Electric power system deep peak regulation method for assisting thermal power generating unit to provide fast frequency response in combination with energy storage system

Abstract

The invention discloses a power system deep peak regulation method for assisting a thermal power generating unit to provide fast frequency response by combining an energy storage system, which comprises the following steps: collecting system parameters of the thermal power generating unit and the energy storage system, and establishing a model for characteristics of providing frequency modulation resources and inertia resources for the thermal power generating unit and providing fast frequency modulation resources for energy storage equipment; collecting frequency parameters of the system and output data of renewable energy sources, and establishing a model in which the system participates in deep peak regulation and performs frequency and inertia response; integrating the frequency modulation resource and inertia resource model of the thermal power generating unit, the energy storage rapid frequency modulation resource model and the deep peak regulation and frequency and inertia response model of the system into system scheduling to form an energy storage-containing power system scheduling model integrated with deep peak regulation and frequency and inertia response functions; system parameters are imported, and a solver is used for solving the scheduling model, so that an optimized system scheduling strategy is obtained. According to the method, the performance condition of the energy storage-containing power system in deep peak regulation and frequency and inertia response is more accurately considered, so that the optimal scheduling decision conforming to the actual condition can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage scheduling, and in particular to a method for deep peak regulation of an electric power system that combines an energy storage system to assist a thermal power unit in providing a rapid frequency response. Background Art

[0002] As the proportion of renewable energy access to the grid continues to increase, it has relatively replaced a large number of traditional synchronous units, resulting in a significant reduction in grid frequency regulation resources, threatening the stability and security of the grid. In order to solve the problem of frequency regulation difficulties caused by a high proportion of renewable energy, energy storage with fast frequency regulation function is introduced as a flexible resource, and the system's frequency regulation process is incorporated into the system scheduling model to achieve frequency support for the system.

[0003] For the dynamic response of system frequency and frequency regulation technology, there have been rich research results at home and abroad for many years, such as document 1 "Joint optimization model of 100MW energy storage participating in dual auxiliary service dispatch of power grid" (Zhou Xiaoyu, Liu Rao, Bao Fuzeng. Automation of Electric Power Systems, 2021, 45(19): 60-69.) Considering the dual auxiliary services of energy storage participating in peak regulation and frequency response, the system minimum frequency during frequency response is calculated and the droop coefficient of energy storage is selected based on it. A grid unit combination model that takes into account the participation of energy storage in dual auxiliary services is established to determine the unit combination and output plan every 15 minutes the next day, thereby solving the problem of coordinated dispatch of electrochemical energy storage at a large scale of 100MW at multiple time scales; document 2 "Assigning shadow prices to synthetic inertia and frequency response reserves from renewable energy sources" (Badesa, L., Matamala, C., Zhou, Y, et al. IEEE Transactions on SustainableEnergy, 2023, 14(01): 12-26.) The frequency minimum point constraint in frequency security is accurately modeled, the product of variables in the constraint is relaxed, and the frequency minimum point constraint is established as a second-order cone constraint, which improves the compatibility of the model. The value of frequency regulation services provided by energy storage, units and other equipment is evaluated by calculating the shadow prices of each constraint; Reference 3 "Energy storage self-dispatching strategy considering cycle life and electric energy-frequency market clearing" (Chen Zeyu, Chen Yanbo. Automation of Electric Power Systems, 2024, 48(14): 28-41) Considering the contradiction between the cycle life calculation of energy storage and the balance of electricity and frequency regulation requirements, an improved equivalent cycle life model (MECL) is designed to accurately characterize the operating loss of energy storage, and a double-layer Stackelberg game model is established to describe the energy storage self-dispatching operation strategy. The model is divided into main and sub-problems for iterative solution by combining the strong duality theorem, KKT conditions and Benders decomposition. Scenario analysis verifies that the operation strategy can improve the operating efficiency of energy storage in the electric energy-frequency market.

[0004] According to current research results, there are many patent applications involving system frequency dynamic response and frequency modulation technology. For example, Patent 1 "A hybrid energy storage participating in grid frequency regulation method and system" (Liu Mosi, Sun Zhiyuan, Li Mingpo, Zheng Kun, Xiong Li, Zhang Yihui, Dou Qian, Hu Hong, Meng Xuanren. CN118157183A, 2024-06-07.) proposed a method and system for hybrid energy storage participating in grid frequency regulation, established a grid-connected inverter model based on virtual synchronous machine technology, and realized the smooth switching of energy storage between virtual inertia control and virtual synchronous machine control through the control strategy of adaptive functions, which smoothed the low and high frequency power disturbances in the new energy power system and improved the system stability. Qualitative; Patent 2 "A strategy construction method and device for an energy storage system participating in primary frequency regulation" (Xie Pingping, Lu Qiuyu, Zhu Yu, Yang Yinguo, Lin Xu, Chen Yue, Wu Jiekang, Lei Zhen. CN116961023A, 2023-10-27.) proposes a strategy construction method and device for an energy storage system participating in primary frequency regulation. The target frequency regulation reserve capacity of the energy storage system is calculated by the system's preset primary frequency regulation demand. When the actual frequency regulation capacity is insufficient, the virtual droop and inertia control method is used to construct the frequency regulation strategy; when the capacity reaches the target, a self-recovery strategy is constructed based on the charge and discharge self-recovery coefficient.

[0005] However, the traditional method has the following inherent defects:

[0006] 1. The accurate characterization of the deep peak load regulation and frequency regulation response process of the power system has not been integrated into the system dispatch model, and the description of the system flexibility is not accurate enough.

[0007] 2. The coupling process of energy storage equipment and thermal power units jointly participating in the system frequency regulation response is not accurately presented. The lack of modeling of one of them cannot accurately reflect the frequency regulation response of the energy storage system.

[0008] In view of this, there is a need for a deep peak-shaving method for the power system that combines an energy storage system to assist thermal power units in providing rapid frequency response. Summary of the invention

[0009] In view of the problems existing in the prior art, the present invention provides a method for deep peak regulation of the power system that combines an energy storage system to assist thermal power units to provide rapid frequency response, which can integrate deep peak regulation and frequency response functions into the system dispatch model, strengthen the consideration of system flexibility, meet the system's frequency regulation requirements, and improve the system's safety and reliability. The specific technical solution is as follows:

[0010] A method for deep peak regulation of a power system in combination with an energy storage system to assist a thermal power unit to provide a fast frequency response, comprising:

[0011] Import thermal power unit system parameters, energy storage system parameters, system frequency parameters and renewable energy output data;

[0012] Establish a model where the energy storage system provides rapid frequency regulation and the thermal power unit provides inertia resources and frequency regulation resources;

[0013] Establish system deep peak regulation model, total frequency regulation resource measurement model and system frequency security model;

[0014] Supplement the energy storage system operation model, thermal power unit operation model and system grid operation model;

[0015] Set up the objective function and build a power system dispatch model with energy storage that integrates deep peak regulation and frequency and inertia response functions;

[0016] The power system dispatch model is solved to obtain the dispatch strategy of thermal power units and energy storage dispatch strategy.

[0017] Preferably, the thermal power unit system parameters include the maximum output of the unit, the minimum output of the unit, the maximum climbing power, the minimum online / offline time, the output cost, the startup cost, the inertia coefficient and the maximum frequency regulation capacity;

[0018] The energy storage device parameters include energy storage device capacity, maximum output, charging efficiency and discharging efficiency.

[0019] Preferably, the fast frequency regulation model provided by the energy storage system, the inertia resource model provided by the thermal power unit and the frequency regulation resource model are established to embody model optimization through constraint conditions.

[0020] Preferably, the constraints of the fast frequency regulation model include upper and lower limit constraints of energy storage frequency regulation reserve and energy storage frequency regulation energy reserve constraints;

[0021] The constraints of the inertia resource model include inertia reserve constraints of thermal power units;

[0022] The constraints of the frequency resource model include the frequency reserve power constraint of the thermal power unit and the output upper limit constraint of the unit reserve power.

[0023] Preferably, the system frequency parameters include system rated frequency, damping coefficient, maximum frequency change rate, maximum quasi-steady-state frequency deviation, and maximum frequency deviation;

[0024] The output data of the renewable energy includes a new energy output curve and a unit output cost.

[0025] Preferably, the establishing of the system deep peak shaving model, the total frequency regulation resource metering model and the system frequency security model also includes constraining the system deep peak shaving model, the total frequency regulation resource metering model and the system frequency security model and then optimizing the model.

[0026] Preferably, the constraint conditions of the system deep peak-shaving model include deep peak-shaving output balance constraints;

[0027] The constraints of the total frequency regulation resource metering model include the total inertia resource constraints of thermal power units, the total frequency regulation resource constraints of thermal power units, and the total frequency regulation resource constraints of energy storage;

[0028] The constraints of the system frequency safety model include a maximum frequency change rate constraint, a maximum quasi-steady-state frequency deviation constraint, and a frequency minimum point constraint.

[0029] Preferably, the energy storage system operation model includes charging power constraints, discharging power constraints, state of charge timing constraints, state of charge upper and lower limit constraints, and initial and final charge consistency constraints;

[0030] The thermal power unit operation model includes unit active output constraints, unit reactive output constraints, unit ramp constraints, unit minimum online and offline time constraints, and unit switch action and online state logic constraints;

[0031] The system grid operation model includes node voltage constraints, line flow constraints, power balance constraints and second-order cone relaxation constraints.

[0032] Preferably, the objective function is the sum of the new energy station output cost and the unit operation cost.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. In the present invention, the deep peak regulation and frequency response functions are integrated into the system scheduling model, which strengthens the consideration of system flexibility, meets the frequency regulation requirements of the system, and improves the safety and reliability of the system.

[0035] 2. In the present invention, a more accurate modeling of the process of energy storage participating in deep peak regulation and providing frequency regulation services is carried out, which is conducive to incorporating it into system scheduling considerations.

[0036] 3. In the present invention, the deep peak regulation method of the power system that combines the energy storage system to assist the thermal power unit to provide a fast frequency response is established as a second-order cone mixed integer programming model, which is easy to solve using a solver. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0038] Figure 1The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0041] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0042] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0043] Example 1

[0044] See also Figure 1 , a method for deep peak regulation of a power system in combination with an energy storage system to assist thermal power units in providing fast frequency response, comprising:

[0045] Import the system parameters of thermal power units, energy storage system parameters, system frequency parameters and output data of renewable energy; establish a model in which the energy storage system provides rapid frequency regulation, and a model in which the thermal power units provide inertia resources and frequency regulation resources; establish a system deep peak regulation model, a total frequency regulation resource metering model and a system frequency safety model; supplement the energy storage system operation model, the thermal power unit operation model and the system grid operation model; set up an objective function and establish a power system dispatch model with energy storage that integrates deep peak regulation as well as frequency and inertia response functions; solve the power system dispatch model to obtain the thermal power unit dispatch strategy and the energy storage dispatch strategy.

[0046] As a preferred embodiment, the system parameters of the thermal power unit include the maximum output of the unit, the minimum output of the unit, the maximum climbing power, the minimum online / offline time, the output cost, the startup cost, the inertia coefficient and the maximum frequency regulation capacity; the energy storage device parameters include the energy storage device capacity, maximum output, charging efficiency and discharging efficiency.

[0047] The details are shown in the following table:

[0048] Table 1 System parameters of thermal power units and energy storage that need to be obtained

[0049]

[0050] As a preferred embodiment, the fast frequency regulation model provided by the energy storage system, the inertia resource model provided by the thermal power unit and the frequency regulation resource model are established to embody model optimization through constraint conditions. The details are as follows:

[0051] The constraints of the fast frequency modulation model include:

[0052] Energy storage frequency regulation reserve upper and lower limit constraints:

[0053] Energy reserve constraints for energy storage and frequency regulation:

[0054] In the formula, represents the fast frequency regulation resource backup provided by the energy storage device s at time t, and They represent the charging power, discharging power and maximum output of the energy storage device s respectively; represents the energy level of energy storage device s, t reg,dur Indicates the duration of a frequency modulation process, η d represents the discharge efficiency of energy storage, Indicates the minimum energy capacity of the energy storage device s.

[0055] The constraints of the inertia resource model include:

[0056] Inertia reserve constraints of thermal power units: In the formula, It is a 0-1 variable, indicating the operating status of the unit, 1 indicates operation, 0 indicates shutdown, represents the fixed inertia coefficient of the unit u, Indicates the maximum output of unit u.

[0057] The constraints of the frequency resource model include:

[0058] Frequency reserve power constraints of thermal power units:

[0059] The upper limit constraint of the unit's standby power output:

[0060] In the formula, represents the power capacity of the frequency regulation resource provided by thermal power unit u at time t, represents the maximum frequency regulation resource capacity that thermal power unit u can provide, P u,t represents the output of thermal power unit u at time t, Indicates the maximum output of thermal power unit u.

[0061] As a preferred embodiment, the system frequency parameters include system rated frequency, damping coefficient, maximum frequency change rate, maximum quasi-steady-state frequency deviation, and maximum frequency deviation; the output data of the renewable energy include new energy output curve and unit output cost. The details are shown in the following table:

[0062] Table 2 System frequency parameters and renewable energy output data that need to be obtained

[0063]

[0064] As a preferred embodiment, the established system deep peak regulation model, total frequency regulation resource metering model and system frequency security model reflect model optimization through constraint conditions.

[0065] Specifically, the constraints of the system deep peak-shaving model include:

[0066] Deep peak load regulation output balance constraints: In the formula, Indicates the actual output of new energy, Indicates the charging power received by the energy storage from the new energy source, The amount of new energy power discarded, Contribute to new energy forecasting.

[0067] The constraints of the total frequency modulation resource measurement model include:

[0068] Total inertia resource constraints of thermal power units:

[0069] Total frequency regulation resource constraints of thermal power units:

[0070] Total frequency regulation resource constraints of energy storage:

[0071] in, Represents the total inertia resource capacity provided by the thermal power generation units. is a 0-1 variable, representing the operating status variable of unit u, represents the inertia coefficient of thermal power unit u, Indicates the maximum output of unit u; It represents the total frequency regulation resource capacity provided by the thermal power generation units; Indicates the total frequency regulation resource capacity provided by the energy storage collection.

[0072] The constraints of the system frequency security model include:

[0073] Maximum frequency change rate constraint:

[0074] Maximum quasi-steady-state frequency deviation constraint:

[0075] Frequency minimum point constraint:

[0076] in, Indicates the system load power shortage, ROCOF max Indicates the maximum limit of the system frequency change rate; Δ Indicates the maximum limit of the system's quasi-steady-state frequency deviation, f D represents the damping coefficient of the system, represents the total load of the system at time t; Δf max Indicates the maximum frequency deviation of the system, Represents the total inertia resource capacity of the system, T e It represents the time constant of frequency regulation resources provided by energy storage, T g Indicates the time constant of the thermal power unit providing frequency regulation resources.

[0077] As a preferred embodiment, the energy storage system operation model includes:

[0078] Charging power constraints:

[0079] Discharge power constraint:

[0080] State of charge timing constraints:

[0081] State of charge upper and lower limit constraints:

[0082] Consistent charge control:

[0083] in, For energy storage, the charging power constraint specifies the upper and lower limits of the energy storage charging and discharging power. They represent the charging and discharging power of the energy storage element s at time t, They represent the upper limit of the charge and discharge power of the energy storage element s respectively; the state of charge timing constraint reflects the relationship between the state of charge of the energy storage during the charge and discharge process and the charge and discharge power as well as the state of charge at the previous moment, and is affected by the charge and discharge efficiency. s,t represents the charge state of the selected energy storage element s at time t, η c , η d They represent the charging efficiency and discharging efficiency of energy storage respectively; the upper and lower limits of the state of charge restrict the state of charge to a reasonable range. Soc s They represent the upper and lower limits of the state of charge of the energy storage element s respectively; the initial and final charge consistency constraint is a constraint set for the sustainable utilization of energy storage in each scheduling cycle. They respectively represent the charge state of the energy storage element at the beginning and end of the scheduling cycle.

[0084] In addition, the thermal power unit operation model includes:

[0085] Unit active output constraints:

[0086] Reactive power output constraints of the unit:

[0087] Unit climbing constraints:

[0088] Minimum online and offline time constraints for units:

[0089] Logical constraints on unit switch action and online status:

[0090] in, For the unit collection, They represent the upper and lower limits of the active output of unit u at time t, They represent the upper and lower limits of reactive power output of unit u at time t, RU u , RD u They represent the upper and lower climbing power limits of unit u respectively. It is a 0-1 variable, indicating the status of the unit in use: 1 means in use, 0 means out of use, It is a 0-1 variable, which indicates the start and shutdown of the unit respectively: 1 indicates the action occurs, TU u , TD u It is the minimum online time and minimum offline time of the thermal power unit.

[0091] Finally, the system power flow model adopts second-order cone power flow modeling. Therefore, the system grid operation model includes:

[0092] Node voltage constraints:

[0093] Line flow constraints:

[0094] Power balance constraints:

[0095] Second-order cone relaxation constraints:

[0096] in, is the node set, ε is the branch set, W ii is the auxiliary variable introduced, and is defined as V i is the voltage vector of node i, B ij , G ij is the conductance and susceptance of line ij, Re() represents the real part, Im() represents the imaginary part,

[0097] is the total active and reactive load on node i, is the total active and reactive power generation at node i.

[0098] As a preferred embodiment, the objective function is the sum of the output cost of the new energy station and the unit operation cost:

[0099]

[0100] in, and Respectively represent the collection of new energy stations and thermal power units, represents the actual output of the new energy station k at time t, c w represents the unit cost of renewable energy output, represents the output of unit u at time t, and Respectively represent the unit output cost, no-load cost and startup cost of thermal power units, It is a 0-1 variable, indicating the operating status of the unit, 1 means running, 0 means shutdown, It is a 0-1 variable, indicating the power-on status of the unit, 1 means power-on.

[0101] To summarize, in this embodiment, the parameters of the relevant equipment and systems are substituted into the established power system dispatch model with energy storage that integrates deep peak regulation and frequency and inertia response functions, and the non-convex parts of the model are correspondingly convexly modeled. The entire model belongs to mixed integer second-order cone programming. By solving the second-order cone mixed integer programming, an optimized system dispatch strategy for deep peak regulation and frequency and inertia response functions can be obtained, including an optimized dispatch strategy for thermal power units and an optimized dispatch strategy for energy storage equipment.

[0102] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0103] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0104] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0105] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for deep peak regulation of a power system in combination with an energy storage system to assist thermal power units in providing rapid frequency response, characterized in that: include: Import thermal power unit system parameters, energy storage system parameters, system frequency parameters and renewable energy output data; Establish a model where the energy storage system provides rapid frequency regulation and the thermal power unit provides inertia resources and frequency regulation resources; Establish system deep peak regulation model, total frequency regulation resource measurement model and system frequency security model; Supplement the energy storage system operation model, thermal power unit operation model and system grid operation model; Set up the objective function and build a power system dispatch model with energy storage that integrates deep peak regulation and frequency and inertia response functions; The power system dispatch model is solved to obtain the dispatch strategy of thermal power units and energy storage dispatch strategy.

2. The method for deep peak regulation of a power system in combination with an energy storage system to assist a thermal power unit to provide a fast frequency response according to claim 1, characterized in that: The thermal power unit system parameters include the maximum output of the unit, the minimum output of the unit, the maximum climbing power, the minimum online / offline time, the output cost, the startup cost, the inertia coefficient and the maximum frequency regulation capacity; The energy storage device parameters include energy storage device capacity, maximum output, charging efficiency and discharging efficiency.

3. The method for deep peak regulation of a power system in combination with an energy storage system to assist a thermal power unit to provide a fast frequency response according to claim 1, characterized in that: The fast frequency regulation model provided by the energy storage system, the inertia resource model provided by the thermal power unit and the frequency regulation resource model are established to embody model optimization through constraint conditions.

4. The method for deep peak regulation of a power system in combination with an energy storage system to assist a thermal power unit to provide a fast frequency response according to claim 3, characterized in that: The constraints of the fast frequency regulation model include upper and lower limit constraints of energy storage frequency regulation reserve and energy storage frequency regulation energy reserve constraints; The constraints of the inertia resource model include inertia reserve constraints of thermal power units; The constraints of the frequency resource model include the frequency reserve power constraint of the thermal power unit and the output upper limit constraint of the unit reserve power.

5. The method for deep peak regulation of a power system in combination with an energy storage system to assist a thermal power unit to provide a fast frequency response according to claim 1, characterized in that: The system frequency parameters include system rated frequency, damping coefficient, maximum frequency change rate, maximum quasi-steady-state frequency deviation, and maximum frequency deviation; The output data of the renewable energy includes a new energy output curve and a unit output cost.

6. The method for deep peak regulation of a power system in combination with an energy storage system to assist a thermal power unit to provide a fast frequency response according to claim 1, characterized in that: The established system deep peak regulation model, total frequency regulation resource metering model and system frequency security model reflect model optimization through constraint conditions.

7. The method for deep peak regulation of a power system in combination with an energy storage system to assist a thermal power unit to provide a fast frequency response according to claim 6, characterized in that: The constraint conditions of the system deep peak-shaving model include deep peak-shaving output balance constraint; The constraints of the total frequency regulation resource metering model include the total inertia resource constraints of thermal power units, the total frequency regulation resource constraints of thermal power units, and the total frequency regulation resource constraints of energy storage; The constraints of the system frequency safety model include a maximum frequency change rate constraint, a maximum quasi-steady-state frequency deviation constraint, and a frequency minimum point constraint.

8. The method for deep peak regulation of a power system in combination with an energy storage system to assist a thermal power unit to provide a fast frequency response according to claim 1, characterized in that: The energy storage system operation model includes charging power constraints, discharging power constraints, state of charge timing constraints, state of charge upper and lower limit constraints, and initial and final charge consistency constraints; The thermal power unit operation model includes unit active output constraints, unit reactive output constraints, unit ramp constraints, unit minimum online and offline time constraints, and unit switch action and online state logic constraints; The system grid operation model includes node voltage constraints, line flow constraints, power balance constraints and second-order cone relaxation constraints.

9. The method for deep peak regulation of a power system in combination with an energy storage system to assist a thermal power unit to provide a fast frequency response according to claim 1, characterized in that: The objective function is the sum of the output cost of the new energy station and the operating cost of the unit.

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