A hierarchical coordination control method and device for a hybrid energy storage system

Abstract

The application discloses a layered coordination control method and equipment of a hybrid energy storage system, relates to the field of power regulation, and comprises a day-ahead peak regulation step, a real-time frequency regulation step and an SOC recovery step. The day-ahead peak regulation step comprises the following steps: determining a target load curve of a power grid, determining a peak regulation demand according to the target load curve, and calling at least part of the capacity of an energy type energy storage battery energy storage system according to the peak regulation demand to realize peak regulation operation on the power grid system. The real-time frequency regulation step comprises the following steps: calling the remaining capacity of the energy type energy storage battery energy storage system, a power type energy storage flywheel energy storage system and a photovoltaic system in real time to realize frequency regulation operation on the power grid system. The SOC recovery step comprises the following step: realizing SOC recovery operation on the hybrid energy storage system by coordinating power exchange between the hybrid energy storage system and the power grid system. Systematic coupling modeling between multiple types of energy storage resources and multiple power grid service tasks is realized.

Description

Technical Field

[0001] This application relates to the field of power regulation, and in particular to a hierarchical coordinated control method and device for a hybrid energy storage system. Background Technology

[0002] The penetration rate of new energy power generation in the power system continues to rise, especially with the widespread deployment of photovoltaic power generation and energy storage systems, which has promoted the deep decarbonization and intelligent development of the power system. However, the randomness and intermittency of new energy output pose a severe challenge to the operational stability of the power grid. To achieve multi-energy synergy among power generation, grid, load, and storage, more and more research focuses on the joint optimization and regulation of photovoltaic and energy storage systems to enhance their ability to participate in grid operation support services.

[0003] Currently, flexible load resources such as battery energy storage systems (BESS), flywheel energy storage systems (FESS), and electric vehicles (EVs) play an important role in ancillary services, peak shaving and valley filling, and emergency response. However, in practical applications, existing energy storage regulation methods generally have the following problems: (1) Most regulation methods are only oriented towards a single energy storage technology or a single service objective, and lack systematic coupling modeling between multiple types of energy storage resources and multiple grid service tasks (such as peak shaving, frequency regulation, and SOC management); (2) Regulation methods mostly adopt centralized optimization methods, ignoring the control conflicts and resource coordination problems caused by the differences in system response characteristics under multiple time scales; (3) In scenarios where electric vehicles have a high proportion of access, existing regulation methods often fail to fully consider the dynamic trade-off between their rigid travel demand and scheduling flexibility, resulting in the load regulation capacity not being effectively explored.

[0004] At least for the target regulation method, there is a lack of systematic coupling modeling between multiple types of energy storage resources and multiple grid service tasks (such as peak shaving, frequency regulation, and SOC management), and a solution is needed. Summary of the Invention

[0005] The purpose of this application is to provide a hierarchical coordinated control method and device for hybrid energy storage systems, which can realize the systematic coupling modeling between multiple types of energy storage resources (photovoltaic systems, energy-type battery energy storage systems, and power-type flywheel energy storage systems) and multiple grid service tasks (peak shaving, frequency regulation, and SOC recovery), and solve the problem that the target control method lacks systematic coupling modeling between multiple types of energy storage resources and multiple grid service tasks.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] In a first aspect, this application provides a hierarchical coordinated control method for a hybrid energy storage system, wherein the hybrid energy storage system includes a photovoltaic system, an energy-type battery energy storage system, and a power-type flywheel energy storage system;

[0008] The hierarchical coordinated control method includes a day-ahead peak shaving step, a real-time frequency modulation step, and a SOC recovery step.

[0009] The daytime peak shaving steps include:

[0010] Determine the target load curve of the power grid, determine the peak shaving demand based on the target load curve, and call at least part of the capacity of the energy storage battery system to realize the peak shaving operation of the power grid system based on the peak shaving demand.

[0011] The real-time frequency modulation step includes:

[0012] The remaining capacity of the energy-type battery energy storage system, the power-type flywheel energy storage system, and the photovoltaic system are used in real time to achieve frequency regulation of the power grid system;

[0013] The SOC recovery steps include:

[0014] The SOC recovery operation of the hybrid energy storage system is achieved by coordinating the power exchange between the hybrid energy storage system and the power grid system.

[0015] Secondly, this application provides a hierarchical coordination control device for a hybrid energy storage system, wherein the hybrid energy storage system includes a photovoltaic system, an energy-type battery energy storage system and a power-type flywheel energy storage system, and the hierarchical coordination control device includes a day-ahead peak shaving module, a real-time frequency modulation module and a SOC recovery module.

[0016] The current peak shaving module is used to: determine the target load curve of the power grid, determine the peak shaving demand based on the target load curve, and call at least part of the capacity of the energy storage battery system to realize the peak shaving operation of the power grid system based on the peak shaving demand;

[0017] The real-time frequency regulation module is used to: call upon the remaining capacity of the energy-type battery energy storage system, the power-type flywheel energy storage system, and the photovoltaic system in real time to achieve frequency regulation operation on the power grid system;

[0018] The SOC recovery module is used to: perform SOC recovery operation on the hybrid energy storage system by coordinating the power exchange between the hybrid energy storage system and the power grid system.

[0019] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the hierarchical coordinated control method for the hybrid energy storage system described in any one of the above.

[0020] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the hierarchical coordinated control method for the hybrid energy storage system described above.

[0021] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the hierarchical coordinated control method for the hybrid energy storage system described above.

[0022] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0023] This application provides a hierarchical coordinated control method and device for a hybrid energy storage system. Step A establishes a three-layer coordinated control architecture: day-ahead peak shaving, real-time frequency regulation, and State of Charge (SOC) recovery. Specifically, the battery energy storage system (BESS) prioritizes peak shaving (PS), with its idle capacity dynamically reused in the middle layer for frequency regulation (FR). The flywheel energy storage system (FESS) focuses on high-frequency frequency regulation, achieving a clear division of roles and efficient time-domain reuse for ancillary services. The day-ahead peak shaving step is executed one day before the target date, while the real-time frequency regulation and SOC recovery steps are performed in real-time on the target date. The priority participation of the battery energy storage system in peak shaving, with its idle capacity dynamically reused in the middle layer for frequency regulation, fully utilizes the battery energy storage system. Furthermore, the real-time frequency regulation utilizes the idle capacity of the battery energy storage system and does not affect peak shaving. Therefore, this hierarchical coordinated control method can fully exploit the regulation potential of battery energy storage, flywheel energy storage, and flexible loads from electric vehicles at different time scales. Unlike traditional methods that rely on centralized optimization or single energy storage resources, this invention adopts a hierarchical rolling optimization strategy. While ensuring frequency stability, it achieves synergistic optimization of system economy and energy storage health. It realizes systematic coupling modeling between multiple types of energy storage resources (photovoltaic systems, energy-type battery energy storage systems, and power-type flywheel energy storage systems) and multiple grid service tasks (peak shaving, frequency regulation, and SOC recovery), solving the problem that target control methods lack systematic coupling modeling between multiple types of energy storage resources and multiple grid service tasks. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1This is an application environment diagram of a hierarchical coordinated control method for a hybrid energy storage system according to an embodiment of this application;

[0026] Figure 2 A schematic flowchart illustrating a hierarchical coordinated control method for a hybrid energy storage system provided in an embodiment of this application;

[0027] Figure 3 A schematic diagram of a day-ahead peak shaving step provided for an embodiment of this application;

[0028] Figure 4 A schematic diagram illustrating a real-time frequency modulation step according to an embodiment of this application;

[0029] Figure 5 A schematic diagram illustrating a SOC recovery step according to an embodiment of this application;

[0030] Figure 6 This is a topology diagram of an IEEE 9-node power system in an example scenario of this application;

[0031] Figure 7 This is a schematic diagram illustrating power and electricity price in an example scenario of this application;

[0032] Figure 8 This is a diagram showing the peak-shaving results of the previous peak-shaving step in one embodiment of this application;

[0033] Figure 9 The figure shows the simulation results of the hierarchical coordinated control method of the hybrid energy storage system in the embodiments of this application in a residential area regarding frequency deviation.

[0034] Figure 10 The figure shows the simulation results of the hierarchical coordinated control method of the hybrid energy storage system in the embodiment of this application regarding power commands in a residential area;

[0035] Figure 11 The figure shows the simulation results of the hierarchical coordinated control method of the hybrid energy storage system in the embodiments of this application with respect to the SOC of battery energy storage in a residential area;

[0036] Figure 12 The figure shows the simulation results of the hierarchical coordinated control method of the hybrid energy storage system in the embodiments of this application with respect to the SOC of flywheel energy storage in a residential area;

[0037] Figure 13 The figure shows the simulation results of the hierarchical coordinated control method of the hybrid energy storage system in the embodiment of this application in a commercial area with respect to frequency deviation.

[0038] Figure 14 The figure shows the simulation results of the hierarchical coordinated control method of the hybrid energy storage system in the embodiment of this application in terms of power command in a commercial area;

[0039] Figure 15 The figure shows the simulation results of the hierarchical coordinated control method of the hybrid energy storage system in the embodiment of this application in terms of the SOC of battery energy storage in a commercial area;

[0040] Figure 16 The figure shows the simulation results of the hierarchical coordinated control method of the hybrid energy storage system in the embodiment of this application, with respect to the SOC of flywheel energy storage in a commercial area. Detailed Implementation

[0041] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] The hierarchical coordinated control method for hybrid energy storage systems provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be set up independently, integrated into server 102, or placed in the cloud or on other servers. Furthermore, in some embodiments, the hierarchical coordination control method of the hybrid energy storage system can also be implemented independently by server 102 or terminal 101.

[0044] The terminal 101 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, and IoT devices. The server 102 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0045] In one exemplary embodiment, such as Figure 2 As shown, a hierarchical coordinated control method for a hybrid energy storage system is provided. This method is executed by computer equipment, which can be executed by a computer device such as a terminal or server alone, or by a terminal and server together.

[0046] In this embodiment of the application, the method is applied to Figure 1Taking server 102 as an example, the hybrid energy storage system includes photovoltaic systems, energy-type battery energy storage systems, and power-type flywheel energy storage systems. The hierarchical coordinated control method includes day-ahead peak shaving steps, real-time frequency regulation steps, and SOC (State of Charge) recovery steps.

[0047] The current peak shaving steps include: determining the target load curve of the power grid, determining the peak shaving demand based on the target load curve, and calling at least part of the capacity of the energy storage battery system to realize the peak shaving operation of the power grid system based on the peak shaving demand.

[0048] The real-time frequency regulation steps include: real-time mobilization of the remaining capacity of energy-type battery energy storage systems, power-type flywheel energy storage systems, and photovoltaic systems to achieve frequency regulation operations on the power grid system;

[0049] The SOC recovery steps include: achieving SOC recovery of the hybrid energy storage system by coordinating power exchange between the hybrid energy storage system and the grid system.

[0050] In the aforementioned hierarchical coordinated control method, a three-layer coordinated control architecture of "day-ahead peak shaving – real-time frequency regulation – SOC recovery" is constructed. Specifically, battery energy storage systems (BESS) prioritize peak shaving (PS), and their idle capacity is dynamically reused in the middle layer for frequency regulation (FR). Power energy storage flywheel energy storage systems (FESS) focus on high-frequency frequency regulation tasks, achieving a clear division of roles among energy storage systems and an efficient time-domain reuse ancillary service strategy. The day-ahead peak shaving step is executed one day before the target date, while the real-time frequency regulation and SOC recovery steps are performed in real-time on the target date. The priority participation of battery energy storage systems in peak shaving, with their idle capacity dynamically reused in the middle layer for frequency regulation, ensures full utilization of battery energy storage systems. Furthermore, real-time frequency regulation utilizes the idle capacity of battery energy storage systems and does not affect peak shaving. Therefore, this hierarchical coordinated control method can fully explore the regulation potential of battery energy storage, flywheel energy storage, and flexible loads of electric vehicles at different time scales. Unlike traditional methods that rely on centralized optimization or single energy storage resources, this invention adopts a hierarchical rolling optimization strategy. While ensuring frequency stability, it achieves synergistic optimization of system economy and energy storage health. It realizes systematic coupling modeling between multiple types of energy storage resources (photovoltaic systems, energy-type battery energy storage systems, and power-type flywheel energy storage systems) and multiple grid service tasks (peak shaving, frequency regulation, and SOC recovery), solving the problem that target control methods lack systematic coupling modeling between multiple types of energy storage resources and multiple grid service tasks.

[0051] Reference Figure 3 The following is a detailed explanation of the daytime peak shaving steps.

[0052] In this embodiment, determining the target load curve of the power grid includes: constructing a first arrival time distribution and a second arrival time distribution of the electric vehicle cluster using a piecewise Gaussian distribution, wherein the first arrival time distribution is the arrival time distribution under home charging mode and the second arrival time distribution is the arrival time distribution under public charging mode; determining the initial charging demand curve of the electric vehicle cluster based on the first arrival time distribution and the second arrival time distribution; and determining the target load curve based on the initial charging demand curve.

[0053] Specifically, determining the target load curve based on the initial charging demand curve includes: optimizing the charging power of a portion of the electric vehicles in the electric vehicle cluster according to a preset ratio, thereby optimizing the initial charging demand curve to obtain the target charging demand curve, with the goal of minimizing the total charging cost of electric vehicles; and combining the target charging demand curve with the electricity demand curve of non-electric vehicle clusters to obtain the target load curve.

[0054] Based on the arrival time distribution, the number of electric vehicles that have started charging at each time point can be determined. Combined with the charging time of each electric vehicle, the number of electric vehicles currently charging at each time point can be determined. Furthermore, by considering the charging power of each electric vehicle, the total charging power of the electric vehicles at each time point can be determined. The specific process is as follows:

[0055] 1. Optimization is performed on flexible loads represented by electric vehicles (EVs). EV charging can be categorized into two common charging modes: home charging and public charging. In home charging, users typically start charging after returning home in the evening and stop charging before their commute the next day. In public charging, EV owners start charging upon arriving at work in the morning and stop charging after leaving get off work. To accurately characterize the behavioral features of these two charging modes, the charging start time is modeled using a piecewise Gaussian distribution.

[0056] In home charging mode, the EV arrival time distribution (first arrival time distribution) is as follows:

[0057]

[0058] Among them, parameters μ s =18, parameter σ s =3.3.

[0059] In public charging mode, the EV arrival time distribution (second arrival time distribution) is as follows:

[0060]

[0061] in, μ 2s =8.5, σ2s =3.3.

[0062] In the above arrival time distribution, f t ( x () indicates the arrival time is x The proportion of electric vehicles.

[0063] Based on the two arrival time distributions mentioned above, the number of electric vehicles starting to charge at each time point can be determined. Next, the charging time of the electric vehicles needs to be determined.

[0064] 2. Considering the battery capacity configuration of mainstream models in the market, for example, assume that the EV battery capacity follows a uniform distribution of 20-60kWh:

[0065]

[0066] In the above battery capacity distribution, f c ( x () indicates that the battery capacity is x The proportion of electric vehicles.

[0067] By combining battery capacity and charging speed (characterized by charging power), the charging time distribution of electric vehicles can be determined. For example, if 100 electric vehicles start charging at a certain moment, the battery capacity of these 100 electric vehicles will follow the uniform distribution described above. Then, by combining the charging speed, the charging time distribution of these 100 electric vehicles can be obtained.

[0068] 3. Fast charging mode and smart charging mode are two common charging strategies. Fast charging mode provides a shorter charging time, suitable for users who need to charge immediately. However, fast charging may cause large current and voltage surges to the battery, affecting battery life. Therefore, smart charging mode is provided for users who do not urgently need to charge. This mode charges in a gentler way, thereby extending battery life. This embodiment will optimize the charging power curve of an electric vehicle that selects smart charging mode. The charging power of the electric vehicle is then:

[0069]

[0070] in, quickcharging Indicates fast charging. smartcharging Indicates smart charging. This represents the charging power of the electric vehicle at time t during the nth charging session. P ev,max This represents the maximum charging power of the electric vehicle (in this embodiment, the average maximum charging power of various electric vehicles can be used as a reference based on historical data). P ev,max), This represents the adjustment coefficient of the electric vehicle at time t in the nth charging session. In subsequent charging power optimization processes, the adjustment coefficient of the electric vehicle that selects the smart charging mode will be adjusted to achieve charging power optimization.

[0071] Regarding charging mode selection, users charging at night are more likely to choose smart charging (for example, set to 80%) because they generally don't need to use the vehicle immediately at night, and smart charging can effectively utilize off-peak electricity. Conversely, users charging during the day are less likely to choose smart charging (for example, set to 50%) because they may need to complete charging in a shorter time. Furthermore, considering that some users urgently need to use the vehicle or have strict requirements on charging time, 5% of users can be set to always choose fast charging. It should be noted that during the optimization process, the electric vehicles choosing smart charging mode can be randomly determined based on the above proportions. For example, if 100 electric vehicles start charging at a certain time during the day, 20 of them can be randomly designated to choose smart charging mode, and the remaining 80 to choose fast charging mode. Then, combined with the battery capacity of these 100 vehicles, the charging time for these 100 vehicles can be obtained.

[0072] By following the steps above, we can obtain the total charging demand of the electric vehicle at each time point before optimization, i.e., the initial charging demand curve. For electric vehicles that select the smart charging mode, we can also optimize the charging power to further optimize the initial charging demand curve.

[0073] 4. The charging power optimization model for electric vehicles that select intelligent charging mode is as follows:

[0074]

[0075] in, J 1 represents the charging cost. n Indicates the charging session sequence number. N Indicates the total number of charging sessions. This represents the unit price of electricity at time t. t arr,n and t end,n These represent charging sessions. n The start and end times, Δ t It represents the time interval between two consecutive moments.

[0076] Using the above charging power optimization model, the charging power of some electric vehicles can be optimized. After optimization, the total power of the electric vehicle group (the optimized initial charging demand curve) is:

[0077]

[0078] in, Let t represent the total power of the electric vehicle group at time t.

[0079] The optimized initial charging demand curve and the electricity demand curve of non-electric vehicle clusters can be used to obtain the target load curve of the power grid system. This determines the peak position of the power grid system, allowing for peak shaving during the corresponding time period (assisted by energy storage battery systems to reduce the output power of the power grid system).

[0080] In this embodiment, peak shaving demand includes the output power of the energy storage battery system participating in peak shaving at various times; the peak shaving operation of the power grid system by calling at least a portion of the capacity of the energy storage battery system according to the peak shaving demand includes: optimizing the first charge and discharge operation of the energy storage battery system for the next day based on the peak shaving demand, wherein the optimization of the first charge and discharge operation aims to minimize the operating cost of the energy storage battery system.

[0081] By optimizing the charging and discharging operations of energy storage battery systems at different times, the electricity procurement costs of the grid system during charging periods can be reduced. J 2. Maximize power extraction from the photovoltaic system while meeting peak-shaving requirements during discharge periods. The specific optimization function is as follows:

[0082]

[0083] , and Let $t$ represent the electricity purchase cost, peak-shaving revenue, and battery degradation cost of the energy storage battery system at time $t$. and All are weighting coefficients. This represents the input power that the energy storage battery system obtains from the power grid at time t. K PS This indicates the peak-shaving revenue electricity price. This represents the output power of the energy storage battery system participating in peak shaving at time t. C e It is the unit energy cost of the battery. D It is the depth of discharge. L C It refers to the battery's cycle life. η It refers to the charging / discharging efficiency of energy storage battery systems. It is the input power obtained by the energy storage battery system from the photovoltaic system at time t.

[0084] Through the above optimization, the charging and discharging operations of the energy storage battery system at various times can be determined. The charging and discharging operations include the output power and the input power obtained from the grid system and the photovoltaic system, respectively.

[0085] The above is a complete explanation of the day-ahead peak shaving procedure. The optimization results of the day-ahead peak shaving procedure are as follows: Figure 8 As shown, without a smart charging strategy, the charging demand of electric vehicles is concentrated during peak hours, leading to a significant increase in the system load peak. However, by shifting or dispersing the charging load to off-peak hours through a smart charging strategy, the load peak is suppressed to a certain extent, and the difference between day and night loads is reduced accordingly. Figure 8 In the figure, the SOC curve of the energy storage battery system reflects its charging and discharging behavior during peak shaving. During periods of high renewable energy output, the energy storage battery system charges to prepare for subsequent peak shaving; during peak load periods, it discharges to meet peak shaving commands, thus achieving peak shaving and valley filling. In summary, the synergistic effect of flexible load optimization and the peak shaving strategy of the energy storage battery system not only effectively reduces peak loads but also provides flexible energy storage support for the power grid.

[0086] Reference Figure 4 The following is a detailed explanation of the real-time frequency modulation steps.

[0087] When performing peak-shaving operations, energy storage battery systems may have surplus capacity, which can then participate in real-time frequency regulation. During real-time frequency regulation, the energy storage battery system outputs surplus power to maintain the frequency stability of the power grid. The surplus power limitation of the energy storage battery system can be expressed by the following formula:

[0088] - P B,FR,min ≤ P B,FR,t ≤ P B,FR,max

[0089] in, P B,FR,min and P B,FR,max This represents the maximum power output of an energy storage battery system. P B,FR,t Let be the power of the energy storage battery system at time t.

[0090] To accurately reflect the charging and discharging capabilities of an energy storage battery system during idle periods, its remaining capacity needs to be modeled. During idle periods, the remaining capacity of the energy storage battery system is considered as a virtual energy storage system with power and capacity thresholds, possessing power response capabilities similar to physical energy storage systems. Specifically, the remaining capacity of the energy storage battery system can be modeled based on the following factors: the lower power limit is determined by the minimum remaining power of the energy storage battery system, and the upper power limit is jointly determined by the maximum remaining power of the energy storage battery system, the maximum remaining power based on the upper capacity limit of the energy storage battery system, and the maximum remaining power based on the lower capacity limit of the energy storage battery system.

[0091]

[0092]

[0093] in, P B,n It is the rated power of the energy storage battery system. P B,PS,min and P PS,max These are the minimum and maximum limits for frequency modulation power; C B,rated It is the rated capacity of the energy storage battery system; and cap B,t It represents the remaining capacity of the energy storage battery system at time t. P B,m1 , P B,m2 and P B,m3 These are the first power, the second power, and the third power, respectively.

[0094] In this embodiment, the real-time frequency modulation step is divided into a primary frequency modulation stage and a secondary frequency modulation stage.

[0095] The primary frequency regulation phase involves real-time mobilization of the remaining capacity of the energy-type battery storage system, the power-type flywheel energy storage system, and the photovoltaic system to regulate the frequency of the power grid. Specifically, this includes determining the real-time power changes of the energy-type battery storage system, the power-type flywheel energy storage system, and the photovoltaic system, with the optimization objective of minimizing the differences between each state variable and the target value. These state variables include the frequency deviation, frequency regulation benefit, frequency change rate, and carbon emissions of the hybrid energy storage system, as well as the power changes of the energy-type battery storage system, the power-type flywheel energy storage system, and the photovoltaic system.

[0096] The secondary frequency regulation stage involves real-time activation of AGC units to supplement power based on the power deviation of the hybrid energy storage system.

[0097] In summary, the real-time frequency regulation step employs the MPC (Model Predictive Control) method to leverage the remaining capacity obtained by the battery energy storage through idle time reuse response strategies, working in synergy with photovoltaic and flywheel energy storage to rapidly respond to frequency disturbances and perform initial frequency regulation. In the secondary frequency regulation stage, while maintaining the output power of photovoltaic, battery, and flywheel energy storage constant, an AGC (Automatic Generation Control) generator is introduced to participate in fine-tuning to achieve long-term frequency stability. The modeling process of model predictive control is explained below.

[0098] 1. In the primary frequency regulation (PFR) phase, the state space is first established, and the selected state variables include the frequency deviation Δ. f ( t ), frequency change rate R ocof Frequency modulation revenue C and reduced carbon emissions C ce The selected control variable is the power change Δ of the photovoltaic system. P PV The power change Δ of the energy storage battery system P B The power change Δ of a power-type flywheel energy storage system P F The formula for calculating frequency deviation is:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] in, These are all intermediate parameters. This represents the change in the output power of the power grid system. R It is the reciprocal of the pure gain of a conventional generator speed governor. M The rotor time constant is related to the moment of inertia. D The active frequency response coefficient of the load. T Let be the equivalent inertial time constant of the turbine. aThese are the characteristic coefficients of the turbine. η M The proportion of inertia-free units in a hybrid energy storage system. η R The proportion of generating units that do not participate in frequency regulation.

[0106] Differentiating the frequency deviation yields the rate of frequency change. R ocof :

[0107]

[0108] Meanwhile, the benefits of using hybrid energy storage systems to provide frequency regulation services are also considered as follows:

[0109]

[0110] In the formula, K c It refers to the electricity price for energy storage participating in frequency regulation ancillary services.

[0111] Purchasing energy from the power grid is a significant source of pollutant emissions from energy-type battery storage systems and power-type flywheel energy storage systems. Obtaining electricity from photovoltaic power plants can reduce carbon emissions. The carbon emission reductions achieved by photovoltaics are as follows:

[0112]

[0113] In the formula, C ce It is the lowest carbon emission level. K ce The carbon dioxide emission factor is 0.67 kg / kW.

[0114] When the power system frequency deviates from its rated value, the dispatch center sends AGC (Automatic Generation Control) commands to each power station. Subsequently, the hybrid energy storage system initiates primary frequency regulation support, prioritizing the use of photovoltaic (PV) power to execute the AGC commands. If PV output is insufficient, the power-type flywheel energy storage system uses its rapid response capability to compensate for the remaining shortfall. If the combined capacity of the PV and power-type flywheel energy storage systems is insufficient to provide further support, the energy-type battery energy storage system is activated to ensure that frequency regulation requirements are met.

[0115] Select the state variable as The control quantity is The error between each physical quantity and its expected trajectory is , , , , , , After discretization, the state-space equation for the upper-level primary frequency modulation optimization control can be obtained as follows:

[0116]

[0117] In the formula, the state quantity error Control quantity error , It is the output of the prediction model. The coefficient matrix is ​​as follows:

[0118]

[0119] in, K 1(k) and K 2(k) These are the first frequency modulation revenue coefficient and the second frequency modulation revenue coefficient at time k, respectively.

[0120] Based on state-space equations, rolling optimization modeling is performed. The objective function is to make the actual trajectories of the hybrid energy storage system—frequency deviation, frequency regulation benefit, frequency change rate, carbon emission reduction, and power change of the energy-type battery storage system, power-type flywheel storage system, and photovoltaic system—as close as possible to the expected trajectory. The objective function can be defined as:

[0121]

[0122] In the formula: γ 1. γ 2. γ 3. γ 4. γ 5. γ 6 is the weighting coefficient. T To optimize the total duration of time in the rolling optimization time domain, T =1.0s, d t Step size optimized for rolling, d t =0.1s, k Used to identify discrete moments in the time domain of rolling optimization, Δ f k Δ is the actual frequency deviation at time k. f rk It is the reference frequency deviation at time k, Δ R ocofk Let Δ be the actual rate of change of frequency at time k. R ocofrk It is the rate of change of the reference frequency at time k. C k It is the frequency modulation gain at time k. C rk It is the reference frequency modulation gain at time k.C cek This represents the reduction in carbon emissions at time k. C cerk Δ is the carbon emission reduction referenced at time k. P PVk It is the actual power of the photovoltaic system at time k, Δ P PVrk This is the reference power of the photovoltaic system at time k. Δ P Bk It is the actual power of the energy storage battery system at time k, Δ P Brk This is the reference power of the energy storage battery system at time k. Δ P Fk It is the actual power of the power-type flywheel energy storage system at time k, Δ P Frk It is the reference power of the power-type flywheel energy storage system at time k.

[0123] In solving the problem, the above predictive control problem can be transformed into a standard quadratic programming problem, as follows:

[0124]

[0125] Let k be the predicted power of the photovoltaic system at time k. and This refers to the maximum charge and discharge power of an energy storage battery system participating in frequency regulation. and This refers to the maximum charging and discharging power of a power-type flywheel energy storage system participating in frequency regulation. The rated capacity of the energy storage battery system is [missing information]. The rated capacity of the power-type flywheel energy storage system. The initial state of charge (SOC) of an energy storage battery system. The initial state of charge (SOC) of the power-type flywheel energy storage system. and To add upper and lower limits to the rate of change of power for photovoltaic systems. and The upper and lower limits of the power change rate are added to the energy storage battery system. and The upper and lower limits of the power change rate are added to the power-type flywheel energy storage system.

[0126] 2. During the Second Frequency Regulation (SFR) phase, the photovoltaic system, energy-type battery storage system, and power-type flywheel energy storage system maintain a constant power output to stabilize the grid system frequency at a new equilibrium point. The fixed control time domain set for SFR is 2 minutes. During this period, the photovoltaic system, energy-type battery storage system, and power-type flywheel energy storage system continue to provide frequency regulation support to the grid system, while the AGC units participate in the regulation synchronously according to the system frequency deviation.

[0127]

[0128] In the formula, Δ P AGC (Unit: MW) indicates the additional power required by the AGC unit to compensate for frequency deviations. K ACE This represents the adjustment amount of the area control error (ACE); K AGC and K ACE All are frequency modulation gain coefficients. f 0 (Unit: Hz) is the system's rated frequency. f 0 =50Hz.

[0129] In this embodiment, the SOC recovery operation of the hybrid energy storage system by coordinating the power exchange between the hybrid energy storage system and the power grid system specifically includes: determining the current accumulated SOC change of the hybrid energy storage system; and optimizing the hybrid energy storage system for a second charge and discharge operation for future time, with the goal of minimizing the operating cost of the hybrid energy storage system.

[0130] Reference Figure 4 The following is a detailed explanation of the SOC recovery steps.

[0131] Specifically, within the lower-level optimization time domain, the SOC changes caused by the middle-level FR (Free Flow) operation accumulate to form a total SOC change value, which reflects the overall impact of FR operations on the hybrid energy storage system's energy storage state during that period. Subsequently, the lower-level optimization calculates a series of compensating power commands for the future control time domain to offset the accumulated SOC changes within that time domain. At time t, the total power command received by the hybrid energy storage system is the superposition result of all pre-allocated power commands from time domain start 1 to time t-1. The lower-level SOC recovery optimization model minimizes the operating cost of the hybrid energy storage system through peak-valley arbitrage. J 4. To restore SOC with the goal of optimization at time t during the day:

[0132]

[0133] In the above optimization model: and All are weighting coefficients. , and These represent the electricity purchase cost, electricity sales revenue, and battery degradation cost corresponding to the electricity allocated from time t to time j in the hybrid energy storage system.

[0134] Furthermore, the optimization process should also satisfy several constraints, including energy compensation constraints for SOC recovery, SOC range constraints for hybrid energy storage systems, constraints on rules for photovoltaic system participation in lower-level optimization, and charging / discharging energy constraints. For example, these constraints can take the following specific forms.

[0135] 1. Energy compensation constraints for SOC recovery.

[0136] The lower-level SOC recovery optimization strategy should compensate for the SOC changes caused by the middle-level frequency modulation optimization:

[0137]

[0138] in, Let t be the amount of battery power that the photovoltaic system allocates to time j at time t. Let be the amount of electricity that the photovoltaic system allocates to the flywheel at time j at time t. Let t be the amount of electricity purchased from the power grid at time j that the battery allocates at time t. Let be the amount of electricity that the battery allocates to the power grid system at time t and sells to the grid at time j. Let t be the amount of electricity purchased from the power grid at time j that the flywheel allocates at time t. Let be the amount of electricity that the flywheel allocates to the power grid system at time t, which is then sold to the grid at time j. Let be the change in battery SOC at time t due to mid-layer frequency modulation. This represents the change in flywheel SOC at time t due to mid-level frequency modulation. It should be noted that "battery" is short for energy-type battery storage system, and "flywheel" is short for power-type flywheel storage system.

[0139] 2. SOC range constraints of hybrid energy storage systems.

[0140]

[0141] in, and These are the upper and lower limits of the battery's State of Charge (SOC). and The upper and lower limits of the flywheel's SOC constraint.

[0142] 3. Photovoltaic systems participate in lower-level optimization rule constraints.

[0143] When the photovoltaic system outputs high power, energy storage battery systems and power storage flywheel systems will actively obtain excess electricity from the photovoltaic power station; however, when the photovoltaic system outputs low power and its power storage capacity is weak, these systems will no longer purchase electricity from the photovoltaic system.

[0144]

[0145] in, The photovoltaic critical power point at which the photovoltaic system participates in lower-level control. P PVj This represents the output power of the photovoltaic system participating in the lower-level control at time j.

[0146] 4. Charge and discharge energy constraints.

[0147] The power command allocated to time j calculated at time t should be within a reasonable range to prevent excessive SOC recovery power from adversely affecting FR performance.

[0148]

[0149] in, This represents the maximum amount of electricity that the photovoltaic system can contribute to the recovery of the underlying SOC every 15 minutes. and This represents the maximum amount of charge the battery can contribute to lower-level SOC recovery every 15 minutes. and This represents the maximum amount of electricity the flywheel participates in restoring the lower-level SOC every 15 minutes.

[0150] The following practical case will be used to verify the hierarchical coordinated control method of the hybrid energy storage system provided in this embodiment.

[0151] Take the IEEE 9-bus power system as an example. The system's topology is as follows: Figure 6 As shown, the system includes 1000MW of conventional load, 300 electric vehicles, two 600MW thermal power units, a 40MWp photovoltaic power station, a 6MW / 12MWh BESS, and a 2MW / 0.5MWh FESS. To comprehensively evaluate the adaptability and control capability of the proposed hierarchical coordinated control strategy in multiple scenarios, this paper designs two typical application scenarios: residential areas and commercial areas. The two scenarios differ significantly in peak load periods and frequency fluctuation characteristics. The peak load in residential areas mainly occurs in the afternoon and evening, with a maximum frequency fluctuation amplitude of approximately ±0.3Hz, simulating the slight frequency disturbances caused by changes in daily electricity consumption. In contrast, the peak load in commercial areas is concentrated in the morning and afternoon, with a maximum frequency fluctuation amplitude of approximately ±0.4Hz, reflecting the more severe load disturbance characteristics caused by concentrated operation of office and commercial facilities. The predicted power and daily electricity price of the photovoltaic power station are shown in the figure. Figure 7 As shown. The critical power point of the photovoltaic system participating in the lower-level control is set to half of the photovoltaic installed capacity, i.e., PPV.set = 20MW.

[0152] The hierarchical coordinated control method for the hybrid energy storage system provided in the above embodiments has the following simulation results in a residential area: Figure 9 , Figure 10 , Figure 11 and Figure 12 As shown, the simulation results in the commercial area are as follows: Figure 13 , Figure 14 , Figure 15 and Figure 16 As shown.

[0153] The mid-level frequency regulation control strategy demonstrated good robustness and adaptability in scenarios with frequent load surges. Simulations were set to experience 2–4 load surges every 15 minutes, with frequency fluctuations of ±0.3Hz in residential areas and ±0.4Hz in commercial areas. Without the FR control strategy, the system frequency fluctuated drastically, with the MFD repeatedly exceeding the set upper limit. However, after introducing mid-level control, the frequency fluctuations converged significantly, and the MFD was successfully controlled within ±0.2Hz, significantly enhancing the system's dynamic response and stability to high-frequency disturbances.

[0154] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a hierarchical coordinated control method for a hybrid energy storage system.

[0155] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0156] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0157] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0158] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0159] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0160] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0161] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0162] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A hierarchical coordinated control method of a hybrid energy storage system, characterized by, The hybrid energy storage system comprises a photovoltaic system, an energy-type energy storage battery energy storage system, and a power-type energy storage flywheel energy storage system; The hierarchical coordination control method comprises a day-ahead peak regulation step, a real-time frequency regulation step, and an SOC recovery step; The day-ahead peak regulation step comprises: determining a target load curve of the power grid, determining a peak regulation demand according to the target load curve, and calling at least part of the capacity of the energy-type energy storage battery energy storage system to implement peak regulation operation on the power grid system according to the peak regulation demand; The real-time frequency regulation step comprises: real-time calling of the remaining capacity of the energy-type energy storage battery energy storage system, the power-type energy storage flywheel energy storage system, and the photovoltaic system to implement frequency regulation operation on the power grid system; The SOC recovery step comprises: recovering the SOC of the hybrid energy storage system by coordinating the power exchange between the hybrid energy storage system and the power grid system.

2. The hierarchical coordinated control method of the hybrid energy storage system according to claim 1, characterized in that, The determination of the target load curve of the power grid comprises: constructing a first arrival time distribution and a second arrival time distribution of an electric vehicle cluster by piecewise Gaussian distribution, the first arrival time distribution being an arrival time distribution in a home charging mode, and the second arrival time distribution being an arrival time distribution in a public charging mode; determining an initial charging demand curve of the electric vehicle cluster according to the first arrival time distribution and the second arrival time distribution; determining the target load curve according to the initial charging demand curve.

3. The hierarchical coordinated control method of the hybrid energy storage system according to claim 2, characterized in that, The determination of the target load curve according to the initial charging demand curve comprises: optimizing the charging power of part of the electric vehicles in the electric vehicle cluster according to a preset proportion, so as to optimize the initial charging demand curve to obtain a target charging demand curve, the charging power optimization aiming to minimize the total charging cost of the electric vehicles; combining the target charging demand curve and a power demand curve of non-electric vehicle clusters to obtain the target load curve.

4. The hierarchical coordinated control method of the hybrid energy storage system according to claim 1, wherein, The peak regulation demand comprises the output power of the energy-type energy storage battery energy storage system participating in peak regulation at each time; The calling of at least part of the capacity of the energy-type energy storage battery energy storage system to implement peak regulation operation on the power grid system according to the peak regulation demand comprises: performing first charge-discharge operation optimization of the energy-type energy storage battery energy storage system for the next day based on the peak regulation demand, the first charge-discharge operation optimization aiming to minimize the operating cost of the energy-type energy storage battery energy storage system.

5. The hierarchical coordinated control method of hybrid energy storage system according to claim 1, wherein, The real-time calling of the remaining capacity of the energy-type energy storage battery energy storage system, the power-type energy storage flywheel energy storage system, and the photovoltaic system to implement frequency regulation operation on the power grid system comprises: determining real-time power change amounts of the energy-type energy storage battery energy storage system, the power-type energy storage flywheel energy storage system, and the photovoltaic system, with the optimization target being minimization of the difference between each state quantity and a target value. The state quantity comprises frequency deviation, frequency regulation benefit, frequency change rate, carbon emission reduction amount of the hybrid energy storage system, and power change of the energy-type energy storage battery energy storage system, the power-type energy storage flywheel energy storage system, and the photovoltaic system.

6. The hierarchical coordinated control method of the hybrid energy storage system according to claim 1, wherein, The real-time frequency regulation step further comprises: According to the power deviation of the hybrid energy storage system, an AGC unit is called in real time to supplement power.

7. The hierarchical coordinated control method of hybrid energy storage system according to claim 1, wherein, The SOC recovery operation of the hybrid energy storage system is achieved by coordinating the power exchange between the hybrid energy storage system and the power grid system, including: Determining the current accumulated SOC change amount of the hybrid energy storage system; Optimizing the second charging and discharging operation of the hybrid energy storage system for future time, and the second charging and discharging operation optimization aims to minimize the operating cost of the hybrid energy storage system.

8. A computer device comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the hierarchical coordinated control method of the hybrid energy storage system according to any one of claims 1-7.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the hierarchical coordinated control method of the hybrid energy storage system according to any one of claims 1-7.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the hierarchical coordinated control method of the hybrid energy storage system according to any one of claims 1-7.

Images