Large-scale energy storage frequency modulation optimization control method based on multi-scale collaboration

Abstract

The invention provides a large-scale energy storage frequency modulation optimization control method based on multi-scale collaboration, and relates to the technical field of power market and power system optimization operation, the method comprises the steps that operation parameters and system state information of an energy storage system are acquired, and the energy storage system comprises energy storage units of different types and different capacities; according to the obtained operation parameters, a multi-time-scale collaborative optimization target function is constructed, and the target function comprises a system frequency modulation performance index, energy storage operation cost and equipment life loss; according to the acquired operation parameters, constructing constraint conditions including basic operation constraints of the energy storage units and uncertainty-oriented distributed cooperative control constraints; and constructing a multi-scale dynamic optimization model according to the obtained system state information and the constructed objective function and constraint conditions, and realizing real-time evaluation and optimal configuration of the energy storage frequency modulation capability. According to the scheme, the frequency modulation potential of the energy storage system is brought into full play, and the frequency regulation capability of the power grid is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power market and power system optimization operation, and in particular to a large-scale energy storage frequency optimization control method and energy storage system based on multi-scale collaboration. Background Art

[0002] As the scale of renewable energy integration continues to expand, power system frequency fluctuations are intensifying, placing higher demands on the system's frequency regulation capabilities. The randomness, volatility, and intermittent nature of renewable energy output make traditional frequency regulation methods difficult to meet the requirements for safe and stable grid operation.

[0003] From the perspective of system frequency regulation performance, random fluctuations in renewable energy sources can lead to increased system frequency deviations. Traditional frequency regulation resources have slow response times and limited regulation capabilities, making them unable to cope with rapidly changing frequency disturbances. Insufficient system frequency regulation capabilities can lead to frequency violations, necessitating emergency measures such as limiting renewable energy output or load shedding.

[0004] From the perspective of system reliability, frequent frequency regulation increases wear and tear on conventional units, shortens equipment lifespan, and increases system operating costs. Furthermore, the dispersed and heterogeneous nature of frequency regulation resources complicates coordinated control and reduces overall system frequency regulation efficiency. Therefore, it is necessary to establish an efficient mechanism for collaborative optimization of frequency regulation resources.

[0005] Energy storage systems offer fast response, high regulation accuracy, and strong bidirectional regulation capabilities, making them ideal frequency regulation resources. In particular, energy storage systems can simultaneously provide millisecond-level rapid response, sub-second frequency regulation, and minute-level AGC frequency regulation services for multi-timescale frequency regulation, making them the optimal choice for enhancing system frequency regulation capabilities. Therefore, it is necessary to conduct research on large-scale energy storage frequency regulation optimization control based on multi-scale collaboration to fully leverage the frequency regulation potential of energy storage systems and enhance the grid's frequency regulation capabilities. Summary of the Invention

[0006] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0007] To this end, the first purpose of this application is to propose a large-scale energy storage frequency regulation optimization control method based on multi-scale collaboration, starting from the system frequency regulation control architecture, aiming at optimizing the system frequency regulation performance, taking into account the rapid response and precise control characteristics of the energy storage system, and considering the capacity and power constraints of the energy storage itself, so that it can coordinate and cooperate at different time scales, jointly deal with the frequency fluctuations and uncertainties brought about by the access of new energy to the power system, improve the system frequency regulation capability, and tap the frequency regulation potential of the energy storage system.

[0008] The second objective of this application is to provide an energy storage system.

[0009] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a large-scale energy storage frequency regulation optimization control method based on multi-scale collaboration, including: obtaining the operating parameters and system status information of the energy storage system, wherein the energy storage system includes energy storage units of different types and capacities; constructing a multi-time scale collaborative optimization objective function based on the obtained operating parameters, wherein the objective function includes system frequency regulation performance indicators, energy storage operating costs and equipment life loss; constructing constraints based on the obtained operating parameters, wherein the constraints include basic operating constraints of the energy storage unit and distributed collaborative control constraints for uncertainty; constructing a multi-scale dynamic optimization model based on the obtained system status information and the constructed objective function and constraints to achieve real-time evaluation and optimal configuration of energy storage frequency regulation capabilities.

[0010] To achieve the above objectives, a second embodiment of the present invention proposes an energy storage system, which adopts the above-mentioned large-scale energy storage frequency regulation optimization control method based on multi-scale collaboration to achieve frequency regulation.

[0011] The large-scale energy storage frequency regulation optimization control method and energy storage system based on multi-scale collaboration in the embodiments of the present application aim to optimize system frequency regulation performance. Taking into account the characteristics of the energy storage system, such as fast response speed, high regulation accuracy, and strong bidirectional regulation capability, and considering the capacity and power constraints of the energy storage itself, the method coordinates millisecond-level rapid response, sub-second frequency regulation, and minute-level AGC to jointly address the frequency fluctuations and uncertainties brought about by the integration of new energy sources on the power system, improve the system frequency regulation capability, and tap the frequency regulation potential of the energy storage system. This is also of great significance for improving the frequency regulation performance of the power grid and promoting the consumption of new energy sources.

[0012] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0014] Figure 1 This is a flow chart of a large-scale energy storage frequency regulation optimization control method based on multi-scale collaboration provided in Example 1 of the present application. DETAILED DESCRIPTION

[0015] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0016] The following describes, with reference to the accompanying drawings, a large-scale energy storage frequency regulation optimization control method and an energy storage system based on multi-scale collaboration according to an embodiment of the present application.

[0017] Figure 1 This is a flow chart of a large-scale energy storage frequency regulation optimization control method based on multi-scale collaboration provided in Example 1 of the present application.

[0018] like Figure 1 As shown, the large-scale energy storage frequency regulation optimization control method based on multi-scale collaboration includes the following steps:

[0019] Step 101: Acquire operating parameters and system status information of an energy storage system, wherein the energy storage system includes energy storage units of different types and capacities;

[0020] Step 102: constructing a multi-time-scale collaborative optimization objective function based on the acquired operating parameters, wherein the objective function includes system frequency regulation performance indicators, energy storage operating costs, and equipment life loss;

[0021] In this embodiment, the objective function of the multi-time-scale collaborative optimization is constructed as follows:

[0022] minF=ω1F freq +ω2F cost +ω3F deg +ω4F couple

[0023] Among them, ω is the weight coefficient, ω1 represents the weight coefficient of frequency control performance, ω2 represents the weight coefficient of economic cost, ω3 represents the weight coefficient of life loss, ω4 represents the weight coefficient of coupling term, F freq is the system frequency control performance index, including frequency deviation, frequency change rate, etc., expressed as:

[0024]

[0025] T represents the optimization period, α is the weight coefficient, Δf t Indicates the frequency deviation at time t, ROCOF t represents the frequency change rate at time t,

[0026] F cost The operating cost of the energy storage system, including charging and discharging costs, maintenance costs, etc., is expressed as:

[0027]

[0028] N represents the number of energy storage units, P ch,i,t 、P dis,i,t They represent the charging and discharging power of energy storage i at time t, cch 、c dis represents the charge and discharge cost coefficient, c om represents the operation and maintenance cost coefficient, P i,t Indicates the actual output power,

[0029] F deg Represents life loss, expressed as:

[0030]

[0031] β represents the weight coefficient, DOD i,t Indicates the depth of discharge, ΔP i,t Indicates the power change rate, Temp i,t Indicates operating temperature, T ref represents the reference temperature,

[0032] F couple It represents the coordination between different time scale controls, which is related to the charge and discharge depth, cycle number, etc., and is expressed as:

[0033]

[0034] K represents the number of time scales, γ k represents the coordination coefficient, P k represents the power command of k scale, P j represents the power instruction of scale j, Ω k Represents the set of sub-time periods corresponding to the k scale.

[0035] Step 103: constructing constraint conditions based on the acquired operating parameters, wherein the constraint conditions include basic operating constraints of the energy storage unit and distributed collaborative control constraints facing uncertainty;

[0036] In this embodiment, the basic operating constraints of the energy storage unit include:

[0037] Charge and discharge power limit constraints:

[0038]

[0039] State of charge constraints:

[0040]

[0041] Ramp rate constraint:

[0042]

[0043] Charge and discharge mutual exclusion constraints;

[0044]

[0045] Where: P i,t represents the power of energy storage unit i at time t (charging is negative, discharging is positive); Indicates the maximum charging power; Indicates the maximum discharge power; N indicates the energy storage unit set; T indicates the time period set; SOC i,t Indicates the state of charge of energy storage unit i at time t; SOC i,min , SOC i,max Indicates the minimum and maximum state of charge limits; η ch ,η dis Indicates the charge and discharge efficiency; E i represents the rated capacity of energy storage unit i; Δt represents the time interval; Indicates the ramp rate limit; Indicates the down-slope rate limit; u i.t Represents a 0-1 variable, indicating the charge and discharge status (0 for discharge, 1 for charge).

[0046] In this embodiment, the uncertainty-oriented distributed collaborative control constraints include:

[0047] Regional autonomous control constraints:

[0048]

[0049] Neighborhood collaborative optimization constraints:

[0050]

[0051] Global objective coordination constraints:

[0052]

[0053] Uncertainty constraints:

[0054]

[0055] Where: Ω m represents the energy storage unit set in region m; P i,t Represents the output power of energy storage unit i; ΔP m,t represents the power deviation of region m; represents the reference power of region m; M represents the region set; P mn,t represents the power exchange from region m to region n; Indicates the maximum switching power limit; x m,t represents the state variable of region m; N m Represents the neighborhood set of region m; ∈ mn Indicates the allowable state deviation; Δf sys,t Indicates the system frequency deviation; represents the global average state; ∈ sys represents the allowable deviation of the system; ξ represents the uncertain parameter; Ξ represents the uncertainty set; α represents the confidence level.

[0056] Step 104 : Based on the acquired system status information and the constructed objective function and constraints, a multi-scale dynamic optimization model is constructed to achieve real-time evaluation and optimal configuration of the energy storage frequency regulation capability.

[0057] In this embodiment, the multi-scale dynamic optimization model constructed includes:

[0058]

[0059] in: Indicates millisecond-level response power; Indicates the second-level frequency modulation power; Indicates AGC power instruction; K p,i Indicates the proportional coefficient; K d,i represents the differential coefficient; Δf t Indicates frequency deviation; R i Indicates rate regulation; Indicates initial power; ACE t represents the regional control error; α i represents the participation factor; β represents the deviation integral coefficient.

[0060] The large-scale energy storage frequency regulation optimization control method based on multi-scale collaboration in the embodiment of the present application aims to optimize system frequency regulation performance. It takes into account the characteristics of the energy storage system, such as fast response speed, high regulation accuracy, and strong bidirectional regulation capability, and considers the capacity and power constraints of the energy storage itself. Through millisecond-level rapid response, second-level frequency regulation, and minute-level AGC coordination, it jointly addresses the frequency fluctuations and uncertainties brought about by the integration of new energy sources on the power system, improves the system frequency regulation capability, and taps the frequency regulation potential of the energy storage system. It is also of great significance for improving the frequency regulation performance of the power grid and promoting the consumption of new energy sources.

[0061] In order to implement the above embodiments, the present invention further proposes an energy storage system, which implements frequency modulation using the method described in the above embodiments.

[0062] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0063] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0064] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0065] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0066] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0067] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0068] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0069] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A large-scale energy storage frequency modulation optimization control method based on multi-scale collaboration, characterized in that: include: Acquiring operating parameters and system status information of an energy storage system, wherein the energy storage system includes energy storage units of different types and capacities; Based on the acquired operating parameters, construct an objective function for multi-time-scale collaborative optimization, wherein the objective function includes system frequency regulation performance indicators, energy storage operating costs, and equipment life loss; Constructing constraint conditions based on the acquired operating parameters, wherein the constraint conditions include basic operating constraints of the energy storage unit and distributed collaborative control constraints facing uncertainty; Based on the acquired system status information and the constructed objective functions and constraints, a multi-scale dynamic optimization model is constructed to achieve real-time evaluation and optimal configuration of energy storage frequency regulation capabilities.

2. The method according to claim 1, wherein The objective function includes: minF=ω1F freq +ω2F cost +ω3F deg +ω4F couple Among them, ω is the weight coefficient, F freq is the frequency control performance, expressed as: T represents the optimization period, α is the weight coefficient, Δf t Indicates the frequency deviation at time t, ROCOF t represents the frequency change rate at time t, F cost represents the operating cost of the energy storage system, which is expressed as: N represents the number of energy storage units, P ch,i,t 、P dis,i,t They represent the charging and discharging power of energy storage i at time t, c ch 、c dis represents the charge and discharge cost coefficient, c om represents the operation and maintenance cost coefficient, P i,t Indicates the actual output power, F deg Represents life loss, expressed as: β represents the weight coefficient, DOD i,t Indicates the depth of discharge, ΔP i,t Indicates the power change rate, Temp i,t Indicates operating temperature, T ref represents the reference temperature, F couple It represents the coordination between different time scale controls and is expressed as: K represents the number of time scales, γ k represents the coordination coefficient, P k represents the power command of k scale, P j represents the power instruction of scale j, Ω k Represents the set of sub-time periods corresponding to the k scale.

3. The method according to claim 1, wherein The basic operating constraints of the energy storage unit include charge and discharge power limit constraints, state of charge constraints, ramp rate constraints, and charge and discharge mutual exclusion constraints.

4. The method according to claim 3, wherein The charge and discharge power limit constraints are: The state of charge constraint is: The ramp rate constraint is: The charge and discharge mutual exclusion constraint is: Among them, P i,t represents the power of energy storage unit i at time t, N represents the set of energy storage units, T is the optimization period, Represents the maximum charging power and maximum discharging power, SOC i,t Indicates the state of charge of energy storage unit i at time t, SOC i,min , SOC i,max Represent the maximum and minimum state of charge limits, η ch ,η dis Indicates the charge and discharge efficiency, E i represents the rated capacity of energy storage unit i, Respectively represent the upper and lower climbing rate limits, u i.t Represents a 0-1 variable, 0 represents the discharge state, and 1 represents the charge state.

5. The method according to claim 1, wherein The uncertainty-oriented distributed collaborative control constraints include regional autonomous control constraints, neighborhood collaborative optimization constraints, global target coordination constraints and uncertainty constraints.

6. The method according to claim 1, wherein The regional autonomous control constraints are: The neighborhood collaborative optimization constraints are: The global objective coordination constraint is: The uncertainty constraints are: Where i represents the energy storage unit i, m and n are regions, t is the time, ξ represents the uncertain parameter, M represents the region set, N m represents the neighborhood set of region m, T represents the optimization period, Ξ is the uncertainty set, Ω m is the set of energy storage units in region m, P i,t represents the power of energy storage unit i at time t, ΔP m,t represents the power deviation of region m, represents the reference power of region m, Δf m,t represents the frequency deviation of region m, represents the maximum frequency deviation of region m, P mn,t represents the power exchange from region m to region n, Indicates the maximum power exchange limit, x m,t represents the state variable of region m, ∈ mn Indicates the allowable state deviation, Δf sys,t Indicates the system frequency deviation, represents the global average state, ∈ sys Indicates the system allowable deviation, represents the minimum and maximum power of energy storage unit i at time t under uncertain parameters ξ.

7. The method according to claim 1, wherein The real-time evaluation and optimization configuration of the energy storage frequency regulation capability includes millisecond-level fast response strategy, second-level frequency regulation control and minute-level AGC coordinated optimization. In the multi-scale dynamic optimization model, the power P of energy storage unit i at time t is i,t for: in, Indicates the millisecond-level response power, expressed as: K p,i Represents the proportionality coefficient, K d,i represents the differential coefficient, Δf t Indicates the frequency deviation, Indicates the second-level frequency modulation power, expressed as: R i Indicates the rate regulation, represents the initial power, Indicates the AGC power instruction, expressed as: α i represents participation factor, β represents deviation integral coefficient, ACE t Represents the regional control error.

8. An energy storage system, characterized in that: The system implements frequency regulation by adopting the large-scale energy storage frequency regulation optimization control method based on multi-scale collaboration as described in claims 1-7.

Images