Control method and device for participation of energy storage in power system scheduling, terminal equipment and storage medium

Abstract

The invention discloses a control method and device for participation of energy storage in power system scheduling, terminal equipment and a storage medium, and the method comprises the steps: firstly constructing an energy storage scheduling optimization model which can comprehensively consider an energy storage operation mode, a real-time scheduling demand quantity, an energy storage equipment charging and discharging power constraint, an energy storage charge state constraint and energy storage operation cost; if yes, the energy storage scheduling optimization model can be solved according to the current operation mode and the scheduling demand quantity of energy storage so as to obtain a scheduling variable which can reflect the real-time operation condition of the power system and is the current optimal output strategy of the energy storage equipment; the real-time output of the energy storage equipment can be automatically and dynamically adjusted according to the real-time operation condition of the power system and the actual operation mode of the energy storage equipment without manual intervention, so that the output of the energy storage equipment can be quickly adjusted according to the real-time operation condition of the power system; and the supply-demand relationship of the power system can be balanced more effectively, and the stability and reliability of the power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of dispatching control of power systems, and in particular to a control method, device, terminal equipment and storage medium for energy storage participating in power system dispatching. Background Art

[0002] As the proportion of renewable energy access increases, the uncertainty and volatility of new energy output is difficult to be fully balanced by the output of thermal power units. Energy storage can achieve the time transfer of power supply through short-term and long-term capacity storage and release, converting instantaneous power balance into long-term power balance, which can effectively promote the consumption of new energy and improve the safety and stability of the power system. It is an important supporting technology for the development of new power systems.

[0003] In the time-sharing multiplexing mode, energy storage can not only participate in the peak-shaving service of the power system by optimizing its charging and discharging output, but also optimize the output of energy storage to match it with the frequency regulation requirements of the power system, thereby achieving the clearing of frequency regulation services. Therefore, energy storage equipment needs to participate in frequency regulation services in a specific time period according to the operation requirements of the power system to stabilize the frequency of the power system, or participate in peak-shaving services in other different time periods to balance the supply and demand relationship of the power system. However, when energy storage equipment participates in peak-shaving or frequency regulation, the prior art requires manual regulation of the output of energy storage equipment based on a preset fixed strategy, such as setting the charging and discharging power, charging and discharging time and other parameters of the energy storage equipment only based on historical data or experience, and failing to dynamically adjust the output of the energy storage equipment according to the real-time operation status of the power system and the actual operation mode of the energy storage equipment, which will make it impossible for the energy storage equipment to be quickly adjusted according to the real-time operation status of the power system, thereby causing an imbalance in the supply and demand of the power system and affecting the stability and reliability of the system. Summary of the invention

[0004] The embodiments of the present invention provide a control method, device, terminal device and storage medium for energy storage to participate in power system scheduling. The energy storage scheduling optimization model comprehensively considers the real-time scheduling demand of the power system, the charging and discharging power constraints of the energy storage equipment, the energy storage charge state constraints and the energy storage operation cost, and can derive an optimal output strategy that can accurately reflect the real-time operation status of the power system. This can effectively solve the problem in the prior art that the output of the energy storage equipment is regulated based on a preset fixed strategy, and the output of the energy storage equipment cannot be dynamically adjusted according to the real-time operation status of the power system and the actual operation mode of the energy storage equipment, resulting in an imbalance between supply and demand of the power system.

[0005] An embodiment of the present invention provides a control method for energy storage participating in power system dispatching, comprising:

[0006] Obtaining the current operation mode of the energy storage power station in the power system dispatching process and the dispatching demand corresponding to the current operation mode; wherein the current operation mode includes: frequency modulation mode, or peak regulation mode; the dispatching demand corresponding to the frequency modulation mode is the frequency modulation demand, and the dispatching demand corresponding to the peak regulation mode is the peak regulation demand;

[0007] When it is determined that the current operation mode is the frequency modulation mode, the frequency modulation mode and the frequency modulation demand are used as inputs of the energy storage scheduling optimization model, and then the energy storage scheduling optimization model is solved under the energy storage charging and discharging power constraints and the energy storage charge state constraints, and a first scheduling variable is generated when the total energy storage scheduling cost is minimized; wherein the first scheduling variable includes: a first energy storage discharging power and a first energy storage charging power;

[0008] When it is determined that the current operation mode is the peak load mode, the peak load mode and the peak load demand are used as inputs of the energy storage scheduling optimization model, and then the energy storage scheduling optimization model is solved under the energy storage charging and discharging power constraints and the energy storage charge state constraints, and a second scheduling variable is generated when the total energy storage scheduling cost is minimized; wherein the second scheduling variable includes: a second energy storage discharging power and a second energy storage charging power;

[0009] Controlling the output of the energy storage power station during the power system dispatching process according to the first dispatching variable or the second dispatching variable;

[0010] The construction of the energy storage scheduling optimization model includes:

[0011] An energy storage scheduling optimization model is constructed according to the scheduling demand of the power system, the energy storage operation cost data and the operation mode of the energy storage power station; wherein the constraints corresponding to the energy storage scheduling optimization model include: energy storage charging and discharging power constraints and energy storage charge state constraints.

[0012] Preferably, the energy storage operation cost data includes: energy storage operation loss cost and energy storage operation penalty cost used to quantify the additional cost incurred when the energy storage power station deviates from the preset charge and discharge state;

[0013] The energy storage scheduling optimization model is constructed according to the scheduling demand of the power system, the energy storage operation cost data and the operation mode of the energy storage power station, including:

[0014] Construct an energy storage operation loss cost function based on the energy storage battery replacement cost, energy storage discharge power, energy storage charging power, energy storage charge and discharge cycle times, and energy storage capacity;

[0015] Construct an energy storage operation penalty cost function based on the preset penalty coefficient, the total energy storage charging power, the total energy storage discharging power, the energy storage discharging energy conversion efficiency, and the energy storage charging energy conversion efficiency;

[0016] An energy storage scheduling optimization model is constructed based on an energy storage operation loss cost function, an energy storage operation penalty cost function, a first variable used to characterize the operation mode of the energy storage power station, and a second variable used to characterize the scheduling demand of the power system.

[0017] Preferably, the first variable is a first preset value, and the first variable is used to characterize that the operation mode of the energy storage power station is a frequency regulation mode, the second variable is used to characterize the frequency regulation demand of the power system, and the energy storage scheduling optimization model is a frequency regulation scheduling optimization model;

[0018] The frequency modulation scheduling optimization model includes:

[0019]

[0020]

[0021] Among them, Z reg represents the total energy storage dispatch cost of the frequency regulation dispatch optimization model, σ t represents the first variable, σ t =1, and σ t Indicates that the operation mode of the energy storage power station is frequency regulation mode. is the second variable, and represents the frequency regulation demand of the power system, C loss represents the energy storage operation loss cost function, C pen represents the energy storage operation penalty cost function, p t It represents the total output of the energy storage power station at time t, which is the sum of the total energy storage charging power and the total energy storage discharging power. Indicates the reference price of a frequency modulation. It represents the energy storage discharge power corresponding to time t when the energy storage power station is in frequency regulation mode. It represents the energy storage charging power corresponding to time t when the energy storage power station is in frequency regulation mode. It represents the penalty factor corresponding to the maximum power of energy storage charging and discharging when the energy storage power station is in frequency modulation mode, ω t It represents the penalty factor corresponding to the minimum power of energy storage charging and discharging when the energy storage power station is in frequency regulation mode. represents the penalty factor corresponding to the upper limit constraint of the energy storage state of charge, κ t represents the penalty factor corresponding to the lower limit constraint of the energy storage charge state, P dch,max Represents the maximum energy storage discharge power of the energy storage power station, P ch,max It represents the maximum energy storage charging power of the energy storage station, S1 represents the initial charge state of the energy storage station, represents the discharge power of the energy storage power station at time t', represents the charging power of the energy storage station at time t', t is the time, Δt represents the length of the unit time period, η dch represents the energy conversion efficiency of energy storage and discharge, η ch Represents the energy conversion efficiency of energy storage and charging, S max Indicates the maximum state of charge of energy storage, E s,rate Represents the energy storage capacity, S min Indicates the minimum state of charge for energy storage, N 100％ It represents the maximum number of charge and discharge cycles that can be performed before the battery is retired when the discharge depth is 100%. λ represents the preset penalty coefficient. It represents the total discharge power of the energy storage power station at time t, Represents the total charging power of the energy storage station at time t.

[0022] Preferably, the first variable is a second preset value, and the first variable is used to characterize that the operation mode of the energy storage power station is a peak-shaving mode, and the second variable is used to characterize the peak-shaving demand of the power system; the energy storage scheduling optimization model is a peak-shaving scheduling optimization model; wherein the first preset value is different from the second preset value;

[0023] The peak load scheduling optimization model includes:

[0024]

[0025] Among them, minZ e represents the total energy storage dispatch cost of the peak load dispatch optimization model, σ t represents the first variable, σ t =0 means the operation mode of the energy storage power station is peak load regulation mode. is the second variable, represents the peak load demand of the power system, It represents the reference price of peak load regulation. It represents the energy storage discharge power corresponding to time t when the energy storage power station is in peak load regulation mode. It represents the energy storage charging power corresponding to time t when the energy storage power station is in peak load regulation mode. It represents the penalty factor corresponding to the maximum power of energy storage charging and discharging when the energy storage power station is in peak load mode. γ t It indicates the penalty factor corresponding to the minimum charging and discharging power of the energy storage station in the peak load mode.

[0026] Preferably, when the energy storage scheduling optimization model is a frequency modulation scheduling optimization model, the energy storage charging and discharging power constraints include:

[0027]

[0028] Among them, σt represents the first variable, σ t =1, It represents the energy storage discharge power corresponding to time t when the energy storage power station is in frequency modulation mode, P dch,max Indicates the maximum energy storage discharge power of the energy storage power station; P ch,max Indicates the maximum energy storage charging power of the energy storage power station, It indicates the energy storage charging power corresponding to time t when the energy storage power station is in frequency regulation mode.

[0029] When the energy storage scheduling optimization model is a peak-load scheduling optimization model, the energy storage charging and discharging power constraints include:

[0030]

[0031] Among them, σ t represents the first variable, σ t =0, It represents the energy storage discharge power corresponding to time t when the energy storage power station is in peak load regulation mode. It indicates the energy storage charging power corresponding to time t when the energy storage power station is in peak load regulation mode.

[0032] Preferably, the energy storage state of charge constraint includes:

[0033] S min ≤S t ≤S max ;

[0034]

[0035] Among them, S max Indicates the maximum state of charge of energy storage, S min Indicates the minimum state of charge for energy storage, S t represents the state of charge of the energy storage at time t, is the energy change at time t.

[0036] Preferably, solving the energy storage scheduling optimization model to generate a first scheduling variable when the total cost of energy storage scheduling is minimized includes:

[0037] Repeat the following model solving operations until the first scheduling variable is output:

[0038] The energy storage scheduling optimization model is derived based on the current scheduling variable to generate a gradient value corresponding to the energy storage scheduling optimization model at the current scheduling variable; wherein a current scheduling variable is randomly generated initially, and the current scheduling variable includes the energy storage discharge power and the energy storage charging power; the gradient value includes: a vector composed of partial derivatives of the scheduling optimization model at the current scheduling variable;

[0039] Solving the energy storage scheduling optimization model based on the gradient value, the current scheduling variable and the preset learning rate, and then generating an updated scheduling variable;

[0040] When it is determined that the difference between the updated function value and the current function value is greater than a preset threshold, the updated dispatch variable is used as the current dispatch variable when the model solving operation is performed next time; wherein the updated function value is the function value corresponding to the updated dispatch variable in the energy storage dispatch optimization model; the current function value is the function value corresponding to the current dispatch variable in the energy storage dispatch optimization model;

[0041] When it is determined that the difference between the updated function value and the current function value is not greater than a preset threshold, the updated dispatch variable is used as the first dispatch variable and the first dispatch variable is output; wherein the total energy storage dispatch cost corresponding to the first dispatch variable is the smallest.

[0042] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0043] An embodiment of the present invention provides a control device for energy storage participating in power system dispatching, comprising: a data acquisition module, a model solving module and an output control module;

[0044] The data acquisition module is used to obtain the current operation mode of the energy storage power station in the power system dispatching process and the dispatching demand corresponding to the current operation mode; wherein the current operation mode includes: frequency modulation mode, or peak regulation mode; the dispatching demand corresponding to the frequency modulation mode is the frequency modulation demand, and the dispatching demand corresponding to the peak regulation mode is the peak regulation demand;

[0045] The model solving module is used to use the frequency modulation mode and the frequency modulation demand as inputs of the energy storage scheduling optimization model when determining that the current operation mode is the frequency modulation mode, and then solve the energy storage scheduling optimization model under the energy storage charging and discharging power constraints and the energy storage charge state constraints, and generate a first scheduling variable when the total energy storage scheduling cost is minimized; wherein the first scheduling variable includes: a first energy storage discharging power and a first energy storage charging power;

[0046] When it is determined that the current operation mode is the peak load mode, the peak load mode and the peak load demand are used as inputs of the energy storage scheduling optimization model, and then the energy storage scheduling optimization model is solved under the energy storage charging and discharging power constraints and the energy storage charge state constraints, and a second scheduling variable is generated when the total energy storage scheduling cost is minimized; wherein the second scheduling variable includes: a second energy storage discharging power and a second energy storage charging power;

[0047] The output control module is used to control the output of the energy storage power station during the power system dispatching process according to the first dispatching variable or the second dispatching variable;

[0048] The construction of the energy storage scheduling optimization model includes:

[0049] An energy storage scheduling optimization model is constructed according to the scheduling demand of the power system, the energy storage operation cost data and the operation mode of the energy storage power station; wherein the constraints corresponding to the energy storage scheduling optimization model include: energy storage charging and discharging power constraints and energy storage charge state constraints.

[0050] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0051] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, a control method for energy storage participating in power system scheduling as described in the above-mentioned embodiment of the invention is implemented.

[0052] Based on the above method embodiments, the present invention provides corresponding storage medium item embodiments.

[0053] Another embodiment of the present invention provides a storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a control method for energy storage participating in power system scheduling as described in the above-mentioned embodiment of the invention.

[0054] The following beneficial effects are achieved by implementing the present invention:

[0055] The embodiment of the present invention provides a control method, device, terminal device and storage medium for energy storage to participate in power system dispatching. The present invention first constructs an energy storage dispatching optimization model with the goal of minimizing the total cost of energy storage dispatching according to the dispatching demand of the power system, the energy storage operating cost data and the operating mode of the energy storage power station, so that after obtaining the current operating mode of the energy storage power station in the power system dispatching process and the dispatching demand under this mode, the current operating mode can be judged. If the current operating mode is the frequency modulation mode, the energy storage dispatching optimization model can be solved based on the frequency modulation mode and the corresponding frequency modulation demand to obtain the first dispatching variable corresponding to the minimum total cost of energy storage dispatching; if the current operating mode is the peak regulation mode, the energy storage dispatching optimization model can be solved based on the peak regulation mode and the corresponding peak regulation demand to obtain the second dispatching variable corresponding to the minimum total cost of energy storage dispatching. Therefore, finally, based on the current operating mode and the optimal dispatching variable automatically solved, the output of the energy storage power station in the power system dispatching process can be automatically controlled. Compared with the prior art, since the energy storage scheduling optimization model comprehensively considers the real-time scheduling demand of the power system, the charging and discharging power constraints of the energy storage equipment, the energy storage charge state constraints and the energy storage operating cost, the present invention can obtain the first scheduling variable or the second scheduling variable that can accurately reflect the real-time operating status of the power system and is the current optimal output strategy of the energy storage device when solving the energy storage scheduling optimization model according to the corresponding mode and scheduling demand. It can realize the dynamic adjustment of the output strategy of the energy storage device according to the real-time operating status of the power system and the actual operating mode of the energy storage device, instead of only regulating the output of the energy storage device according to the preset fixed strategy, which significantly reduces manual intervention. The present invention can not only quickly adjust the output of the energy storage device according to the real-time operating status of the power system, but also more effectively balance the supply and demand relationship of the power system, and improve the stability and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of a control method for energy storage participating in power system dispatching provided by one embodiment of the present invention.

[0057] Figure 2 It is a schematic diagram of the solution process of the energy storage scheduling optimization model provided by another embodiment of the present invention.

[0058] Figure 3 It is a structural schematic diagram of a control device for energy storage participating in power system dispatching provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] 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 only 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.

[0060] like Figure 1 FIG. 1 is a flow chart of a control method for energy storage participating in power system dispatching provided by an embodiment of the present invention, wherein the control method for energy storage participating in power system dispatching comprises:

[0061] Step S1: obtaining the current operation mode of the energy storage power station in the power system dispatching process and the dispatching demand corresponding to the current operation mode; wherein the current operation mode includes: frequency modulation mode or peak regulation mode; the dispatching demand corresponding to the frequency modulation mode is the frequency modulation demand, and the dispatching demand corresponding to the peak regulation mode is the peak regulation demand;

[0062] Step S2: when it is determined that the current operation mode is the frequency modulation mode, the frequency modulation mode and the frequency modulation demand are used as inputs of the energy storage scheduling optimization model, and then the energy storage scheduling optimization model is solved under the energy storage charging and discharging power constraints and the energy storage charge state constraints, and a first scheduling variable is generated when the total energy storage scheduling cost is minimized; wherein the first scheduling variable includes: a first energy storage discharging power and a first energy storage charging power;

[0063] When it is determined that the current operation mode is the peak load mode, the peak load mode and the peak load demand are used as inputs of the energy storage scheduling optimization model, and then the energy storage scheduling optimization model is solved under the energy storage charging and discharging power constraints and the energy storage charge state constraints, and a second scheduling variable is generated when the total energy storage scheduling cost is minimized; wherein the second scheduling variable includes: a second energy storage discharging power and a second energy storage charging power;

[0064] The construction of the energy storage scheduling optimization model includes:

[0065] According to the dispatch demand of the power system, the energy storage operation cost data and the operation mode of the energy storage power station, an energy storage dispatch optimization model is constructed; wherein the constraints corresponding to the energy storage dispatch optimization model include: energy storage charging and discharging power constraints and energy storage charge state constraints;

[0066] Step S3: According to the first dispatching variable or the second dispatching variable, the output of the energy storage power station during the current power system dispatching process is controlled.

[0067] For step S1, the present invention can realize automatic optimal output control when the energy storage power station provides peak load regulation service or frequency regulation service for the power system.

[0068] First, it is necessary to obtain the current operating mode of the energy storage power station in the power system dispatching process and the dispatching demand corresponding to the current operating mode;

[0069] In schematic form, when the operating mode is the frequency modulation mode, the obtained scheduling demand is the scheduling demand corresponding to the frequency modulation mode; when the operating mode is the peak regulation mode, the obtained scheduling demand is the scheduling demand corresponding to the peak regulation mode.

[0070] By obtaining the current operating mode (frequency regulation or peak regulation) and the corresponding dispatch demand, it can be ensured that when the output of the energy storage power station is controlled, the frequency regulation service or peak regulation service provided by the energy storage power station is accurately matched with the actual needs of the power system, thereby maintaining the stability and reliability of the power system and avoiding grid fluctuations or failures caused by imbalance between supply and demand.

[0071] For step S2, in a preferred embodiment, the present invention can identify the current operating mode. If the current operating mode is the frequency modulation mode, the frequency modulation mode and the frequency modulation demand are used as the operating mode and the scheduling demand in the energy storage scheduling optimization model, so that when the energy storage scheduling optimization model is solved, the optimal output corresponding to the energy storage device in the frequency modulation mode can be obtained, that is, the first scheduling variable in the embodiment of the present invention.

[0072] If the current operating mode is the peak-shaving mode, the peak-shaving mode and the peak-shaving demand are used as the operating mode and the scheduling demand in the energy storage scheduling optimization model, so that when the energy storage scheduling optimization model is solved, the optimal output corresponding to the energy storage device in the peak-shaving mode can be obtained, that is, the second scheduling variable in the embodiment of the present invention.

[0073] The frequency regulation mode focuses on quickly responding to changes in grid frequency, while the peak regulation mode focuses on the peak-valley balance of electricity supply and demand. By identifying the current operating mode, it can ensure that the energy storage scheduling optimization model adapts to different power system requirements, allowing energy storage power stations to participate in the scheduling of the power system more flexibly and meet diverse needs.

[0074] In order to allow the energy storage scheduling optimization model of the present invention to take into account the aging cost and penalty cost of energy storage, when constructing the energy storage scheduling optimization model, specifically:

[0075] Construct an energy storage operation loss cost function based on the energy storage battery replacement cost, energy storage discharge power, energy storage charging power, energy storage charge and discharge cycle times, and energy storage capacity;

[0076] Construct an energy storage operation penalty cost function based on the preset penalty coefficient, the total energy storage charging power, the total energy storage discharging power, the energy storage discharging energy conversion efficiency, and the energy storage charging energy conversion efficiency;

[0077] An energy storage scheduling optimization model is constructed based on an energy storage operation loss cost function, an energy storage operation penalty cost function, a first variable used to characterize the operation mode of the energy storage power station, and a second variable used to characterize the scheduling demand of the power system.

[0078] Among them, the first variable can represent the operating mode of the energy storage power station by setting different values, and the second variable can also correspond to different values, so that the energy storage scheduling optimization model can realize the output optimization solution of the frequency regulation mode and the output optimization solution of the peak regulation mode.

[0079] When constructing the energy storage dispatch optimization model, the aging cost of energy storage (such as the replacement cost of energy storage batteries, the number of energy storage charge and discharge cycles, etc.) and penalty costs (such as penalties for not meeting dispatch requirements) are taken into account, which helps to minimize costs. By optimizing the output of energy storage power stations, the operating costs of energy storage power stations can be reduced while meeting the needs of the power system, thereby improving economic benefits.

[0080] Therefore, according to the specific requirements of the frequency regulation or peak regulation mode, the energy storage scheduling optimization model of the present invention can solve the corresponding optimal output (i.e., the first scheduling variable or the second scheduling variable), thereby ensuring that the output of the energy storage power station matches the actual needs of the power system and improving the stability and reliability of the power system.

[0081] In a preferred embodiment, the first variable is a first preset value, and the first variable is used to characterize that the operating mode of the energy storage power station is a frequency regulation mode, then the second variable is used to characterize the frequency regulation demand of the power system, and the energy storage scheduling optimization model is a frequency regulation scheduling optimization model; schematically, the first preset value can be 1.

[0082] The frequency modulation scheduling optimization model includes:

[0083]

[0084] Among them, Z reg represents the total energy storage dispatch cost of the frequency regulation dispatch optimization model, σ t represents the first variable, σ t =1, and σ t Indicates that the operation mode of the energy storage power station is frequency regulation mode. is the second variable, and represents the frequency regulation demand of the power system, C loss represents the energy storage operation loss cost function, C pen represents the energy storage operation penalty cost function, p t It represents the total output of the energy storage power station at time t, which is the sum of the total energy storage charging power and the total energy storage discharging power. Indicates the reference price of a frequency modulation. It represents the energy storage discharge power corresponding to time t when the energy storage power station is in frequency regulation mode. It represents the energy storage charging power corresponding to time t when the energy storage power station is in frequency regulation mode. It represents the penalty factor corresponding to the maximum power of energy storage charging and discharging when the energy storage power station is in frequency modulation mode, ω t It represents the penalty factor corresponding to the minimum power of energy storage charging and discharging when the energy storage power station is in frequency regulation mode. represents the penalty factor corresponding to the upper limit constraint of the energy storage state of charge, κ t represents the penalty factor corresponding to the lower limit constraint of the energy storage charge state, P dch,max Represents the maximum energy storage discharge power of the energy storage power station, P ch,max It represents the maximum energy storage charging power of the energy storage station, S1 represents the initial charge state of the energy storage station, represents the discharge power of the energy storage power station at time t', represents the charging power of the energy storage station at time t', t is the time, Δt represents the length of the unit time period, usually 5min or 15min; η dch represents the energy conversion efficiency of energy storage and discharge, η ch Represents the energy conversion efficiency of energy storage and charging, S max Indicates the maximum state of charge of energy storage, E s,rate Represents the energy storage capacity, S min Indicates the minimum state of charge for energy storage, N 100％ It represents the maximum number of charge and discharge cycles that can be performed before the battery is retired when the discharge depth is 100%. λ represents the preset penalty coefficient. It represents the total discharge power of the energy storage power station at time t, Represents the total charging power of the energy storage station at time t.

[0085] In a preferred embodiment, the first variable is the second preset value, and the first variable is used to characterize that the operation mode of the energy storage power station is a peak-shaving mode, then the second variable is used to characterize the peak-shaving demand of the power system; the energy storage scheduling optimization model is a peak-shaving scheduling optimization model; wherein the first preset value is different from the second preset value; schematically, the first preset value may be 1, and the second preset value may be 0.

[0086] The peak load scheduling optimization model includes:

[0087]

[0088] Among them, minZ e represents the total energy storage dispatch cost of the peak load dispatch optimization model, σ t represents the first variable, σ t=0 means the operation mode of the energy storage power station is peak load regulation mode. is the second variable, represents the peak load demand of the power system, It represents the reference price of peak load regulation. It represents the energy storage discharge power corresponding to time t when the energy storage power station is in peak load regulation mode. It represents the energy storage charging power corresponding to time t when the energy storage power station is in peak load regulation mode. It represents the penalty factor corresponding to the maximum power of energy storage charging and discharging when the energy storage power station is in peak load mode. γ t It indicates the penalty factor corresponding to the minimum charging and discharging power of the energy storage station in the peak load mode.

[0089] When the energy storage scheduling optimization model is a frequency modulation scheduling optimization model, the corresponding energy storage charging and discharging power constraints include:

[0090]

[0091] Among them, σ t represents the first variable, σ t =1, It represents the energy storage discharge power corresponding to time t when the energy storage power station is in frequency modulation mode, P dch,max Indicates the maximum energy storage discharge power of the energy storage power station; P ch,max Indicates the maximum energy storage charging power of the energy storage power station, It indicates the energy storage charging power corresponding to time t when the energy storage power station is in frequency regulation mode.

[0092] When the energy storage scheduling optimization model is a peak-load scheduling optimization model, the corresponding energy storage charging and discharging power constraints include:

[0093]

[0094] Among them, σ t represents the first variable, σ t =0, It represents the energy storage discharge power corresponding to time t when the energy storage power station is in peak load regulation mode. It indicates the energy storage charging power corresponding to time t when the energy storage power station is in peak load regulation mode.

[0095] Furthermore, the energy storage charge state constraints corresponding to the frequency regulation scheduling optimization model or the peak regulation scheduling optimization model include:

[0096] S min ≤S t ≤S max ;

[0097]

[0098] Among them, S max Indicates the maximum state of charge of energy storage, S min Indicates the minimum state of charge for energy storage, S t represents the state of charge of the energy storage at time t, is the energy change at time t.

[0099] It can be understood that the present invention uses a variable of 0 or 1 to characterize whether the energy storage is involved in frequency regulation or peak regulation in each period, thereby realizing that the energy storage provides different auxiliary services in different periods. For example, the first variable (the first preset value, such as 1) clearly characterizes that the energy storage power station is in frequency regulation mode, and the second variable directly reflects the frequency regulation demand of the power system. The model can optimize the calculation according to the specific frequency regulation demand.

[0100] Whether in the frequency regulation optimization model or the peak regulation optimization model, the functions of the energy storage operation loss cost and penalty cost are comprehensively considered, so that the model can find the lowest cost energy storage power station output plan while meeting the frequency regulation or peak regulation requirements, thereby reducing operating costs.

[0101] Moreover, the frequency regulation optimization model or the peak regulation optimization model takes into account the power constraints and capacity constraints of the energy storage power station, as well as factors such as the aging rate of the battery, so as to ensure that the final strategy can prevent the energy storage power station from exceeding its physical limits during operation, thereby enhancing the stability of the power system.

[0102] Furthermore, by setting the penalty coefficient and the corresponding penalty cost, the energy storage system can be incentivized to meet the dispatching needs of the power system more accurately. When the energy storage output deviates from the expected output of the system, additional penalty costs will be incurred, prompting the energy storage system to adjust its output to better match the system demand. When there is an imbalance between supply and demand in the power system, the energy storage system can respond quickly and provide the necessary regulation capabilities. The setting of penalty costs further ensures that the energy storage system can play its due role at critical moments.

[0103] Therefore, the energy storage scheduling optimization model of the embodiment of the present invention can automatically perform optimization calculations based on the frequency regulation or peak regulation requirements of the power system and the operating status of the energy storage power station, thereby improving the intelligence level of the energy storage power station participating in the power system scheduling.

[0104] In a preferred embodiment, solving the energy storage scheduling optimization model to generate a first scheduling variable when the total cost of energy storage scheduling is minimized includes:

[0105] Repeat the following model solving operations until the first scheduling variable is output:

[0106] The energy storage scheduling optimization model is derived based on the current scheduling variable to generate a gradient value corresponding to the energy storage scheduling optimization model at the current scheduling variable; wherein a current scheduling variable is randomly generated initially, and the current scheduling variable includes the energy storage discharge power and the energy storage charging power; the gradient value includes: a vector composed of partial derivatives of the scheduling optimization model at the current scheduling variable;

[0107] Solving the energy storage scheduling optimization model based on the gradient value, the current scheduling variable and the preset learning rate, and then generating an updated scheduling variable;

[0108] When it is determined that the difference between the updated function value and the current function value is greater than a preset threshold, the updated dispatch variable is used as the current dispatch variable when the model solving operation is performed next time; wherein the updated function value is the function value corresponding to the updated dispatch variable in the energy storage dispatch optimization model; the current function value is the function value corresponding to the current dispatch variable in the energy storage dispatch optimization model;

[0109] When it is determined that the difference between the updated function value and the current function value is not greater than a preset threshold, the updated dispatch variable is used as the first dispatch variable and the first dispatch variable is output; wherein the total energy storage dispatch cost corresponding to the first dispatch variable is the smallest.

[0110] It can be understood that when solving the energy storage scheduling optimization model and generating the second scheduling variable when the total energy storage scheduling cost is minimized, the solution principle and process adopted are the same as the solution process of the first scheduling variable mentioned above, and will not be repeated here.

[0111] like Figure 2 The schematic diagram of the solution process of the energy storage scheduling optimization model shown in FIG. 1 shows, in the embodiment of the present invention, the first variable σ t Make a judgment, if σ t =1, it means that the operation mode of the energy storage power station is frequency regulation mode, then the frequency regulation scheduling optimization model is derived to obtain the gradient value, such as

[0112] Understandably, is a vector composed of partial derivatives of the frequency regulation dispatch optimization model at the energy storage discharge power obtained by the current optimization solution; and It is a vector composed of partial derivatives of the frequency regulation dispatch optimization model at the energy storage charging power obtained by the current optimization solution;

[0113] If σ t =0, it means that the operation mode of the energy storage power station is the peak load mode, then the peak load scheduling optimization model is derived to obtain the gradient value, such as

[0114] Understandably, It is a vector composed of the partial derivatives of the peak-shaving dispatch optimization model at the energy storage discharge power obtained by the current optimization solution; It is a vector composed of the partial derivatives of the peak-shaving scheduling optimization model at the energy storage charging power obtained by the current optimization solution.

[0115] Then, each time the model solving operation is performed, for the frequency modulation scheduling optimization model, the energy storage scheduling optimization model is solved based on the gradient value, the current scheduling variable and the preset learning rate, and then the updated scheduling variable is generated. The specific calculation formula is:

[0116]

[0117] Among them, Z reg is the objective function of energy storage participating in frequency regulation, i.e., the frequency regulation scheduling optimization model; α is the unit optimization step length, i.e., the preset learning rate; k is the number of cycles; are all updated scheduling variables;

[0118] Each time the model solving operation is performed, for the peak-shaving scheduling optimization model, the energy storage scheduling optimization model is solved based on the gradient value, the current scheduling variable and the preset learning rate, and then the updated scheduling variable is generated. The specific calculation formula is:

[0119]

[0120] Among them, Z e is the objective function of energy storage participating in peak load regulation, that is, the peak load regulation optimization model; α is the unit optimization step of the closed-loop control model, that is, the preset learning rate; k is the number of cycles; are all updated scheduling variables.

[0121] Therefore, the above-mentioned solution process for the frequency regulation scheduling optimization model or the peak regulation scheduling optimization model adopts the gradient descent method for iterative solution. By continuously iterating and calculating the gradient value of the energy storage scheduling optimization model (frequency regulation scheduling optimization model or peak regulation scheduling optimization model) based on the current scheduling variables, it is possible to gradually approach the optimal solution with the minimum total cost of energy storage scheduling. The accuracy of the solution can be ensured in complex energy storage scheduling problems, and it can converge to the optimal solution more quickly, reducing the consumption of computing resources.

[0122] For step S3, in a preferred embodiment, after determining the operation mode of the energy storage power station (such as frequency regulation mode or peak regulation mode), the optimal first scheduling variable or second scheduling variable can be obtained by solving the energy storage scheduling optimization model.

[0123] The dispatching variables (i.e., energy storage discharge power and energy storage charging power) represent the optimal output state that the energy storage power station should achieve when meeting the dispatching requirements of the power system. Therefore, when the control system adjusts the output of the energy storage power station in real time according to these optimal dispatching variables, it can ensure that it accurately matches the dispatching requirements of the power system.

[0124] Since the solution of the first dispatch variable or the second dispatch variable takes into account factors such as the operating loss cost and penalty cost of the energy storage power station, when controlling according to these optimal dispatch variables, the operating cost of the energy storage power station can be minimized while meeting the dispatch requirements of the power system.

[0125] Therefore, the embodiment of the present invention can avoid unnecessary charging and discharging operations by accurately controlling the output of the energy storage power station, thereby reducing the loss and maintenance cost of the energy storage equipment. As an important part of the power system, the precise control of the output of the energy storage power station helps to enhance the stability of the power system, especially when there is an imbalance between supply and demand or a fault in the power system, the control method of the present invention can enable the energy storage power station to respond quickly and provide the necessary adjustment capabilities to ensure the safe and stable operation of the power system.

[0126] like Figure 3 As shown, based on the above-mentioned various embodiments of the control method for energy storage participating in power system dispatching, the present invention provides a corresponding device embodiment;

[0127] An embodiment of the present invention provides a control device for energy storage participating in power system dispatching, comprising: a data acquisition module, a model solving module and an output control module;

[0128] The data acquisition module is used to obtain the current operation mode of the energy storage power station in the power system dispatching process and the dispatching demand corresponding to the current operation mode; wherein the current operation mode includes: frequency modulation mode, or peak regulation mode; the dispatching demand corresponding to the frequency modulation mode is the frequency modulation demand, and the dispatching demand corresponding to the peak regulation mode is the peak regulation demand;

[0129] The model solving module is used to use the frequency modulation mode and the frequency modulation demand as inputs of the energy storage scheduling optimization model when determining that the current operation mode is the frequency modulation mode, and then solve the energy storage scheduling optimization model under the energy storage charging and discharging power constraints and the energy storage charge state constraints, and generate a first scheduling variable when the total energy storage scheduling cost is minimized; wherein the first scheduling variable includes: a first energy storage discharging power and a first energy storage charging power;

[0130] When it is determined that the current operation mode is the peak load mode, the peak load mode and the peak load demand are used as inputs of the energy storage scheduling optimization model, and then the energy storage scheduling optimization model is solved under the energy storage charging and discharging power constraints and the energy storage charge state constraints, and a second scheduling variable is generated when the total energy storage scheduling cost is minimized; wherein the second scheduling variable includes: a second energy storage discharging power and a second energy storage charging power;

[0131] The output control module is used to control the output of the energy storage power station during the power system dispatching process according to the first dispatching variable or the second dispatching variable;

[0132] The construction of the energy storage scheduling optimization model includes:

[0133] An energy storage scheduling optimization model is constructed according to the scheduling demand of the power system, the energy storage operation cost data and the operation mode of the energy storage power station; wherein the constraints corresponding to the energy storage scheduling optimization model include: energy storage charging and discharging power constraints and energy storage charge state constraints.

[0134] It should be noted that the device embodiments described above are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without paying creative labor.

[0135] Those skilled in the art can clearly understand that, for the sake of convenience and simplicity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0136] Based on the above-mentioned various embodiments of the control method for energy storage participating in power system dispatching, the present invention provides corresponding embodiments of terminal equipment items.

[0137] An embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a control method for energy storage participating in power system scheduling as described in any method embodiment of the present invention is implemented.

[0138] The terminal device may be a computing terminal device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0139] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0140] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device or other volatile solid-state storage device.

[0141] Based on the above-mentioned various embodiments of the control method for energy storage participating in power system dispatching, the present invention provides corresponding storage medium item embodiments.

[0142] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a control method for energy storage participating in power system scheduling as described in any method embodiment of the present invention.

[0143] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each method embodiment described above can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0144] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A control method for energy storage participating in power system dispatching, characterized in that: include: Obtaining the current operation mode of the energy storage power station in the power system dispatching process and the dispatching demand corresponding to the current operation mode; wherein the current operation mode includes: frequency modulation mode, or peak regulation mode; the dispatching demand corresponding to the frequency modulation mode is the frequency modulation demand, and the dispatching demand corresponding to the peak regulation mode is the peak regulation demand; When it is determined that the current operation mode is the frequency modulation mode, the frequency modulation mode and the frequency modulation demand are used as inputs of the energy storage scheduling optimization model, and then the energy storage scheduling optimization model is solved under the energy storage charging and discharging power constraints and the energy storage charge state constraints, and a first scheduling variable is generated when the total energy storage scheduling cost is minimized; wherein the first scheduling variable includes: a first energy storage discharging power and a first energy storage charging power; When it is determined that the current operation mode is the peak load mode, the peak load mode and the peak load demand are used as inputs of the energy storage scheduling optimization model, and then the energy storage scheduling optimization model is solved under the energy storage charging and discharging power constraints and the energy storage charge state constraints, and a second scheduling variable is generated when the total energy storage scheduling cost is minimized; wherein the second scheduling variable includes: a second energy storage discharging power and a second energy storage charging power; Controlling the output of the energy storage power station during the power system dispatching process according to the first dispatching variable or the second dispatching variable; The construction of the energy storage scheduling optimization model includes: An energy storage scheduling optimization model is constructed according to the scheduling demand of the power system, the energy storage operation cost data and the operation mode of the energy storage power station; wherein the constraints corresponding to the energy storage scheduling optimization model include: energy storage charging and discharging power constraints and energy storage charge state constraints.

2. A control method for energy storage participating in power system dispatching as claimed in claim 1, characterized in that: The energy storage operation cost data includes: energy storage operation loss cost and energy storage operation penalty cost used to quantify the additional cost incurred when the energy storage power station deviates from the preset charge and discharge state; The energy storage scheduling optimization model is constructed according to the scheduling demand of the power system, the energy storage operation cost data and the operation mode of the energy storage power station, including: Construct an energy storage operation loss cost function based on the energy storage battery replacement cost, energy storage discharge power, energy storage charging power, energy storage charge and discharge cycle times, and energy storage capacity; Construct an energy storage operation penalty cost function based on the preset penalty coefficient, the total energy storage charging power, the total energy storage discharging power, the energy storage discharging energy conversion efficiency, and the energy storage charging energy conversion efficiency; An energy storage scheduling optimization model is constructed based on an energy storage operation loss cost function, an energy storage operation penalty cost function, a first variable used to characterize the operation mode of the energy storage power station, and a second variable used to characterize the scheduling demand of the power system.

3. A control method for energy storage participating in power system dispatching as claimed in claim 2, characterized in that: The first variable is a first preset value, and the first variable is used to characterize that the operation mode of the energy storage power station is a frequency regulation mode, the second variable is used to characterize the frequency regulation demand of the power system, and the energy storage scheduling optimization model is a frequency regulation scheduling optimization model; The frequency modulation scheduling optimization model includes: Among them, Z reg represents the total energy storage dispatch cost of the frequency regulation dispatch optimization model, σ t represents the first variable, σ t =1, and σ t Indicates that the operation mode of the energy storage power station is frequency regulation mode. is the second variable, and represents the frequency regulation demand of the power system, C loss represents the energy storage operation loss cost function, C pen represents the energy storage operation penalty cost function, p t It represents the total output of the energy storage power station at time t, which is the sum of the total energy storage charging power and the total energy storage discharging power. Indicates the reference price of a frequency modulation. It represents the energy storage discharge power corresponding to time t when the energy storage power station is in frequency regulation mode. It represents the energy storage charging power corresponding to time t when the energy storage power station is in frequency regulation mode. It represents the penalty factor corresponding to the maximum power of energy storage charging and discharging when the energy storage power station is in frequency regulation mode. ω t It represents the penalty factor corresponding to the minimum power of energy storage charging and discharging when the energy storage power station is in frequency regulation mode. represents the penalty factor corresponding to the upper limit constraint of the energy storage state of charge, κ t It represents the penalty factor corresponding to the lower limit constraint of the energy storage charge state, P dch,max Represents the maximum energy storage discharge power of the energy storage power station, P ch,max It represents the maximum energy storage charging power of the energy storage station, S1 represents the initial charge state of the energy storage station, represents the discharge power of the energy storage power station at time t', represents the charging power of the energy storage station at time t', t is the time, Δt represents the length of the unit time period, η dch represents the energy conversion efficiency of energy storage and discharge, η ch Represents the energy conversion efficiency of energy storage and charging, S max Indicates the maximum state of charge of energy storage, E s,rate Represents the energy storage capacity, S min Indicates the minimum state of charge for energy storage, N 100％ It represents the maximum number of charge and discharge cycles that can be performed before the battery is retired when the discharge depth is 100%. λ represents the preset penalty coefficient. It represents the total discharge power of the energy storage power station at time t, Represents the total charging power of the energy storage station at time t.

4. A control method for energy storage participating in power system dispatching as claimed in claim 3, characterized in that: The first variable is a second preset value, and the first variable is used to characterize that the operation mode of the energy storage power station is a peak-shaving mode, and the second variable is used to characterize the peak-shaving demand of the power system; the energy storage scheduling optimization model is a peak-shaving scheduling optimization model; wherein the first preset value is different from the second preset value; The peak load scheduling optimization model includes: Among them, minZ e represents the total energy storage dispatch cost of the peak load dispatch optimization model, σ t represents the first variable, σ t =0 means the operation mode of the energy storage power station is peak load regulation mode. is the second variable, represents the peak load demand of the power system, It represents the reference price of peak load regulation. It represents the energy storage discharge power corresponding to time t when the energy storage power station is in peak load regulation mode. It represents the energy storage charging power corresponding to time t when the energy storage power station is in peak load regulation mode. It represents the penalty factor corresponding to the maximum power of energy storage charging and discharging when the energy storage power station is in peak load mode. γ t It indicates the penalty factor corresponding to the minimum charging and discharging power of the energy storage station in the peak load mode.

5. A control method for energy storage participating in power system dispatching as claimed in claim 4, characterized in that: When the energy storage scheduling optimization model is a frequency modulation scheduling optimization model, the energy storage charging and discharging power constraints include: Among them, σ t represents the first variable, σ t =1, It represents the energy storage discharge power corresponding to time t when the energy storage power station is in frequency modulation mode, P dch,max Indicates the maximum energy storage discharge power of the energy storage power station; P ch,max Indicates the maximum energy storage charging power of the energy storage power station, It indicates the energy storage charging power corresponding to time t when the energy storage power station is in frequency regulation mode. When the energy storage scheduling optimization model is a peak-load scheduling optimization model, the energy storage charging and discharging power constraints include: Among them, σ t represents the first variable, σ t =0, It represents the energy storage discharge power corresponding to time t when the energy storage power station is in peak load regulation mode. It indicates the energy storage charging power corresponding to time t when the energy storage power station is in peak load regulation mode.

6. A control method for energy storage participating in power system dispatching as claimed in claim 5, characterized in that: The energy storage state of charge constraint includes: S min ≤S t ≤S max ; Among them, S max Indicates the maximum state of charge of energy storage, S min Indicates the minimum state of charge for energy storage, S t represents the state of charge of the energy storage at time t, is the energy change at time t.

7. A control method for energy storage participating in power system dispatching as claimed in claim 1, characterized in that: The step of solving the energy storage scheduling optimization model and generating a first scheduling variable when the total cost of energy storage scheduling is minimized includes: Repeat the following model solving operations until the first scheduling variable is output: The energy storage scheduling optimization model is derived based on the current scheduling variable to generate a gradient value corresponding to the energy storage scheduling optimization model at the current scheduling variable; wherein a current scheduling variable is randomly generated initially, and the current scheduling variable includes the energy storage discharge power and the energy storage charging power; the gradient value includes: a vector composed of partial derivatives of the scheduling optimization model at the current scheduling variable; Solving the energy storage scheduling optimization model based on the gradient value, the current scheduling variable and the preset learning rate, and then generating an updated scheduling variable; When it is determined that the difference between the updated function value and the current function value is greater than a preset threshold, the updated dispatch variable is used as the current dispatch variable when the model solving operation is performed next time; wherein the updated function value is the function value corresponding to the updated dispatch variable in the energy storage dispatch optimization model; the current function value is the function value corresponding to the current dispatch variable in the energy storage dispatch optimization model; When it is determined that the difference between the updated function value and the current function value is not greater than a preset threshold, the updated dispatch variable is used as the first dispatch variable and the first dispatch variable is output; wherein the total energy storage dispatch cost corresponding to the first dispatch variable is the smallest.

8. A control device for energy storage participating in power system dispatching, characterized in that: include: Data acquisition module, model solving module and output control module; The data acquisition module is used to obtain the current operation mode of the energy storage power station in the power system dispatching process and the dispatching demand corresponding to the current operation mode; wherein the current operation mode includes: frequency modulation mode, or peak regulation mode; the dispatching demand corresponding to the frequency modulation mode is the frequency modulation demand, and the dispatching demand corresponding to the peak regulation mode is the peak regulation demand; The model solving module is used to use the frequency modulation mode and the frequency modulation demand as inputs of the energy storage scheduling optimization model when determining that the current operation mode is the frequency modulation mode, and then solve the energy storage scheduling optimization model under the energy storage charging and discharging power constraints and the energy storage charge state constraints, and generate a first scheduling variable when the total energy storage scheduling cost is minimized; wherein the first scheduling variable includes: a first energy storage discharging power and a first energy storage charging power; When it is determined that the current operation mode is the peak load mode, the peak load mode and the peak load demand are used as inputs of the energy storage scheduling optimization model, and then the energy storage scheduling optimization model is solved under the energy storage charging and discharging power constraints and the energy storage charge state constraints, and a second scheduling variable is generated when the total energy storage scheduling cost is minimized; wherein the second scheduling variable includes: a second energy storage discharging power and a second energy storage charging power; The output control module is used to control the output of the energy storage power station during the power system dispatching process according to the first dispatching variable or the second dispatching variable; The construction of the energy storage scheduling optimization model includes: An energy storage scheduling optimization model is constructed according to the scheduling demand of the power system, the energy storage operation cost data and the operation mode of the energy storage power station; wherein the constraints corresponding to the energy storage scheduling optimization model include: energy storage charging and discharging power constraints and energy storage charge state constraints.

9. A terminal device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a control method for energy storage participating in power system dispatching as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a control method for energy storage participating in power system dispatching as described in any one of claims 1 to 7.

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