer (such as standard...). (Value 3.0 under operating conditions).

[0069] The attenuation coefficient of the associated device at time t The result is derived from the calculation in the second formula above and is updated in real time as the equipment operates.

[0070] The basic parameters of the energy storage material include the number of heat storage / release cycles, the highest historical operating temperature of the material, and the rated attenuation baseline value of the material. The basic parameters of the associated equipment include the cumulative usage time of the equipment, the historical operating temperature of the equipment, and the rated attenuation baseline value of the equipment. When the control module performs weighted coupling of the material attenuation coefficient and the equipment attenuation coefficient, it first sets the material attenuation weight and the equipment attenuation weight according to the degree of influence of the energy storage material and the associated equipment on the energy storage efficiency, respectively. Then, it multiplies the material attenuation coefficient by the material attenuation weight to obtain the material attenuation contribution value, multiplies the equipment attenuation coefficient by the equipment attenuation weight to obtain the equipment attenuation contribution value, and finally sums the material attenuation contribution value and the equipment attenuation contribution value to obtain the dual attenuation coefficient. The core formula of the weighted coupling is:

[0071]

[0072] In the formula:

[0073] The system's double attenuation coefficient at time t ( The source is the calculation result of the above formula;

[0074] The material attenuation weight is derived from engineering experience and experimental verification (due to material attenuation). This will lead to a decrease in system efficiency. equipment degradation It only leads to a decrease in efficiency Therefore, the value is 0.6).

[0075] The equipment attenuation weight is derived from a complementary setting to the material attenuation weight. ), with a value of 0.4;

[0076] The definition and source are the same as described above.

[0077] Meanwhile, the process of the control module acquiring data includes the control module acquiring external grid signals through the grid interface module. The external grid signals include peak-valley electricity price time period division, real-time electricity price data, and demand response subsidy signals. Based on the external grid signals, the control module adjusts the trigger threshold and optimization priority of thermal storage / heat release control. The control module incorporates the adjusted trigger threshold and optimization priority into the multi-cycle optimization logic for subsequent generation of thermal storage / heat release control commands. The multi-cycle optimization logic uses "7 days as a basic optimization unit" and combines monthly seasonal variation trends. The adjustment of its trigger threshold needs to match the peak and valley periods of the power grid. For example, when the peak and valley periods of the power grid are "valley power 23:00-7:00 and peak power 9:00-17:00", the charging trigger threshold is set to "10 minutes before the start of the valley period" and the energy release trigger threshold is set to "5 minutes before the start of the peak period". The optimization priority is dynamically adjusted according to the demand response subsidy signal. When a DR subsidy signal is obtained (such as a 0.5 yuan / kWh reduction reward), the priority of "participating in demand response" is raised above "basic peak and valley arbitrage" to ensure that the subsidy benefits are obtained first under the premise of meeting the load demand.

[0078] S2: The control module generates heat storage / heat release control commands for the energy storage module based on the dual attenuation coefficients and multi-cycle optimization targets.

[0079] In this step, the process of the control module generating thermal storage / heat release control commands includes the control module acquiring load signals and dual-attenuation signals. The control module then uses a multi-scale signal decomposition algorithm to process the load signals and dual-attenuation signals, separating celestial, weekly, and monthly signal components. Based on the characteristics of each scale signal component, the control module generates corresponding sub-control logic for each scale. Finally, the control module fuses the sub-control logic at each scale to generate the final thermal storage / heat release control commands. The multi-scale signal decomposition algorithm used here is wavelet transform (Daubechies-4 wavelet basis). This algorithm can decompose the original signal into components of different time scales through multi-resolution analysis, avoiding the problem that single-scale analysis cannot capture multi-period fluctuations. Its core decomposition formula is:

[0080]

[0081] In the formula:

[0082] The signal component at time t with scale s ( Corresponding to the celestial scale, Corresponding to the periodic scale (Corresponding to the monthly scale), the unit is kW (load signal) or no unit (double attenuation signal), and the source is the above integral calculation result;

[0083] for The original load signal or double-attenuated signal at any given time, in units of... The consistency is due to the real-time data collected by the sensor or the double attenuation coefficient calculated above;

[0084] It is a wavelet basis function with an s-scale (Daubechies-4 wavelet), which is dimensionless and comes from the standard function library of wavelet analysis. Its support length is 8, which can balance decomposition smoothness and detail preservation.

[0085] The integral variable (time) is in hours and is derived from the time range of signal acquisition (e.g., data from the last 30 days).

[0086] After performing multi-scale decomposition on the load signal and the dual-attenuation signal, the control module calculates the energy proportion of each signal component. Based on the characteristics of the celestial-scale signal component, the control module optimizes the intraday heat storage / release rhythm; based on the characteristics of the weekly-scale signal component, it adapts to intraweekly load fluctuations; and based on the characteristics of the monthly-scale signal component, it optimizes the long-term attenuation compensation strategy. The energy proportion is used to quantify the influence weight of each scale signal on the total signal. The core calculation formula is as follows:

[0087]

[0088] In the formula:

[0089] The energy proportion of the s-scale signal component ,and Unitless, sourced from the above integral calculation results;

[0090] The calculation period (unit: h) is derived from the multi-period optimization objective setting (e.g., (i.e., 1 week)

[0091] The definition and source are the same as described above.

[0092] Specifically, the proportion of energy in the signal components at the 24-hour scale (Indicating that intraday fluctuations have the greatest impact on total load), optimize the intraday heat storage / release rhythm—such as initiating energy release one hour before the intraday load peak and supplementing energy charging during load troughs; the energy proportion of the signal component on a weekly scale. In this case, it adapts to intra-weekly fluctuations—such as a decrease in workload on weekends compared to weekdays. The charging capacity will be reduced accordingly on weekends; the energy proportion of the signal component at the monthly scale. At the same time, optimize long-term attenuation compensation—such as monthly average attenuation. Then increase monthly The energy redundancy.

[0093] The process of generating thermal storage / heat release control commands by the control module also includes constructing a fractional-order stochastic optimal control model. The control module acquires real-time electricity price data and real-time load data, extracts electricity price fluctuation characteristics and load fluctuation characteristics, and transforms these fluctuation characteristics into random disturbance parameters, which are then incorporated into the fractional-order stochastic optimal control model. The control module adjusts the thermal storage power and heat release rate of the energy storage module in real time by solving the continuous optimization equations corresponding to the fractional-order stochastic optimal control model. Based on the adjusted thermal storage power and heat release rate, the control module refines the thermal storage / heat release control commands. The random disturbance parameters are extracted through historical data statistics. Taking electricity price fluctuations as an example, their random characteristics are described by Brownian motion, and the core parameter formula is:

[0094]

[0095] In the formula:

[0096] The random increment of the electricity price at time t is expressed in yuan / (kWh), and its source is the differential description of the electricity price fluctuation.

[0097] The value is the electricity price drift coefficient (mean), expressed in yuan / (kWh•h). It is derived from linear regression of real-time electricity price data over the past year and has a value of 0.01, indicating a slight daily increase in electricity prices.

[0098] The electricity price diffusion coefficient (standard deviation) is expressed in yuan / kWh. It is calculated from the standard deviation of electricity price data over the past year and has a value of 0.05, indicating the daily fluctuation range of electricity prices.

[0099] The time element is a time microelement, with the unit being hours (h), and its source is the control cycle setting (e.g., ...). );

[0100] This represents the increment of the standard Wiener process (Brownian motion), is dimensionless, and originates from stochastic process theory. It has a mean of 0 and a variance of dt, and is used to describe stochastic disturbances in electricity prices. The extraction of stochastic parameters for load fluctuations is similar to that for electricity prices, using the following formula: ,in (Load drift coefficient) value The load diffusion coefficient is set to 2kW, and all values ​​are derived from the load data statistics of the past 3 years.

[0101] When constructing a fractional-order stochastic optimal control model, the control module aims to minimize the long-term expected cost. It constructs an objective function that includes an energy charging cost term and an efficiency fluctuation penalty term. Based on fractional calculus principles, the control module builds a continuous mathematical model of the system state evolution to describe the dynamic relationship between the energy storage module capacity, dual decay coefficients, and stochastic disturbance parameters. While solving the continuous optimization equations, the control module outputs the thermal power and heat release rate adapted to stochastic disturbances in real time. The core formula of the objective function (long-term expected cost) is:

[0102]

[0103] In the formula:

[0104] The long-term expected cost is expressed in yuan and is derived from the expected integral calculation. The model objective is to minimize this cost. ;

[0105] It is a mathematical expectation operator, without units, derived from probability and statistics theory, and used to deal with the uncertainty of random perturbations;

[0106] Discount factor Unitless, sourced from industry standards (taken as 0.05), used to balance the weights of current and future costs;

[0107] The time is expressed in hours (h) and is derived from system runtime.

[0108] The energy cost at time t is expressed in yuan, and its source is... (in (This refers to the real-time energy efficiency of the cold and heat sources, which is derived from equipment feedback.)

[0109] The thermal storage power at time t (a control variable), in kW, is derived from the model solution results, and the constraint range is... Forehead Forehead (This refers to the rated power of the heat source / cold source).

[0110] This is the efficiency fluctuation penalty coefficient, in yuan. The value is derived from engineering experience (taken as 100) and is used to suppress large fluctuations in efficiency.

[0111] The system real-time efficiency at time t is unitless and sourced from [source missing]. ;

[0112] The system's rated efficiency is dimensionless and derived from industry standards (taken as 0.85). The continuous mathematical model of the system's state evolution is constructed based on fractional calculus, with energy storage capacity and dual decay coefficients as the core state variables. The formula is:

[0113]

[0114] In the formula:

[0115] Caputo fractional derivative operator Unitless, sourced from the fractional-order model mentioned earlier;

[0116] The energy storage capacity at time t is expressed in kWh and is derived from the model solution.

[0117] The charging-deceleration coupling coefficient is expressed in units of 100 kJ / m². The source is experimental regression (taking...) ), used to describe the accelerating effect of thermal energy storage power on decay;

[0118] The definitions and sources are the same as described above. The continuous optimization equation corresponding to the above fractional-order stochastic optimal control model is the Hamilton-Jacobi-Bellman (HJB) equation, whose core form is:

[0119]

[0120] In the formula:

[0121] Value function (state at time t) The minimum expected cost (in yuan) is derived from the solution of the HJB equation.

[0122] The discount factor is from the same source as mentioned above;

[0123] It is an infinitesimal generator, without units, and originates from the theory of stochastic processes. It is used to describe the influence of the stochastic evolution of state variables on the value function.

[0124] This is the system state vector, and the definitions and sources of each component are the same as those mentioned above;

[0125] The definitions and sources of the remaining parameters are the same as those in the previous text.

[0126] The control module solves the HJB equation using numerical methods (such as the finite difference method) and outputs the thermal storage power in real time to adapt to random disturbances. The heat release rate (heat release rate = load demand - real-time thermal storage power) ensures that the long-term expected cost minimization objective is still met under random fluctuations.

[0127] S3: The control module sends the heat storage / heat release control command to the energy storage module, the cold and heat source module and the hydraulic conversion module to control the heat storage / heat release process of the energy storage module.

[0128] In this step, the transmission of heat storage / release control commands adopts a standardized industrial communication protocol (Modbus-RTU) to ensure that the command transmission delay is ≤100ms, meeting the real-time control requirements. The command content includes the heat storage power setpoint, heat release rate threshold, start-stop time nodes, and safety protection parameters (such as upper and lower limits of energy storage capacity). After receiving the command, each module parses and executes it through its built-in controller. The energy storage module adjusts the opening of the internal heat exchange valve according to the command to control the energy storage / release rate. The cold and heat source module adjusts the compressor frequency or electric heating power according to the command to match the energy charging requirements. The hydraulic conversion module controls the energy transmission flow rate by adjusting the speed of the variable frequency water pump. The three work together to achieve precise control of the heat storage / release process of the energy storage module.

[0129] When the control module controls the heat storage / release process of the energy storage module, it also includes acquiring the current ambient temperature and the type of the terminal load. Based on the current ambient temperature, the control module adjusts the operating parameters of the cold and heat source modules. Based on the type of the terminal load, the control module adjusts the heat storage / release rate and energy storage capacity adaptation logic of the energy storage module. Based on the above adjustments, the control module corrects the heat storage / release control commands and sends them to each module. The current ambient temperature is acquired by a platinum resistance temperature sensor in the climate environment monitoring module (accuracy ±0.2℃, sampling frequency 1 time / 5min). The type of terminal load is determined by the load characteristic identification unit of the terminal adaptation module (e.g., building heating / cooling, industrial process energy consumption, identified based on load duration and power fluctuation range). The core logic of adjusting the operating parameters of the cold and heat source based on the ambient temperature, taking an air source heat pump as an example, has the following fitting formula for its energy efficiency as a function of ambient temperature:

[0130]

[0131] In the formula:

[0132] For ambient temperature The real-time energy efficiency of the air source heat pump is shown below, without units, and is derived from the calculation results of the above formula.

[0133] This refers to the rated energy efficiency of the heat pump; it has no unit and is sourced from the equipment manufacturer's parameters (e.g., ...). Corresponding to the rated ambient temperature

[0134] This is the temperature influence coefficient, in units of... The value is derived from the low-temperature performance test regression of the heat pump (0.04 indicates that for every increase in ambient temperature...). promote );

[0135] The current ambient temperature, in units of The data source is temperature sensor data.

[0136] The rated ambient temperature of the heat pump, in units of The data comes from the equipment manufacturer's parameters.

[0137] The control module is based on this formula, when the ambient temperature... (Below the rated temperature) Calculated To maintain the same heat storage capacity, the heat pump compressor frequency needs to be adjusted. The rated frequency is increased from 50Hz to 75Hz to compensate for the insufficient output caused by the decrease in energy efficiency. The frequency adjustment formula is:

[0138] In the formula:

[0139] The adjusted compressor frequency is in Hz and is derived from the calculation results of the above formula.

[0140] The compressor's rated frequency is in Hz and is sourced from the equipment manufacturer's parameters (value 50Hz).

[0141] The definition and source are the same as described above.

[0142] When adjusting the heat storage / release rate and energy storage capacity adaptation logic based on the end-load type, a differentiated strategy is adopted for different load characteristics: when the end-load type is building heating (intraday load fluctuation) Duration The formula for adjusting the heat storage / heat release rate (day) is:

[0143] In the formula:

[0144] The adjusted heat storage / release rate, in units of The source is the calculation result of the above formula;

[0145] Rated heat storage / release rate, in units of The source is the system design parameters (values) );

[0146] This is the load fluctuation factor, which has no unit and is derived from the calculation of the deviation between the real-time load and the rated load (e.g., the real-time load is higher than the rated load). ,but Meanwhile, the energy storage capacity adaptation logic is set to "daily charging capacity". "Daily heating load peak", reserved Redundancy is needed to handle load fluctuations; when the end load type is industrial process energy consumption (load fluctuations) Duration When the heat storage / release rate is maintained at the rated value (to avoid frequent adjustments affecting process stability), the energy storage capacity adaptation logic is set to "charging capacity". "Average process load" ensures continuous and uninterrupted power supply.

[0147] The control module takes the results of adjusting the cold and heat source parameters caused by ambient temperature and adjusting the heat storage / release rate and capacity caused by the end load type, and substitutes them into the initial heat storage / release control command generated in S2 above. After correcting the power setpoint, frequency parameter, and capacity threshold in the command, it sends it back to each module through the communication link, forming a closed-loop control of "command generation - dynamic correction - execution feedback". For example, the heat storage power is set to 10kW in the initial command, which is increased to 12.5kW after ambient temperature correction, and further adjusted to 13.75kW after load fluctuation correction. In the end, it ensures that the heat storage / release process of the energy storage module is both adapted to environmental changes and meets the end energy demand.

[0148] The control module acquires the basic parameters of energy storage materials and associated equipment, and uses the Caputo fractional calculus model to calculate the attenuation coefficients of materials and equipment respectively, and then weighted and coupled them to obtain a dual attenuation coefficient. Because of its historical memory characteristics, this model can accurately capture the cumulative effect of attenuation, avoiding the problem of traditional linear models underestimating long-term attenuation, and thus providing a reliable quantitative basis for attenuation control of thermal storage / heat release. At the same time, combined with external signals such as peak and valley electricity prices and demand response acquired by the grid interface module, the module adjusts the trigger threshold and priority of multi-cycle optimization logic to make the control objectives more in line with the grid economic policy and long-term operation requirements.

[0149] Based on the dual attenuation coefficient and multi-period optimization objective, the control module adopts the Daubechies-4 wavelet basis multi-scale signal decomposition algorithm to separate the load and dual attenuation signal into daily, weekly, and monthly scale components and calculate the energy proportion of each component. Based on the proportion, the influence weight of different period fluctuations on the system is quantified, and sub-control logics of each scale are generated in a targeted manner. This can effectively avoid the strategy mismatch problem that single-scale control cannot adapt to multi-period fluctuations. Furthermore, a fractional-order stochastic optimal control model including energy charging cost and efficiency fluctuation penalty terms is constructed. The random fluctuations of electricity price and load are transformed into model disturbance parameters through Brownian motion. Combined with the fractional-order system state evolution equation, the thermal storage power and heat release rate are adjusted in real time by solving the HJB continuous optimization equation, so that the control strategy can dynamically respond to random disturbances and take into account both long-term expected cost minimization and efficiency stability.

[0150] During the heat storage / release command execution phase, the control module acquires the ambient temperature and end-load type in real time. Based on the ambient temperature, it adjusts the operating parameters of the cold and heat sources (such as the frequency of the air source heat pump compressor) using an energy efficiency fitting formula to compensate for energy efficiency degradation at different temperatures and ensure stable energy charging capacity under wide temperature environments. For different load types such as buildings and industries, it adjusts the heat storage / release rate or sets capacity redundancy through the load fluctuation coefficient to adapt to differentiated energy needs. Finally, the dynamic adjustment results are used to correct the initial heat storage / release command and sent to each module, forming a closed-loop control of "data acquisition - model calculation - strategy generation - dynamic correction - execution feedback". Through multi-dimensional collaboration in mathematical modeling, multi-scale optimization, stochastic response, and environmental and load adaptation, the energy storage system achieves the technical effects of long-term energy cost optimization, stable energy efficiency, and multi-scenario compatibility.

[0151] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0152] In summary, this application has at least the following beneficial effects:

[0153] 1. Improve the accuracy of attenuation quantification and the reliability of long-term control. Caputo fractional calculus model is used to capture the historical memory of energy storage material and equipment attenuation. Combined with weighted coupling to calculate dual attenuation coefficients, the problem of underestimating cumulative attenuation in traditional linear models is avoided. This provides accurate data support for the formulation of thermal storage / heat release strategies and ensures the stability of efficiency and capacity during long-term system operation.

[0154] 2. Enhance multi-scenario adaptability and random disturbance response capabilities. On the one hand, use multi-scale signal decomposition algorithms to separate load and attenuation fluctuations at the daily, weekly, and monthly scales, and generate targeted sub-control logic. On the other hand, convert random fluctuations in electricity prices and loads into model disturbance parameters. Through continuous optimization of the fractional-order stochastic optimal control model, the strategy can dynamically adapt to changes in grid policies, climate environment, and end-load types, reducing the risk of strategy mismatch.

[0155] 3. Achieve synergistic optimization of long-term economic efficiency and efficiency stability. Using an objective function that includes charging costs and efficiency fluctuation penalties as the core, and combining multi-cycle optimization logic with demand response signal priority adjustment, this approach reduces long-term multi-cycle energy consumption costs while suppressing significant efficiency fluctuations through an efficiency fluctuation penalty mechanism, thus balancing the economic efficiency and technical stability of system operation.

[0156] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A smart control method for an energy storage system, characterized in that, include: The control module obtains the dual attenuation coefficients and multi-cycle optimization targets of the energy storage module; The control module generates heat storage / heat release control commands for the energy storage module based on the dual attenuation coefficients and multi-cycle optimization targets. The control module sends the heat storage / heat release control command to the energy storage module, the cold and heat source module and the hydraulic conversion module to control the heat storage / heat release process of the energy storage module. The heat storage process is to use the cold and heat source module to heat the energy storage material during off-peak hours and store it in the energy storage module. The heat release process is to release the heat of the energy storage material during peak hours to reduce the power consumption of the grid. The dual attenuation coefficient characterizes the cumulative attenuation of energy storage materials and associated equipment in the energy storage module, and the multi-cycle optimization objectives include minimizing long-term multi-cycle energy consumption costs and stabilizing energy efficiency throughout the entire cycle.

2. The intelligent control method according to claim 1, characterized in that, The process by which the control module obtains the dual attenuation coefficients includes: The control module acquires the basic parameters of the energy storage material and the basic parameters of the associated equipment; The control module uses a fractional-order calculus model to calculate the material attenuation coefficient based on the basic parameters of the energy storage material. The control module uses a fractional calculus model to calculate the device attenuation coefficient based on the basic parameters of the associated device; The control module performs weighted coupling of the material attenuation coefficient and the equipment attenuation coefficient to obtain a dual attenuation coefficient.

3. The intelligent control method according to claim 2, characterized in that, The basic parameters of the energy storage material include the number of heat storage / heat release cycles, the highest historical operating temperature of the material, and the rated attenuation benchmark value of the material. The basic parameters of the associated equipment include the cumulative usage time of the equipment, the historical operating temperature of the equipment, and the rated attenuation benchmark value of the equipment. When the control module performs weighted coupling of the material attenuation coefficient and the equipment attenuation coefficient, it first sets the material attenuation weight and the equipment attenuation weight according to the degree of influence of the energy storage material and the associated equipment on the energy storage efficiency. Then, it multiplies the material attenuation coefficient with the material attenuation weight to obtain the material attenuation contribution value, multiplies the equipment attenuation coefficient with the equipment attenuation weight to obtain the equipment attenuation contribution value, and finally sums the material attenuation contribution value and the equipment attenuation contribution value to obtain the dual attenuation coefficient.

4. The intelligent control method according to claim 1, characterized in that, The process by which the control module generates heat storage / heat release control commands includes: The control module acquires the load signal and the double-attenuated signal; The control module uses a multi-scale signal decomposition algorithm to process the load signal and the double-attenuated signal, separating the daily, weekly, and monthly signal components. Based on the characteristics of signal components at each scale, the control module generates sub-control logic for the corresponding scale. The control module integrates the control logic of each scale to generate the final heat storage / heat release control command.

5. The intelligent control method according to claim 4, characterized in that, After performing multi-scale decomposition on the load signal and the double-attenuated signal, the control module calculates the energy proportion of each signal component. The control module optimizes the intraday heat storage / release rhythm based on the characteristics of the celestial signal components, adapts to intraweek load fluctuations based on the characteristics of the weekly signal components, and optimizes the long-term attenuation compensation strategy based on the characteristics of the monthly signal components.

6. The intelligent control method according to claim 1, characterized in that, The process of the control module generating heat storage / heat release control commands also includes: The control module constructs a fractional-order stochastic optimal control model; The control module acquires real-time electricity price data and real-time load data, extracts electricity price fluctuation characteristics and load fluctuation characteristics, and converts the fluctuation characteristics into random disturbance parameters and incorporates them into the fractional-order stochastic optimal control model. The control module adjusts the thermal storage power and heat release rate of the energy storage module in real time by solving the continuous optimization equation corresponding to the fractional-order stochastic optimal control model. The control module refines the heat storage / heat release control commands based on the adjusted heat storage power and heat release rate.

7. The intelligent control method according to claim 6, characterized in that, When constructing a fractional-order stochastic optimal control model, the control module aims to minimize the long-term expected cost and constructs an objective function that includes an energy cost term and an efficiency fluctuation penalty term. The control module is based on the principle of fractional calculus to construct a continuous mathematical model of the system state evolution, which is used to describe the dynamic relationship between the energy storage module capacity, the dual attenuation coefficient and the random disturbance parameters. When the control module solves the continuous optimization equation, it outputs the thermal storage power and heat release rate in real time to adapt to random disturbances.

8. The intelligent control method according to claim 1, characterized in that, The process of the control module acquiring data includes: The control module acquires external grid signals through the grid interface module. These external grid signals include peak-valley electricity price time periods, real-time electricity price data, and demand response subsidy signals. Based on the external power grid signal, the control module adjusts the trigger threshold and optimization priority of the thermal storage / heat release control; The control module incorporates the adjusted trigger threshold and optimization priority into the multi-cycle optimization logic for the generation of subsequent heat storage / heat release control commands.

9. The intelligent control method according to claim 1, characterized in that, When the control module controls the heat storage / heat release process of the energy storage module, it also includes: The control module obtains the current ambient temperature and the type of terminal load; The control module adjusts the operating parameters of the cold and heat source modules based on the current ambient temperature. The control module adjusts the heat storage / release rate and energy storage capacity adaptation logic of the energy storage module based on the end load type. Based on the above adjustments, the control module corrects the heat storage / heat release control commands and sends them to each module.

10. An energy storage system, characterized in that, It includes energy storage modules, cold and heat source modules, hydraulic conversion modules, control modules, grid interface modules, and end-point adapter modules; The energy storage module is configured to store heat using energy storage material as the energy storage medium, reflect the energy storage capacity through the temperature change of the energy storage material, and output decay state parameters including the temperature of the energy storage material, the state of the energy storage material, and the number of cycles of the energy storage material to the control module; The cold or heat source module is configured to provide the cold or heat energy required for energy storage and output real-time energy efficiency parameters and operating temperature parameters to the control module. The hydraulic conversion module is configured to transmit cold or heat energy and output transmission loss parameters and cycle number parameters to the control module. The power grid interface module is configured to acquire external power grid signals and transmit the external power grid signals to the control module; The terminal adapter module is configured to connect to different types of terminal loads and output real-time load parameters to the control module; The control module is configured to execute the intelligent control method according to any one of claims 1-9, receive parameters and signals output by each module, and generate and send heat storage / heat release control commands to the energy storage module, the cold and heat source module and the hydraulic conversion module.

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