A micro-grid hybrid energy storage power distribution method for coping with intermittent new energy
By establishing a real-time lifetime stress perception model and a dynamic frequency urgency coefficient, and constructing a dual-objective dynamic reconfiguration cost function, the conflict between frequency stability and energy storage lifetime in islanded microgrids is resolved, achieving a dynamic balance between frequency security and energy storage lifetime, and improving the stability and economy of the microgrid.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SOUTHERN XINJIANG ELECTRICITY SUPPLY COMPANY OF STATE GRID XINJIANG ELECTRIC POWER
- Filing Date
- 2026-02-27
- Publication Date
- 2026-05-29
AI Technical Summary
Existing technologies have failed to effectively reconcile the conflict between frequency stability and energy storage lifetime in isolated microgrids. In particular, the lack of intelligent trade-off mechanisms during frequency crises leads to a shortened lifespan of energy storage systems and affects the long-term economic viability of microgrids.
A real-time lifetime stress perception model for energy storage units is established. A dual-objective dynamic reconfiguration cost function is constructed by combining the dynamic frequency urgency coefficient. The problem of minimization is solved through online optimization to achieve a dynamic balance between frequency deviation and lifetime loss.
It achieves an adaptive balance between frequency security and energy storage lifespan, protecting energy storage lifespan during normal times and quickly stabilizing frequency in critical moments, thus improving the stability and economy of microgrids.
Smart Images
Figure CN122118876A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of microgrid control technology, and more specifically, to a method for power allocation of hybrid energy storage in microgrids to cope with intermittent new energy sources. Background Technology
[0002] The penetration rate of intermittent renewable energy sources, such as wind and solar power, in isolated microgrids is increasing. While bringing clean energy, the strong randomness and volatility of their output pose serious challenges to the safe and stable operation of isolated systems. Isolated microgrids lack the support of a large power grid, have low system inertia, and are extremely sensitive to power imbalances; frequency stability is the lifeline for their safe operation. To mitigate renewable energy fluctuations and maintain real-time power balance, configuring hybrid energy storage systems consisting of power-type energy storage (such as supercapacitors) and energy-type energy storage (such as lithium-ion batteries) has become a key technological approach. The power allocation strategy determines how the two types of energy storage work together to respond to power shortages or surpluses, which is the core of leveraging the advantages of hybrid energy storage.
[0003] Currently, power allocation methods in this field mainly focus on how to quickly and accurately compensate for power discrepancies to stabilize the frequency, typically employing strategies based on filter decomposition or instantaneous power point tracking. However, these methods have a fundamental limitation: they treat frequency stability as the sole or primary optimization objective, neglecting the rapid lifespan degradation of energy storage units, especially energy-type energy storage, under frequent charging and discharging, particularly high-rate emergency power support.
[0004] In isolated microgrids with low inertia, facing extreme conditions of "power cliff" from renewable energy sources, the control system instinctively commands energy storage to discharge at maximum capacity as a form of "punitive" discharge to prevent frequency collapse. While this control logic maintains instantaneous stability, it causes irreversible damage to expensive energy storage assets. Several such extreme events can significantly shorten the lifespan of the energy storage system, greatly impairing the long-term economic viability of the microgrid. Existing technologies have failed to effectively reconcile the two equally important but inherently conflicting goals of "ensuring instantaneous frequency security" and "extending the entire lifespan of energy storage," especially during frequency crises, lacking a mechanism that can intelligently weigh and dynamically adjust the focus of optimization based on the real-time safety status of the system. Therefore, an innovative power allocation method is urgently needed to establish an adaptive balance between frequency security and energy storage lifespan, achieving synergistic optimization of microgrid stability and economy. In light of this, we propose a hybrid energy storage power allocation method for microgrids dealing with intermittent renewable energy sources. Summary of the Invention
[0005] The purpose of this invention is to provide a method for power allocation of hybrid energy storage in microgrids to cope with intermittent new energy sources, so as to solve the technical problem that frequency stability and energy storage life cannot be simultaneously achieved when hybrid energy storage in islanded microgrids is used to smooth out the fluctuations of new energy sources.
[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a microgrid hybrid energy storage power allocation method for intermittent renewable energy sources, comprising the following steps: S1. Establish a real-time lifetime stress perception model for energy storage units, which is used to calculate the equivalent lifetime loss power under the current operating conditions based on the real-time operating physical parameters of the energy storage units. S2. The system frequency of the microgrid is acquired in real time, and a dynamic frequency urgency coefficient representing the frequency urgency is calculated based on the deviation of the system frequency from the preset frequency warning boundary and frequency danger boundary. The microgrid is an islanded microgrid containing intermittent new energy generation units, loads and hybrid energy storage systems. The hybrid energy storage system includes power-type energy storage units and energy-type energy storage units. S3. Construct a dynamic reconfiguration cost function for power allocation optimization. The cost function is expressed as: ; in, for The value of a moment For system frequency deviation, This is the equivalent lifetime power loss. This is the dynamic frequency urgency coefficient. and They are respectively about The weight function, and for A monotonically increasing function. for A monotonically decreasing function; S4. Taking the total power demand of the hybrid energy storage system, the real-time state of charge of the energy-type energy storage unit, the output of the real-time lifetime stress perception model, and the dynamic frequency urgency coefficient as inputs, solve the problem of minimizing the dynamic reconfiguration cost function in each control cycle, while satisfying the power and capacity constraints of the power-type energy storage unit and the energy-type energy storage unit, and obtain the optimal power allocation command for the two in the current control cycle and issue it for execution.
[0007] Preferably, the establishment of a real-time lifetime stress sensing model for the energy storage unit specifically includes: The physical parameters of the energy storage unit, including operating current, internal temperature, and terminal voltage, are collected in real time. The comparison and rate of change between the operating current and the rated value, the deviation and rate of change between the internal temperature and the reference value, and the closeness of the terminal voltage to the healthy operating voltage range are used as input stress factors. By using a simplified empirical model that couples electrochemical reaction kinetics and thermodynamic effects, the input stress factor is calculated and the equivalent lifetime loss power is output. The fusion calculation is achieved through the following formula: ; ; ; ; in, , , These are the current stress factor, temperature stress factor, and voltage stress factor, respectively. , , They are respectively The operating current, internal temperature, and terminal voltage at any given time; , They are respectively The rate of change of operating current and internal temperature at all times; The duration for which the current exceeds the rated threshold. This represents the deviation of the temperature from the reference value. This is the minimum distance between the terminal voltage and the healthy operating voltage boundary; , , This is a preset factor calculation function; This is the stress fusion function; This is the model parameter set.
[0008] Preferably, the input stress factor takes into account the instantaneous amplitude of the operating current, the duration of time it remains above the rated threshold, and the frequency of charging and discharging direction switching.
[0009] Preferably, the input stress factor takes into account internal temperature, including overheating conditions beyond the optimal operating temperature range and rapid temperature gradients per unit time.
[0010] Preferably, the calculation of the dynamic frequency urgency coefficient specifically includes: Set frequency warning boundaries With frequency danger boundary ; Dynamic frequency urgency coefficient according to System frequency at time It is calculated using the following piecewise function: ; in, For definition in A monotonically increasing mapping function over an interval, satisfying... , .
[0011] Preferably, the frequency warning boundary and the frequency danger boundary are set according to the inertia characteristics, load characteristics and the tolerance of key equipment of the microgrid, and the frequency danger boundary corresponds to the critical value that triggers the low-frequency or high-frequency protection action of the microgrid.
[0012] Preferably, the weighting function and The design meets the following requirements: when Approaching hour, The value of makes the lifetime loss power penalty term affect the cost. China is dominant; when Approaching hour, The value of makes the frequency deviation penalty term affect the cost. China holds an absolutely dominant position.
[0013] Preferably, solving the problem of minimizing the dynamic reconstruction cost function specifically includes: The current equivalent lifetime loss power and current state of charge of the energy-type energy storage unit, and the current state of charge of the power-type energy storage unit are used as state variables. The power command of the power-type energy storage unit and the energy-type energy storage unit , As a decision variable; In each control cycle Based on the current state and the dynamic frequency urgency coefficient Solve the following constrained optimization problem:
[0014] The constraints are:
[0015]
[0016]
[0017]
[0018] in, For the first Power commands for power-type energy storage units within a control cycle; For the first Power commands for energy storage units within a control cycle; , These are the minimum and maximum charge / discharge power limits for power-type energy storage units; , These are the minimum and maximum charge / discharge power limits for energy storage units. For the first The initial state of charge of the power-type energy storage unit in each control cycle; For the first The initial state of charge of the energy storage unit in each control cycle; , These are the minimum and maximum state of charge limits for power-type energy storage units; , These are the minimum and maximum state of charge limits for energy storage units. The charging and discharging efficiency of power-type energy storage units; The charging and discharging efficiency of energy storage units; This refers to the rated energy storage capacity of the power-type energy storage unit; This refers to the rated energy storage capacity of the energy-type energy storage unit; To control the cycle length and optimize the time step of the calculation.
[0019] Preferably, the method further includes a step of closed-loop correction of the health status of the energy storage unit: The integral of the equivalent lifetime power loss over time is accumulated and used to update the estimated health status value of the energy storage unit online. The updated formula is: ; in, for Estimated health status value of the instantaneous energy storage unit; This represents the initial health status value of the energy storage unit. This is the cumulative loss factor; From the initial moment to Time integral of equivalent lifetime power loss at any given moment; for Equivalent lifetime power loss at any given moment.
[0020] Preferably, the parameters in the real-time lifetime stress sensing model are periodically corrected using the offline detection data of the energy storage unit. The updated estimated health status value is used as feedback to adjust the basic lifetime loss sensitivity of the energy storage unit in subsequent optimization calculations.
[0021] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs a dual-objective dynamic reconfiguration cost function that includes frequency deviation penalties and lifetime loss penalties. The core of this invention lies in the fact that the weights of the two penalties in the function are not fixed, but dynamically adjusted by a real-time calculated "dynamic frequency urgency coefficient." When the system frequency is normal, the algorithm tends to assign a higher weight to the lifetime loss penalty, guiding power allocation to prioritize a "mild" strategy for energy storage and maximizing lifetime extension. Once the frequency deviates and approaches a dangerous boundary, the frequency urgency coefficient increases, the cost function is automatically reconfigured, the weight of the frequency deviation penalty rises sharply, and the algorithm is authorized to instruct the energy storage to withstand higher losses to quickly stabilize the frequency. This intelligent weight switching mechanism based on the real-time safety status of the system enables the power allocation strategy to have scenario-adaptive decision-making capabilities. It can both "carefully calculate" to protect energy storage during normal times and "decisively and resolutely" ensure safety in critical moments, thus achieving an organic unity and dynamic optimization of the two core objectives of stability and economy at the system level.
[0022] 2. This invention also establishes a real-time lifetime stress sensing model based on multi-dimensional physical parameters such as operating current, internal temperature, and terminal voltage. This model, through a specific factor calculation function, transforms dynamic operating condition characteristics such as current amplitude, rate of change, overcurrent duration, temperature deviation, temperature gradient, and voltage safety margin into corresponding current, temperature, and voltage stress factors. Then, through a stress fusion function, it comprehensively calculates the "equivalent lifetime loss power" under the current operating condition. This model combines electrochemical and thermodynamic effects with real-time operating data, enabling dynamic capture of the combined effects of different stress conditions on lifetime. This allows lifetime assessment to move from static, offline to dynamic, online, and from single-parameter assessment to multi-physics field coupled assessment, providing accurate and reliable lifetime loss feedback input for the cost function. It is a key technological foundation for achieving intelligent balance decision-making.
[0023] 3. This invention also incorporates a closed-loop health status correction step. By integrating and accumulating real-time calculated lifetime losses, the estimated health status value of the energy storage unit is updated online. More importantly, real-time health status data obtained from offline monitoring is periodically used to correct key parameters in the aforementioned lifetime stress perception model. This closed-loop process of "online estimation - offline calibration - feedback correction" enables the model to continuously learn and adapt to the aging trajectory and characteristic drift of the energy storage unit itself, effectively compensating for initial model errors and evaluation biases caused by performance degradation during long-term operation. This not only significantly improves the long-term accuracy of lifetime prediction, ensuring that the dynamic balancing strategy is always based on reliable data, but also endows the entire power distribution system with self-learning and self-optimization capabilities, significantly enhancing its robustness against uncertainties throughout its entire lifecycle and ensuring the sustainability of optimization effects. Attached Figure Description
[0024] Figure 1This is a schematic diagram of the overall method flow of the present invention; Figure 2 A schematic diagram illustrating the process of establishing a life stress sensing model for this invention; Figure 3 This is a schematic diagram of the process for calculating the dynamic frequency urgency coefficient of the present invention; Figure 4 This is a schematic diagram illustrating the process of constructing and solving the dynamic cost function in this invention; Figure 5 This is a schematic diagram of the closed-loop correction process for health status according to the present invention. Detailed Implementation
[0025] To facilitate understanding of the technical solution of the present invention by those skilled in the art, the technical solution of the present invention will now be further described in conjunction with the accompanying drawings.
[0026] Example 1, such as Figures 1-5 As shown, the present invention provides a microgrid hybrid energy storage power allocation method for dealing with intermittent new energy sources. The microgrid is an islanded microgrid that includes intermittent new energy generation units, loads, and a hybrid energy storage system. The hybrid energy storage system includes power-type energy storage units and energy-type energy storage units. The method includes the following steps: S1. Establish a real-time lifetime stress perception model for energy storage units. The model is used to calculate the equivalent lifetime loss power under the current operating conditions of energy storage units based on their real-time operating physical parameters. Real-time lifetime stress sensing model: This refers to a mathematical model that quantifies the degree of lifetime loss (i.e., equivalent lifetime loss power) of an energy storage unit per unit time based on its real-time operating physical parameters (current, temperature, voltage), coupled with electrochemical reaction kinetics and thermodynamic effects. Its core characteristics are "real-time performance" (calculation completed within the control cycle) and "multi-stress coupling" (integrating the multi-dimensional effects of current, temperature, and voltage), distinguishing it from traditional offline lifetime assessment models. S2. Acquire the system frequency of the microgrid in real time, and calculate the dynamic frequency urgency coefficient, which characterizes the frequency urgency level, based on the deviation of the system frequency from the preset safety boundary. Dynamic frequency urgency coefficient: This is a dimensionless coefficient (range [0,1]) used to quantify the degree of urgency of a microgrid system's frequency deviating from the safe range. Its value changes dynamically with the degree of frequency deviation from the warning boundary, providing a basis for adjusting the weight of the cost function. A larger value indicates that the frequency is closer to the critical value for protection action, and the higher the system safety risk. S3. Construct a dynamic reconfiguration cost function for power allocation optimization. The cost function includes a frequency deviation penalty term and a lifetime loss power penalty term, and the weight relationship between the two penalty terms is dynamically adjusted by the dynamic frequency urgency coefficient. Among them, the weight of the frequency deviation penalty term increases with the increase of the dynamic frequency urgency coefficient, while the weight of the lifetime loss power penalty term decreases accordingly. Dynamic reconfiguration cost function: This refers to an optimization objective function that balances the two major optimization objectives of "frequency stability" and "energy storage lifetime," and adaptively adjusts the weights of these two objectives through a dynamic frequency urgency coefficient. Its core feature is "weight dynamism," which allows for switching optimization priorities based on system frequency status, avoiding the unintended consequences of optimizing a single objective. S4. Taking the total power demand of the hybrid energy storage system, the real-time state of charge of the energy-type energy storage unit, the output of the real-time lifetime stress sensing model, and the dynamic frequency urgency coefficient as inputs, solve the problem of minimizing the dynamic reconfiguration cost function in each control cycle, while satisfying the power and capacity constraints of the power-type energy storage unit and the energy-type energy storage unit, and obtain the optimal power allocation command of the power-type energy storage unit and the energy-type energy storage unit in the current control cycle, and issue it for execution. In an embodiment of the present invention, a real-time lifetime stress sensing model for an energy storage unit is established, specifically including: The physical parameters of the energy storage unit, including its operating current, internal temperature, and terminal voltage, are collected in real time. The comparison and rate of change between the operating current and the rated value, the deviation and rate of change between the internal temperature and the reference value, and the closeness of the terminal voltage to the healthy operating voltage range are used as input stress factors. By using a simplified empirical model that couples electrochemical reaction kinetics and thermodynamic effects, the input stress factor is calculated and the equivalent lifetime loss power is output. Equivalent lifetime loss power: refers to the quantitative index (unit: kW) that equates the lifetime degradation of an energy storage unit caused by current, temperature, and voltage stress to the "power" dimension. It is used to characterize the degree of lifetime loss of an energy storage unit per unit time, so that lifetime loss can be directly incorporated into the cost function of power allocation optimization and achieve a quantitative trade-off with frequency deviation. The fusion calculation is achieved through the following formula: ; ; ; ; in, The current stress factor characterizes the degree of influence of the operating current on the lifespan of the energy storage unit; The temperature stress factor characterizes the degree of influence of internal temperature on the lifespan of the energy storage unit. The voltage stress factor characterizes the degree of influence of the terminal voltage on the lifespan of the energy storage unit. for The operating current of the energy storage unit at any given time; for The rate of change of the operating current at any given time reflects how fast the current fluctuates. The duration for which the current exceeds the rated threshold characterizes the cumulative effect of overcurrent conditions. for The deviation of the internal temperature from the reference value at any given time reflects the degree to which the temperature deviates from the optimal operating condition; for The rate of change of internal temperature at any given time characterizes how fast the temperature fluctuates. for The minimum distance between the terminal voltage and the healthy operating voltage boundary reflects the safety margin of the voltage condition; , , These are preset factor calculation functions, used to convert current, temperature, and voltage characteristics into corresponding stress factors; for The equivalent lifetime loss power calculated at any time quantifies the lifetime loss of the energy storage unit per unit time. This is a stress fusion function used to couple the three major stress factors and output the comprehensive lifetime loss power. This is the model parameter set, which contains inherent parameters related to the material and structure of the energy storage unit, and is used to calibrate the model accuracy.
[0027] This formula system achieves coupled quantification of multiple physical parameters, breaking through the limitations of evaluating lifetime loss with a single parameter. It can capture the dynamic impact of operating condition fluctuations on the lifetime of energy storage units in real time, providing accurate lifetime loss feedback for subsequent power allocation optimization. It balances the simplification of the model with the accuracy of the evaluation, and is suitable for the real-time control needs of microgrids.
[0028] In embodiments of the present invention, the input stress factor takes into account the operating current, including its instantaneous amplitude, the duration of time it remains above the rated threshold, and the frequency of charging and discharging direction switching.
[0029] In embodiments of the present invention, the input stress factor takes into account internal temperature, including overheating conditions exceeding the optimal operating temperature range and rapid temperature change gradients per unit time.
[0030] Stress factor (current / temperature / voltage stress factor): refers to the intermediate quantitative index (dimensionless, value range [0,1]) that converts the characteristic quantities (such as amplitude, rate of change, and deviation) of the physical parameters (current, temperature, voltage) of the energy storage unit into the "degree of impact on lifespan". The larger the value, the more significant the impact of the physical parameter on the lifespan of the energy storage unit.
[0031] In embodiments of the present invention, a dynamic frequency urgency coefficient characterizing the frequency urgency level is calculated based on the deviation of the system frequency from a preset safety boundary, specifically including: Set frequency warning boundaries and frequency danger boundaries; When the system frequency is within the frequency warning boundary, the dynamic frequency urgency coefficient is determined to be the lowest value. When the system frequency is between the frequency warning boundary and the frequency danger boundary, the dynamic frequency urgency coefficient increases linearly within a preset range according to the degree to which the system frequency deviates from the frequency warning boundary. When the system frequency exceeds the frequency danger boundary, the dynamic frequency urgency coefficient is determined to be the highest value; The formula for calculating the dynamic frequency urgency coefficient is as follows: ,when ; ,when ; ,when ; in, for The dynamic frequency urgency coefficient is measured in the range of [0,1]. The larger the value, the more critical the frequency. for The system frequency of the microgrid at any given moment; , The lower and upper limits of the frequency warning boundary constitute the safe frequency range; , The lower and upper limits of the frequency danger boundary constitute the critical range for triggering protection actions; Let be a monotonically increasing mapping function defined in the interval [0,1], used to standardize the frequency deviation to an urgency coefficient, satisfying the boundary conditions. , ; By using two-level boundary division and piecewise function design, the precise classification and smooth quantification of frequency urgency were achieved, avoiding control lag or over-adjustment caused by single threshold judgment. This provides a scientific basis for subsequent dynamic adjustment of cost function weights, ensuring the flexibility and reliability of frequency safety control.
[0032] In embodiments of the present invention, the frequency warning boundary and the frequency danger boundary are set according to the inertia characteristics, load characteristics and critical equipment tolerance of the microgrid, and the frequency danger boundary corresponds to the critical value that triggers the low-frequency or high-frequency protection action of the microgrid.
[0033] In an embodiment of the present invention, a dynamic reconfiguration cost function for power allocation optimization is constructed, specifically as follows: The dynamic reconstruction cost function is expressed as: ; in, for The cost of each moment comprehensively reflects the total cost of frequency deviation and lifespan loss; for The system frequency deviation at any given time is the difference between the real-time frequency and the rated frequency. The rated frequency of the microgrid is denoted as , and the reference value for frequency control is denoted as . for Equivalent lifetime power loss at any moment; This is the dynamic frequency urgency coefficient; The weighting function for the frequency deviation penalty term is as follows: Monotonically increasing; The weighting function for the lifetime loss power penalty term is as follows: Monotonically decreasing; This cost function achieves a dynamic balance between the two major objectives of frequency stability and energy storage lifetime. Through adaptive weight adjustment, the power allocation strategy can flexibly switch and optimize priorities according to the system frequency state, which avoids safety risks under extreme frequency conditions and reduces energy storage lifetime loss under normal conditions, thereby improving the economy and stability of microgrid operation.
[0034] In an embodiment of the present invention, the weighting function and The design satisfies: when When it approaches 0, The value of makes the lifetime loss power penalty term affect the cost. China is dominant; when When it approaches 1, The value of makes the frequency deviation penalty term affect the cost. China holds an absolutely dominant position.
[0035] In embodiments of the present invention, solving the problem of minimizing the dynamic reconstruction cost function specifically includes: The current equivalent lifetime loss power and current state of charge of the energy-type energy storage unit, and the current state of charge of the power-type energy storage unit are used as state variables. The power commands of power-type energy storage units and energy-type energy storage units are used as decision variables, and their range of variation is limited by their respective maximum charge and discharge power capabilities. Construct a constraint set that includes constraints on the state of charge maintenance of energy-type energy storage units and constraints on the state of charge maintenance of power-type energy storage units; In each control cycle Based on the current state and the dynamic frequency urgency coefficient Solve the following optimization problem: ; The constraints are: ; ; ; ; in, For the first Power commands for power-type energy storage units within a control cycle; For the first Power commands for energy storage units within a control cycle; , These are the minimum and maximum charge / discharge power limits for power-type energy storage units; , These are the minimum and maximum charge / discharge power limits for energy storage units. For the first The initial state of charge of the power-type energy storage unit in each control cycle; For the first The initial state of charge of the energy storage unit in each control cycle; , These are the minimum and maximum state of charge limits for power-type energy storage units; , These are the minimum and maximum state of charge limits for energy storage units. The charging and discharging efficiency of power-type energy storage units; The charging and discharging efficiency of energy storage units; This refers to the rated energy storage capacity of the power-type energy storage unit; This refers to the rated energy storage capacity of the energy-type energy storage unit; To control the cycle length and optimize the time step of the calculation; Through periodic rolling optimization and multi-constraint design, the feasibility and real-time performance of power allocation commands are ensured. This not only meets the operational safety restrictions of energy storage units but also dynamically tracks changes in system frequency and energy storage lifetime status, ensuring that the power output of the hybrid energy storage system is always in the optimal state and improving the microgrid's ability to cope with new energy fluctuations.
[0036] In embodiments of the present invention, a step of closed-loop correction of the health status of the energy storage unit is further included: The integral of the equivalent lifetime loss power over time is accumulated and used to update the estimated health status value of the energy storage unit online. The parameters in the real-time life stress sensing model are periodically corrected using offline detection data from energy storage units. The updated estimated health status value is used as feedback to adjust the baseline lifetime loss sensitivity of energy storage units in subsequent optimization calculations; estimated health status value Update according to the following formula: ; in, for The estimated health status value of a real-time energy storage unit reflects the current performance integrity of the energy storage unit; The initial health status value of the energy storage unit is the health status of the new equipment or the baseline state. The loss accumulation coefficient is a coefficient used to calibrate the relationship between the "time integral of equivalent lifetime loss power" and the "actual health status decay of the energy storage unit". Its value is obtained by experimental calibration to ensure the accuracy of health status estimation. From the initial moment to The time integral of the equivalent lifetime loss power at any given moment represents the cumulative lifetime loss. for Equivalent lifetime power loss at any given moment.
[0037] This formula enables online dynamic estimation and closed-loop correction of the health status of energy storage units, effectively compensating for estimation deviations caused by model errors and operating condition fluctuations, making the lifetime loss assessment more accurate, providing reliable health status feedback for subsequent power allocation optimization, helping to maximize the full life cycle value of energy storage units, and ensuring the long-term stable operation of microgrids.
[0038] In embodiments of the present invention, the power-type energy storage unit is an energy storage device with a response speed in the millisecond to second range, and the energy-type energy storage unit is an energy storage device with a high energy density.
[0039] In embodiments of the present invention, the power-type energy storage unit is a supercapacitor, and the energy-type energy storage unit is a lithium-ion battery.
[0040] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.
Claims
1. A method for power allocation of hybrid energy storage in microgrids to cope with intermittent renewable energy sources, characterized in that, Includes the following steps: S1. Establish a real-time lifetime stress perception model for energy storage units, which is used to calculate the equivalent lifetime loss power under the current operating conditions based on the real-time operating physical parameters of the energy storage units. S2. The system frequency of the microgrid is acquired in real time, and a dynamic frequency urgency coefficient representing the frequency urgency is calculated based on the deviation of the system frequency from the preset frequency warning boundary and frequency danger boundary. The microgrid is an islanded microgrid containing intermittent new energy generation units, loads and hybrid energy storage systems. The hybrid energy storage system includes power-type energy storage units and energy-type energy storage units. S3. Construct a dynamic reconfiguration cost function for power allocation optimization. The cost function is expressed as: ； in, for The value of a moment For system frequency deviation, This is the equivalent lifetime power loss. This is the dynamic frequency urgency coefficient. and They are respectively about The weight function, and for A monotonically increasing function. for A monotonically decreasing function; S4. Taking the total power demand of the hybrid energy storage system, the real-time state of charge of the energy-type energy storage unit, the output of the real-time lifetime stress perception model, and the dynamic frequency urgency coefficient as inputs, solve the problem of minimizing the dynamic reconfiguration cost function in each control cycle, while satisfying the power and capacity constraints of the power-type energy storage unit and the energy-type energy storage unit, and obtain the optimal power allocation command for the two in the current control cycle and issue it for execution.
2. The microgrid hybrid energy storage power allocation method for intermittent new energy sources according to claim 1, characterized in that, The establishment of a real-time lifetime stress sensing model for energy storage units specifically includes: The physical parameters of the energy storage unit, including operating current, internal temperature, and terminal voltage, are collected in real time. The comparison and rate of change between the operating current and the rated value, the deviation and rate of change between the internal temperature and the reference value, and the closeness of the terminal voltage to the healthy operating voltage range are used as input stress factors. By using a simplified empirical model that couples electrochemical reaction kinetics and thermodynamic effects, the input stress factor is calculated and the equivalent lifetime loss power is output. The fusion calculation is achieved through the following formula: ； ； ； ； in, , , These are the current stress factor, temperature stress factor, and voltage stress factor, respectively. , , They are respectively The operating current, internal temperature, and terminal voltage at any given time; , They are respectively The rate of change of operating current and internal temperature at all times; The duration for which the current exceeds the rated threshold. This represents the deviation of the temperature from the reference value. This is the minimum distance between the terminal voltage and the healthy operating voltage boundary; , , This is a preset factor calculation function; This is the stress fusion function; This is the model parameter set.
3. The microgrid hybrid energy storage power allocation method for intermittent new energy sources according to claim 2, characterized in that, The input stress factor takes into account the operating current, including its instantaneous amplitude, the duration of time it remains above the rated threshold, and the frequency of charging / discharging direction switching.
4. The microgrid hybrid energy storage power allocation method for intermittent new energy sources according to claim 2, characterized in that, The input stress factor takes into account internal temperature, including overheating conditions beyond the optimal operating temperature range and rapid temperature gradients per unit time.
5. The microgrid hybrid energy storage power allocation method for intermittent new energy sources according to claim 1, characterized in that, The calculation of the dynamic frequency urgency coefficient specifically includes: Set frequency warning boundaries With frequency danger boundary ; Dynamic frequency urgency coefficient according to System frequency at time It is calculated using the following piecewise function: ； in, For definition in A monotonically increasing mapping function over an interval, satisfying... , .
6. The microgrid hybrid energy storage power allocation method for intermittent new energy sources according to claim 5, characterized in that, The frequency warning boundary and frequency danger boundary are set according to the inertia characteristics, load characteristics and the tolerance of key equipment of the microgrid. The frequency danger boundary corresponds to the critical value that triggers the low-frequency or high-frequency protection action of the microgrid.
7. The microgrid hybrid energy storage power allocation method for intermittent new energy sources according to claim 1, characterized in that, The weighting function and The design meets the following requirements: when Approaching hour, The value of makes the lifetime loss power penalty term affect the cost. China is dominant; when Approaching hour, The value of makes the frequency deviation penalty term affect the cost. China holds an absolutely dominant position.
8. The microgrid hybrid energy storage power allocation method for intermittent new energy sources according to claim 1, characterized in that, Solving the problem of minimizing the dynamic reconstruction cost function specifically includes: The current equivalent lifetime loss power and current state of charge of the energy-type energy storage unit, and the current state of charge of the power-type energy storage unit are used as state variables. The power command of the power-type energy storage unit and the energy-type energy storage unit , As a decision variable; In each control cycle Based on the current state and the dynamic frequency urgency coefficient Solve the following constrained optimization problem: ； The constraints are: ； ； ； ； in, For the first Power commands for power-type energy storage units within a control cycle; For the first Power commands for energy storage units within a control cycle; , These are the minimum and maximum charge / discharge power limits for power-type energy storage units; , These are the minimum and maximum charge / discharge power limits for energy storage units. For the first The initial state of charge of the power-type energy storage unit in each control cycle; For the first The initial state of charge of the energy storage unit in each control cycle; , These are the minimum and maximum state of charge limits for power-type energy storage units; , These are the minimum and maximum state of charge limits for energy storage units. The charging and discharging efficiency of power-type energy storage units; The charging and discharging efficiency of energy storage units; This refers to the rated energy storage capacity of the power-type energy storage unit; This refers to the rated energy storage capacity of the energy-type energy storage unit; To control the cycle length and optimize the time step of the calculation.
9. A method for power allocation of hybrid energy storage in microgrids to cope with intermittent new energy sources according to claim 1, characterized in that, It also includes a step of closed-loop correction of the health status of the energy storage unit: The integral of the equivalent lifetime power loss over time is accumulated and used to update the estimated health status value of the energy storage unit online. The updated formula is: ； in, for Estimated health status value of the instantaneous energy storage unit; This represents the initial health status value of the energy storage unit. This is the cumulative loss factor; From the initial moment to Time integral of equivalent lifetime power loss at any given moment; for Equivalent lifetime power loss at any given moment.
10. A microgrid hybrid energy storage power allocation method for intermittent new energy sources according to claim 9, characterized in that, The parameters in the real-time lifetime stress sensing model are periodically corrected using the offline detection data of the energy storage unit. The updated estimated health status value is used as feedback to adjust the basic lifetime loss sensitivity of the energy storage unit in subsequent optimization calculations.