Battery and super capacitor hybrid energy storage frequency modulation capability optimization distribution method and system

Abstract

The invention relates to a battery and super capacitor hybrid energy storage frequency modulation capability optimization distribution method and system, and the method comprises the steps: receiving a frequency modulation signal transmitted by a regional power grid at a preset period, and calculating the target frequency modulation power based on the frequency modulation signal and the frequency modulation capacity of hybrid energy storage; adopting a two-stage power distribution strategy to respond to the target frequency modulation power; preferentially scheduling the super capacitor to perform power response, and determining the frequency modulation response power of the super capacitor based on the target frequency modulation power, the real-time energy state of the super capacitor and the power capability constraint; when the response capability of the super capacitor is insufficient, determining the frequency modulation response power of the battery based on the expected income of the unit frequency modulation power of hybrid energy storage, the real-time marginal aging cost of the battery, the state of charge of the battery and the historical extreme value state of charge; based on the frequency modulation response power of the super capacitor and the frequency modulation response power of the battery, updating the energy state of the super capacitor, the state of charge of the battery and the historical extreme value state of charge; and continuously responding to the frequency modulation signal.

Description

Technical Field

[0001] This invention relates to the field of energy storage optimization control technology, and in particular to a method and system for optimizing the allocation of frequency regulation capability of hybrid energy storage of batteries and supercapacitors. Background Technology

[0002] In recent years, with the acceleration of the global energy transition, the large-scale grid connection of renewable energy sources such as wind and solar power has become an inevitable trend in power system development. However, the output power of wind and solar power generation exhibits significant uncertainty and volatility, and its randomness and intermittency pose a severe challenge to the stability of the power grid. This uncertainty not only increases the difficulty of grid frequency regulation but may also lead to power imbalance, affecting the safe operation of the power system. To address these challenges, energy storage technology, as a key means to smooth the fluctuations of renewable energy and enhance grid flexibility, is becoming increasingly important. Energy storage systems can quickly respond to power changes, absorbing or releasing electrical energy, thereby effectively mitigating the impact of the volatility of wind and solar power on the power grid. However, single energy storage technologies often have performance limitations when dealing with complex operating conditions. For example, power-type energy storage has a fast response speed but limited capacity, while energy-type energy storage has a large capacity but a slower response speed. Summary of the Invention

[0003] Based on the above analysis, the embodiments of the present invention aim to provide a method and system for optimizing the allocation of frequency regulation capabilities of hybrid energy storage using batteries and supercapacitors, in order to solve the technical problem of how to coordinate the scheduling of batteries and supercapacitors for power allocation in high-frequency frequency regulation scenarios of the power grid, so as to effectively control and optimize the lifespan degradation of batteries caused by cyclic charging and discharging while meeting the frequency regulation accuracy requirements.

[0004] This invention provides a method for optimizing the allocation of frequency regulation capability in hybrid energy storage systems combining batteries and supercapacitors, comprising the following steps:

[0005] Step S1: Receive frequency regulation signals sent by the target area power grid system at a preset period in real time, and calculate the target frequency regulation power based on the frequency regulation signals and the frequency regulation capacity of the hybrid energy storage system. Step S2: A two-stage power allocation strategy is adopted to respond to the target frequency modulation power. Among them, the supercapacitor is prioritized for power response. Based on the target frequency modulation power, the real-time energy state of the supercapacitor, and power capacity constraints, the frequency modulation response power of the supercapacitor is determined. When the response capability of the supercapacitor is insufficient to reach the target frequency modulation power, the frequency modulation response power of the battery is determined based on the expected benefit of the unit frequency modulation power of the hybrid energy storage, the real-time marginal aging cost of the battery, and the state of charge of the battery and the historical extreme state of charge. Step S3: Based on the frequency modulation response power of the supercapacitor and the frequency modulation response power of the battery, update the energy state of the supercapacitor and the state of charge of the battery and the historical extreme state of charge; return to step S1 to continue responding to subsequent frequency modulation signals.

[0006] Further, step S1 includes: The hybrid energy storage system receives frequency regulation signals from the target area's power grid and normalizes them to obtain normalized frequency regulation values. ; Based on normalized frequency modulation value and frequency regulation capacity of hybrid energy storage The target frequency modulation power is obtained. .

[0007] Furthermore, based on the target frequency modulation power, the real-time energy state of the supercapacitor, and the power capability constraints, the frequency modulation response power of the supercapacitor is determined as follows:

[0008] in, This refers to the frequency modulation response power of the supercapacitor. This represents the maximum power of the supercapacitor. The charging and discharging efficiency of supercapacitors; For the first Frequency modulation value The real-time energy stored in the supercapacitor at the initial moment; For preset period; This refers to the rated capacitance of the supercapacitor.

[0009] Furthermore, the expected benefit per unit frequency regulation power of hybrid energy storage is as follows:

[0010] in, The expected benefit per unit frequency modulation power, For frequency modulation revenue, For battery frequency modulation response power, This represents the expected value of the derivative of the expected return with respect to the regulating power; A set of frequency modulation signals; Frequency modulation value The expected value of the sum of absolute values; For frequency modulation settlement price; For the response accuracy of hybrid energy storage.

[0011] Furthermore, the real-time marginal aging cost of the battery is determined based on the battery's cycle aging model and real-time state of charge; the cycle aging model is based on performing one cycle aging process with a depth of [missing information]. The aging costs caused by the semi-cycle are as follows:

[0012] in, This represents the real-time state of charge of the battery. For the battery during a single charge and discharge operation The change in; The cost of replacing the battery; and These are the proportional coefficient and exponential coefficient for battery aging costs, respectively. This represents the nonlinear influence factor of the cycle depth.

[0013] Furthermore, the real-time marginal aging cost includes the charging marginal cost and the discharging marginal cost; The marginal cost of charging is as follows:

[0014] The marginal cost of discharge is as follows:

[0015] in, In response State of charge of the battery after frequency modulation ; Indicates from 0 to The minimum state of charge of the battery during the period; Indicates from 0 to The maximum state of charge of the battery during the frequency modulation period.

[0016] Furthermore, the theoretical optimal frequency modulation response power of the battery is determined by maximizing the real-time profit model. Based on the theoretical optimal frequency modulation response power, and the constraints of frequency modulation command power, battery maximum power, and battery energy, the frequency modulation response power of the battery is obtained as follows:

[0017] in, This refers to the battery's rated capacity. This refers to the battery's rated power. For real-time SoC of battery; , These are the minimum and maximum states of charge of the battery, respectively. .

[0018] Furthermore, based on the frequency modulation response power of the supercapacitor, the energy state of the supercapacitor is updated as follows:

[0019] in, For the supercapacitor to complete the response Energy after frequency modulation; For index functions, i.e., if the condition is... If true, then ,otherwise, .

[0020] Furthermore, based on the battery's frequency modulation response power, the battery's state of charge is updated as follows:

[0021] in, , These are the charging and discharging power of the battery, respectively. Improve battery charging and discharging efficiency; Update the battery's historical extreme states of charge, including updating the battery's historical maximum and minimum states of charge, as follows:

[0022] in, for From 0 to The battery's historical maximum state of charge during this period; From 0 to The battery's historical minimum state of charge during this period; From 0 to The battery's historical maximum state of charge during this period; From 0 to The battery's historical minimum state of charge during this period.

[0023] The present invention also discloses a hybrid energy storage frequency regulation capability optimization and allocation system of battery and supercapacitor, the system comprising a signal receiving and target parsing module M1, a two-stage optimization decision module M2, and a state management and update module M3; The signal receiving and target parsing module M1 is used to receive frequency modulation signals sent by the power grid system of the target area at a preset period in real time, and calculate the target frequency modulation power based on the frequency modulation signals and the frequency modulation capacity of the hybrid energy storage system. The two-stage optimization decision module M2 is used to respond to the target frequency modulation power using a two-stage power allocation strategy. Specifically, it prioritizes scheduling supercapacitors for power response, determining the frequency modulation response power of the supercapacitors based on the target frequency modulation power, the real-time energy state of the supercapacitors, and power capability constraints. When the response capability of the supercapacitors is insufficient to reach the target frequency modulation power, the frequency modulation response power of the battery is determined based on the expected return per unit frequency modulation power of the hybrid energy storage, the real-time marginal aging cost of the battery, and the battery's state of charge and historical extreme state of charge. The state management and update module M3 is used to update the energy state of the supercapacitor and the state of charge and historical extreme state of charge of the battery based on the frequency modulation response power of the supercapacitor and the frequency modulation response power of the battery, respectively; and return to step S1 to continue responding to subsequent frequency modulation signals.

[0024] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: 1. This invention fully leverages the complementary advantages of hybrid energy storage technologies through a two-stage dynamic response mechanism of "supercapacitor priority and battery supplementation." Supercapacitors, with their millisecond-level response speed and extremely high cycle life, accurately capture and respond to the high-frequency, low-amplitude components in frequency regulation commands, ensuring the system's speed and regulation accuracy. Batteries, as reliable energy backups, provide deep power support when supercapacitor capacity is insufficient, guaranteeing the complete execution of frequency regulation tasks. This strategy achieves seamless coordination and adaptive switching between power-type and energy-type energy storage at the hardware level, significantly improving the overall dynamic response performance and operational reliability of the hybrid energy storage system in response to complex high-frequency grid commands. 2. This invention introduces a marginal aging cost model based on the battery's real-time state of charge (SoC) and historical operating trajectory, quantifying battery life loss as an instantaneous decision cost. By constructing and solving a real-time optimization function aimed at maximizing frequency modulation profits, the system can accurately balance the revenue from frequency modulation services with the cost of battery life loss in each power allocation decision (e.g., every 2 seconds). This avoids overcharging and discharging of batteries and ineffective cycling, fundamentally reducing their operation and replacement costs, thereby significantly improving the utilization rate of hybrid energy storage. 3. The method proposed in this invention is a data-driven, model-predictive intelligent decision-making approach that transforms the approach from passively executing frequency regulation commands to actively optimizing hybrid energy storage. This not only ensures high-quality service (high-precision K-value) for grid frequency regulation but also improves the sustainability of resource utilization by extending battery lifespan. This solution provides a flexible and efficient regulation resource for high-proportion renewable energy grids, helping to improve grid frequency stability and the ability to absorb fluctuating new energy sources. It has significant industry application value and contributes to building a safer and greener new power system.

[0025] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0026] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0027] Figure 1 This is a flowchart of a method for optimizing the allocation of frequency regulation capability in hybrid energy storage of batteries and supercapacitors according to an embodiment of the present invention; Figure 2 This is a schematic diagram showing the SoC changes and half-cycle depth when the battery performs charging frequency modulation operation in an embodiment of the present invention; Figure 3 This is a schematic diagram showing the SoC change and half-cycle depth when the battery performs a discharge frequency modulation operation in an embodiment of the present invention; Figure 4 This is a schematic diagram of a battery and supercapacitor hybrid energy storage frequency regulation capability optimization allocation system module in an embodiment of the present invention. Detailed Implementation

[0028] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0029] The purpose of this invention is to provide an optimized allocation strategy for frequency regulation capability of hybrid energy storage combining batteries and supercapacitors. Considering battery aging and the settlement rules of the frequency regulation market, this strategy aims to meet the requirements of high-frequency frequency regulation in grid scenarios while effectively controlling and optimizing the battery life degradation caused by cyclic discharge. This results in the optimal real-time power allocation for frequency regulation, providing theoretical guidance for enhancing the operation strategy and profitability of hybrid energy storage.

[0030] To address the aforementioned issues, developing hybrid energy storage systems that can balance rapid response with long-term energy storage requirements has become an important direction for solving the grid connection problem of renewable energy.

[0031] Hybrid energy storage systems organically combine power-type energy storage (such as supercapacitors) with energy-type energy storage (such as batteries), fully leveraging the technological advantages of both to achieve synergistic complementarity between rapid response and long-term energy storage. Supercapacitors, with their extremely low aging costs and millisecond-level response speed, can efficiently handle high-frequency power fluctuations, ensuring rapid response to frequency regulation commands; while batteries, with their high energy density, can undertake long-term charging and discharging tasks, meeting the power system's energy storage capacity requirements. This complementary characteristic gives hybrid energy storage systems an advantage in power systems. Due to their high efficiency and flexibility, hybrid energy storage systems are gradually becoming important participants in grid frequency regulation and energy management.

[0032] Despite the significant potential of hybrid energy storage systems in terms of technical performance, research on optimized control strategies for their application in power systems is still in its early stages. In practical operation, grid dispatching systems typically issue frequency regulation commands to energy storage devices at 2-second intervals, and the response accuracy of hybrid energy storage directly determines the revenue from frequency regulation services. However, existing research often overlooks the impact of battery aging costs during charging and discharging on system profits. Overcharging and discharging significantly shorten battery cycle life, thereby significantly increasing operation and maintenance costs and ultimately affecting the utilization rate of hybrid energy storage.

[0033] Therefore, researching real-time frequency regulation capacity optimization allocation strategies aimed at maximizing the profits of hybrid energy storage is of significant theoretical importance. By optimizing the frequency regulation capacity allocation of hybrid energy storage systems using batteries and supercapacitors, and rationally allocating the frequency regulation tasks of supercapacitors and batteries, it is possible to reduce battery aging costs and improve the overall utilization rate of hybrid energy storage systems while ensuring frequency regulation response accuracy. Breakthroughs in this research direction will provide theoretical support for the widespread application of hybrid energy storage systems in power systems, promote the coordinated development of renewable energy and energy storage, and contribute to building a more efficient, flexible, and sustainable new power system.

[0034] A specific embodiment of the present invention discloses a method for optimizing the allocation of frequency regulation capability in hybrid energy storage systems combining batteries and supercapacitors, such as... Figure 1 As shown, it includes the following steps: Step S1: Receive frequency regulation signals sent by the target area power grid system at a preset period in real time, and calculate the target frequency regulation power based on the frequency regulation signals and the frequency regulation capacity of the hybrid energy storage system. Step S2: A two-stage power allocation strategy is adopted to respond to the target frequency modulation power. Among them, the supercapacitor is prioritized for power response. Based on the target frequency modulation power, the real-time energy state of the supercapacitor, and power capacity constraints, the frequency modulation response power of the supercapacitor is determined. When the response capability of the supercapacitor is insufficient to reach the target frequency modulation power, the frequency modulation response power of the battery is determined based on the expected benefit of the unit frequency modulation power of the hybrid energy storage, the real-time marginal aging cost of the battery, and the state of charge of the battery and the historical extreme state of charge. Step S3: Based on the frequency modulation response power of the supercapacitor and the frequency modulation response power of the battery, update the energy state of the supercapacitor and the state of charge of the battery and the historical extreme state of charge; return to step S1 to continue responding to subsequent frequency modulation signals.

[0035] Step S1, specifically.

[0036] Step S1 includes: The hybrid energy storage system receives frequency regulation signals from the target area's power grid and normalizes them to obtain normalized frequency regulation values. ; Based on normalized frequency modulation value and frequency regulation capacity of hybrid energy storage The target frequency modulation power is obtained. .

[0037] The hybrid energy storage system receives power frequency regulation signals from the power grid system of the target area in real time. For example, the power system sends the signals once every 2 seconds. Normalize the power frequency-modulated signal to obtain the power frequency-modulated value in the interval [-1, 1]. ; Obtain the battery's charging and discharging power. and and frequency regulation capacity of hybrid energy storage , The maximum power capacity of hybrid energy storage that can be used for frequency regulation is a fixed value. frequency modulation value With hybrid energy storage frequency regulation capacity Multiply to obtain the target frequency modulation power. .

[0038] Step S1 is to normalize the frequency regulation signal sent by the power grid in the target area to obtain the frequency regulation value, and then obtain the target frequency regulation power value that the hybrid energy storage system needs to track and execute, so as to provide the target frequency regulation power for subsequent power allocation optimization.

[0039] Step S2 includes steps S21-S22.

[0040] Based on the target frequency modulation power obtained in step S1 The target frequency modulation power is allocated. A two-stage power allocation strategy is adopted to respond to the target frequency modulation power, and the frequency modulation response power of the supercapacitor and battery is determined.

[0041] Step S21: Prioritize the use of supercapacitors with extremely low aging costs for response.

[0042] Based on the target frequency modulation power, the real-time energy state of the supercapacitor, and the power capability constraints, the frequency modulation response power of the supercapacitor is determined as follows: Formula (1) in, This refers to the frequency modulation response power of the supercapacitor. This represents the maximum power of the supercapacitor. The charging and discharging efficiency of supercapacitors; For the first Frequency modulation value The real-time energy stored in the supercapacitor at the initial moment; For preset period; This refers to the rated capacitance of the supercapacitor.

[0043] The index is the serial number of the frequency modulation value; subscript This indicates that it is a supercapacitor; For example, the preset period is the duration of the power frequency modulation signal. It lasts for 2 seconds.

[0044] and The first term in the function ensures the frequency modulation response power of the supercapacitor. Keep close to, but not exceed, the target frequency modulation power. The second term is the maximum power constraint of the supercapacitor, and the third term is the remaining energy constraint of the capacitor, ensuring that the capacitor's energy is within the specified limits. Within the range.

[0045] Supercapacitors charge or absorb power; Supercapacitors discharge or release power.

[0046] Step S22: When the response capability of the supercapacitor is insufficient to reach the target frequency modulation power, the battery is scheduled to respond.

[0047] The frequency regulation response power of the battery is determined based on the expected return per unit frequency regulation power of hybrid energy storage, the real-time marginal aging cost of the battery, and the battery's state of charge and historical extreme state of charge.

[0048] The expected benefit per unit frequency regulation power of hybrid energy storage is as follows: Formula (2) in, The expected benefit per unit frequency modulation power, For frequency modulation revenue, For battery frequency modulation response power, This represents the expected value of the derivative of the expected return with respect to the regulating power; A set of frequency modulation signals; Frequency modulation value The expected value of the sum of absolute values; The settlement price is for frequency modulation. For the response accuracy of hybrid energy storage.

[0049] subscript Indicates battery; Increasing profits will increase revenue, but it may also increase the cost of battery aging. Therefore, a balance must be struck between increasing profits and losses. And reduce aging costs.

[0050] The physical meaning of formula (4) represents the expected value of the gain per unit frequency modulation power.

[0051] The real-time marginal aging cost of the battery is determined based on the battery's cycle aging model and real-time state of charge; the cycle aging model is based on performing one cycle of aging with a depth of [missing information]. The aging costs caused by the semi-cycle are as follows: Formula (3) in, This represents the real-time state of charge of the battery. For the battery during a single charge and discharge operation The change in; The cost of replacing the battery; and These are the proportional coefficient and exponential coefficient for battery aging costs, respectively. This represents the nonlinear influence factor of the cycle depth.

[0052] in, and These are battery aging characteristic parameters obtained by fitting standard cycle aging test data based on the specific chemical system of the battery (such as lithium iron phosphate, ternary lithium, etc.).

[0053] For example: parameter This is a proportionality factor related to the battery's rated cycle life. The value can be obtained by fitting the cycle life data of the battery under standard test conditions.

[0054] parameter This is an exponential coefficient reflecting the nonlinear relationship between aging costs and cycle depth. For most lithium-ion batteries, this value is typically greater than 1 (e.g., in the range of 1.2–1.8), indicating that aging costs increase superlinearly with increasing cycle depth.

[0055] During the frequency modulation phase, when the battery response is at the first... x When the signal is approximate, if the battery is charged, such as... Figure 2 As shown in the figure, Indicates from 0 to x The minimum state of charge of the battery during period 1. In response x -1 signal SoC If the battery is charging, then Figure 2 Increased depth of charging cycle 1 .

[0056] Conversely, if the battery is in a discharged state, such as Figure 3 As shown in the figure, Indicates from 0 to x The maximum state of charge of the battery during period 1. Figure 3 The cycle depth of discharge cycle 1 increases .

[0057] The real-time marginal aging cost includes the charging marginal cost and the discharging marginal cost; The marginal cost of charging is as follows:

[0058] Formula (4) The marginal cost of discharge is as follows:

[0059] Formula (5) in, In response State of charge of the battery after frequency modulation ; Indicates from 0 to The minimum state of charge of the battery during the period; Indicates from 0 to The maximum state of charge of the battery during the frequency modulation period.

[0060] Based on the summary of formulas (6)-(7), Real-time marginal aging cost of batteries due to increases or decreases It is expressed as follows:

[0061] Formula (6) When calculating battery aging costs related to FR (Frequency Regulation) power, the combined impact of the energy market and FR power on the battery's state of charge (SoC) during charging and discharging must be considered. Energy market power is defined as... Then the battery frequency modulation power response is the first The real-time profit model for the secondary frequency modulation value is as follows:

[0062] Formula (7) , This refers to the battery's frequency modulation power. For the battery's rated capacity, Calculate the battery charge / discharge efficiency. Considering revenue and cost, calculate the battery frequency modulation power that maximizes profit.

[0063] By analyzing equation (9) and By considering the positive and negative states, we obtain the expression for the optimal frequency modulation power for profit, as follows:

[0064] Formula (8)

[0065] Formula (9) in, The optimal upward frequency modulation response power for profit. The optimal down-modulation frequency response power for profit.

[0066] The theoretical optimal frequency modulation response power of the battery is determined by maximizing the real-time profit function. Based on the theoretical optimal frequency modulation response power, and under the constraints of the frequency modulation command power, the battery's maximum power, and the battery energy, the frequency modulation response power of the battery is obtained as follows:

[0067] Formula (10) in, This refers to the battery's rated capacity. This refers to the battery's rated power. For real-time SoC of battery; , These are the minimum and maximum states of charge of the battery, respectively. .

[0068] The first term in the min and max functions ensures that the response power of the hybrid energy storage remains close to but does not exceed the frequency modulation command power. The second term is the maximum power constraint of the supercapacitor, the third term is the energy constraint, and the fourth term is the profit-optimal frequency modulation power constraint.

[0069] Step S2 is to intelligently allocate power tasks to supercapacitors and batteries based on the target frequency modulation power and through a two-stage power allocation strategy response mechanism. This ensures command tracking accuracy while minimizing battery life loss and achieving optimal dynamic response of the hybrid energy storage system.

[0070] Step S3, specifically.

[0071] The energy state of the supercapacitor and the state of charge of the battery are updated based on the frequency modulation response power of the supercapacitor and the frequency modulation response power of the battery, respectively, and the historical extreme state of charge.

[0072] Based on the frequency modulation response power of the supercapacitor, the energy state of the supercapacitor is updated as follows: Formula (11) in, For the supercapacitor to complete the response Energy after frequency modulation; For index functions, i.e., if the condition is... If true, then ,otherwise, .

[0073] Based on the battery's frequency modulation response power, the battery's state of charge is updated as follows:

[0074] Formula (12) in, , These are the charging and discharging power of the battery, respectively. Improve battery charging and discharging efficiency; Update the battery's historical extreme states of charge, including updating the battery's historical maximum and minimum states of charge, as follows: Formula (13) in, From 0 to The battery's historical maximum state of charge during this period; From 0 to The battery's historical minimum state of charge during this period; From 0 to The battery's historical maximum state of charge during this period; From 0 to The battery's historical minimum state of charge during this period.

[0075] After completing the frequency regulation signal response, the hybrid energy storage system of battery and supercapacitor receives the next frequency regulation signal sent by the power grid system of the target area.

[0076] Step S3 completes the closed loop of a control cycle by updating the real-time state (energy / state of charge) and historical operating trajectory of the energy storage elements of the battery and supercapacitor, providing a decision basis for responding to the next frequency modulation signal and ensuring the continuity and adaptability of the optimization strategy.

[0077] Example 2: A specific embodiment of the present invention discloses a hybrid energy storage frequency regulation capability optimization allocation system combining batteries and supercapacitors, thereby realizing the hybrid energy storage frequency regulation capability optimization allocation method in Embodiment 1. The specific implementation of each module is described in the corresponding description in Embodiment 1.

[0078] like Figure 4 As shown, a hybrid energy storage frequency regulation capability optimization and allocation system combining batteries and supercapacitors is disclosed. The system includes a signal receiving and target parsing module M1, a two-stage optimization decision-making module M2, and a state management and update module M3. The signal receiving and target parsing module M1 is used to receive frequency modulation signals sent by the power grid system of the target area at a preset period in real time, and calculate the target frequency modulation power based on the frequency modulation signals and the frequency modulation capacity of the hybrid energy storage system. The two-stage optimization decision module M2 is used to respond to the target frequency modulation power using a two-stage power allocation strategy. Specifically, it prioritizes scheduling supercapacitors for power response, determining the frequency modulation response power of the supercapacitors based on the target frequency modulation power, the real-time energy state of the supercapacitors, and power capability constraints. When the response capability of the supercapacitors is insufficient to reach the target frequency modulation power, the frequency modulation response power of the battery is determined based on the expected return per unit frequency modulation power of the hybrid energy storage, the real-time marginal aging cost of the battery, and the battery's state of charge and historical extreme state of charge. The state management and update module M3 is used to update the energy state of the supercapacitor and the state of charge and historical extreme state of charge of the battery based on the frequency modulation response power of the supercapacitor and the frequency modulation response power of the battery, respectively; and return to step S1 to continue responding to subsequent frequency modulation signals.

[0079] Since the system in this embodiment and the method in Embodiment 1 are related and can be referenced from each other, this description is redundant and will not be repeated here. Because this system embodiment shares the same principle as the above method embodiment, it also possesses the corresponding technical effects of the above method embodiment.

[0080] In summary, the method and system for optimizing the frequency regulation capability of hybrid energy storage combining batteries and supercapacitors according to embodiments of the present invention have the following beneficial effects: 1. This invention fully leverages the complementary advantages of hybrid energy storage technologies through a two-stage dynamic response mechanism of "supercapacitor priority and battery supplementation." Supercapacitors, with their millisecond-level response speed and extremely high cycle life, accurately capture and respond to the high-frequency, low-amplitude components in frequency regulation commands, ensuring the system's speed and regulation accuracy. Batteries, as reliable energy backups, provide deep power support when supercapacitor capacity is insufficient, guaranteeing the complete execution of frequency regulation tasks. This strategy achieves seamless coordination and adaptive switching between power-type and energy-type energy storage at the hardware level, significantly improving the overall dynamic response performance and operational reliability of the hybrid energy storage system in response to complex high-frequency grid commands. 2. This invention introduces a marginal aging cost model based on the battery's real-time state of charge (SoC) and historical operating trajectory, quantifying battery life loss as an instantaneous decision cost. By constructing and solving a real-time optimization function aimed at maximizing frequency modulation profits, the system can accurately balance the revenue from frequency modulation services with the cost of battery life loss in each power allocation decision (e.g., every 2 seconds). This avoids overcharging and discharging of batteries and ineffective cycling, fundamentally reducing their operation and replacement costs, thereby significantly improving the utilization rate of hybrid energy storage. 3. The method proposed in this invention is a data-driven, model-predictive intelligent decision-making approach that transforms the approach from passively executing frequency regulation commands to actively optimizing hybrid energy storage. This not only ensures high-quality service (high-precision K-value) for grid frequency regulation but also improves the sustainability of resource utilization by extending battery lifespan. This solution provides a flexible and efficient regulation resource for high-proportion renewable energy grids, helping to improve grid frequency stability and the ability to absorb fluctuating new energy sources. It has significant industry application value and contributes to building a safer and greener new power system.

[0081] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0082] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the allocation of frequency regulation capability in hybrid energy storage systems combining batteries and supercapacitors, characterized in that, Includes the following steps: Step S1: Receive frequency regulation signals sent by the target area power grid system at a preset period in real time, and calculate the target frequency regulation power based on the frequency regulation signals and the frequency regulation capacity of the hybrid energy storage system. Step S2: A two-stage power allocation strategy is adopted to respond to the target frequency modulation power. Among them, the supercapacitor is prioritized for power response. Based on the target frequency modulation power, the real-time energy state of the supercapacitor, and power capacity constraints, the frequency modulation response power of the supercapacitor is determined. When the response capability of the supercapacitor is insufficient to reach the target frequency modulation power, the frequency modulation response power of the battery is determined based on the expected benefit of the unit frequency modulation power of the hybrid energy storage, the real-time marginal aging cost of the battery, and the state of charge of the battery and the historical extreme state of charge. Step S3: Based on the frequency modulation response power of the supercapacitor and the frequency modulation response power of the battery, update the energy state of the supercapacitor and the state of charge of the battery and the historical extreme state of charge; return to step S1 to continue responding to subsequent frequency modulation signals.

2. The method for optimizing the allocation of frequency regulation capability in hybrid energy storage of batteries and supercapacitors according to claim 1, characterized in that, Step S1 includes: The hybrid energy storage system receives frequency regulation signals from the target area's power grid and normalizes them to obtain normalized frequency regulation values. ; Based on normalized frequency modulation value and frequency regulation capacity of hybrid energy storage The target frequency modulation power is obtained. .

3. The method for optimizing the allocation of frequency regulation capability in hybrid energy storage of batteries and supercapacitors according to claim 2, characterized in that, Based on the target frequency modulation power, the real-time energy state of the supercapacitor, and the power capability constraints, the frequency modulation response power of the supercapacitor is determined as follows: in, This refers to the frequency modulation response power of the supercapacitor. This represents the maximum power of the supercapacitor. The charging and discharging efficiency of supercapacitors; For the first Frequency modulation value The real-time energy stored in the supercapacitor at the initial moment; For preset period; This refers to the rated capacity of the supercapacitor.

4. The method for optimizing the allocation of frequency regulation capability in hybrid energy storage of batteries and supercapacitors according to claim 3, characterized in that, The expected benefit per unit frequency regulation power of hybrid energy storage is as follows: in, The expected benefit per unit frequency modulation power, For frequency modulation revenue, For battery frequency modulation response power, This represents the expected value of the derivative of the expected return with respect to the regulating power; A set of frequency modulation signals; Frequency modulation value The expected value of the sum of absolute values; The settlement price is for frequency modulation. For the response accuracy of hybrid energy storage.

5. The method for optimizing the allocation of frequency regulation capability in hybrid energy storage of batteries and supercapacitors according to claim 1, characterized in that, The real-time marginal aging cost of the battery is determined based on the battery's cycle aging model and real-time state of charge; the cycle aging model is based on performing one cycle of aging with a depth of [missing information]. The aging costs caused by the semi-cycle are as follows: in, This represents the real-time state of charge of the battery. For the battery during a single charge and discharge operation The change in; The cost of replacing the battery; and These are the proportional coefficient and exponential coefficient for battery aging costs, respectively. This represents the nonlinear influence factor of the cycle depth.

6. The method for optimizing the allocation of frequency regulation capability in hybrid energy storage of batteries and supercapacitors according to claim 5, characterized in that, The real-time marginal aging cost includes the charging marginal cost and the discharging marginal cost; The marginal cost of charging is as follows: The marginal cost of discharge is as follows: in, In response State of charge of the battery after frequency modulation ; Indicates from 0 to The minimum state of charge of the battery during the period; Indicates from 0 to The maximum state of charge of the battery during the frequency modulation period.

7. The method for optimizing the allocation of frequency regulation capability in hybrid energy storage of batteries and supercapacitors according to claim 6, characterized in that, The theoretical optimal frequency modulation response power of the battery is determined by maximizing the real-time profit model. Based on the theoretical optimal frequency modulation response power, and constraints of the frequency modulation command power, the battery's maximum power, and the battery energy, the frequency modulation response power of the battery is obtained as follows: in, This refers to the battery's rated capacity. This refers to the battery's rated power. For real-time SoC of battery; , These are the minimum and maximum states of charge of the battery, respectively. .

8. The method for optimizing the allocation of frequency regulation capability in hybrid energy storage of batteries and supercapacitors according to claim 7, characterized in that, Based on the frequency modulation response power of the supercapacitor, the energy state of the supercapacitor is updated as follows: in, For the supercapacitor to complete the response Energy after frequency modulation; For index functions, i.e., if the condition is... If true, then ,otherwise, .

9. The method for optimizing the allocation of frequency regulation capability in hybrid energy storage of batteries and supercapacitors according to any one of claims 3-7, characterized in that, Based on the battery's frequency modulation response power, the battery's state of charge is updated as follows: in, , These are the charging and discharging power of the battery, respectively. Improve battery charging and discharging efficiency; Update the battery's historical extreme state of charge, including updating the battery's historical maximum and minimum state of charge, as follows: in, for From 0 to The battery's historical maximum state of charge during this period; From 0 to The battery's historical minimum state of charge during this period; From 0 to The battery's historical maximum state of charge during this period; From 0 to The battery's historical minimum state of charge during this period.

10. A hybrid energy storage frequency regulation capability optimization and allocation system combining batteries and supercapacitors, characterized in that, The system includes a signal receiving and target parsing module M1, a two-stage optimization decision-making module M2, and a state management and update module M3; The signal receiving and target parsing module M1 is used to receive frequency modulation signals sent by the power grid system of the target area at a preset period in real time, and calculate the target frequency modulation power based on the frequency modulation signals and the frequency modulation capacity of the hybrid energy storage system. The two-stage optimization decision module M2 is used to respond to the target frequency modulation power using a two-stage power allocation strategy. Specifically, it prioritizes scheduling supercapacitors for power response, determining the frequency modulation response power of the supercapacitors based on the target frequency modulation power, the real-time energy state of the supercapacitors, and power capability constraints. When the response capability of the supercapacitors is insufficient to reach the target frequency modulation power, the frequency modulation response power of the battery is determined based on the expected return per unit frequency modulation power of the hybrid energy storage, the real-time marginal aging cost of the battery, and the battery's state of charge and historical extreme state of charge. The state management and update module M3 is used to update the energy state of the supercapacitor and the state of charge and historical extreme state of charge of the battery based on the frequency modulation response power of the supercapacitor and the frequency modulation response power of the battery, respectively; and return to step S1 to continue responding to subsequent frequency modulation signals.

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