Power system demand-oriented energy storage system operation and optimal scheduling method

Abstract

The invention provides a power system demand-oriented energy storage system operation and optimal scheduling method, and relates to the technical field of power systems, and the method comprises the steps: obtaining the energy storage system income, the system electricity purchase cost and the auxiliary service cost in an electricity and goods market environment, and building an upper-layer energy storage system optimization model with the maximum income of the energy storage system as a target; establishing a lower-layer system optimization model by taking the minimum sum of the system electricity purchase cost and the auxiliary service cost as an objective function; based on the upper-layer energy storage system optimization model and the lower-layer system optimization model, establishing a double-layer optimization model through double-layer programming BLP; solving the double-layer optimization model, and outputting an energy storage system operation and optimization scheduling decision; the economic benefit of the energy storage system in the electricity market can be remarkably improved, the cost of the energy storage system is reduced, and accurate regulation and control of the energy storage system are realized by fully utilizing resources.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically, to a method for the operation and optimized scheduling of energy storage systems to meet the needs of power systems. Background Technology

[0002] With the advancement of "dual carbon" goals, the importance of energy storage systems as a flexible regulatory resource for the power system is becoming increasingly prominent, especially in maintaining grid stability, promoting the consumption of new energy sources, and improving the economic efficiency of grid operation. Based on this, participating in the electricity market with reasonable operating strategies can not only leverage the regulatory characteristics of energy storage systems but also achieve significant economic benefits. Regarding optimized operating strategies for energy storage systems, their rapid response characteristics and energy storage capacity enable the utilization of resources that traditional generating units and renewable energy generation cannot effectively absorb, thereby reducing system operating costs.

[0003] However, my country's electricity market is still in its early stages of development, and the trading models and operating mechanisms for energy storage systems participating in the electricity market are still being explored. Existing research on the optimal scheduling of energy storage systems participating in the spot electricity market does not comprehensively consider the carbon trading costs of the system and the lifespan degradation costs of the energy storage system. The revenue scenarios for energy storage systems participating in the electricity market are relatively simple, and resource utilization is insufficient. The control precision and flexibility of energy storage systems are also lacking, resulting in low returns and high system costs for existing optimal scheduling methods for energy storage systems. Summary of the Invention

[0004] The purpose of this invention is to provide a method for the operation and optimized scheduling of energy storage systems oriented towards the needs of power systems. This method can significantly improve the economic benefits of energy storage systems oriented towards the needs of power systems in the electricity market, reduce the cost of energy storage systems, and make full use of resources to achieve precise control of energy storage systems.

[0005] The technical solution of this invention is as follows:

[0006] In a first aspect, this application provides a method for the operation and optimized scheduling of an energy storage system to meet the needs of a power system, which includes the following steps:

[0007] S1. Obtain the revenue of the energy storage system, the system's electricity purchase cost, and the ancillary service fees under the electricity market environment, and establish an upper-level energy storage system optimization model with the goal of maximizing the revenue of the energy storage system.

[0008] S2. Establish a lower-level system optimization model with the objective function of minimizing the sum of system electricity purchase cost and ancillary service fees;

[0009] S3. Based on the optimization models of the upper-level energy storage system and the lower-level system, a two-level optimization model is established through two-level planning (BLP).

[0010] S4. Solve the two-layer optimization model and output the energy storage system operation and optimization scheduling decisions.

[0011] Furthermore, in step S1, the revenue of the aforementioned energy storage system includes revenue from the spot electricity market and revenue from the frequency regulation ancillary services market; the revenue from the aforementioned frequency regulation ancillary services market includes revenue from frequency regulation capacity and revenue from frequency regulation mileage.

[0012] Furthermore, in step S1, the objective function and constraints of the aforementioned upper-level energy storage system optimization model include:

[0013] Objective function: , , ,

[0014] Energy storage system charge and discharge power constraints: , , , , , ,

[0015] Energy storage system state of charge constraints: , , ,

[0016] Energy storage system application capacity constraints: ,

[0017] In the formula, C represents the total revenue of the energy storage system. fre For the revenue of the frequency regulation ancillary services market for energy storage systems, C e For spot electricity market revenue, S is the total capacity of the energy storage system, and T is the total time. For energy storage system s to participate in the frequency regulation ancillary services market during time period t, a frequency regulation capacity bid is required. For energy storage system s, the frequency regulation capacity won in the frequency regulation ancillary service market during time period t. The frequency regulation mileage bid for energy storage system s to participate in the frequency regulation ancillary services market during time period t. The mileage of the energy storage system s participating in the frequency regulation ancillary services market during time period t is the frequency regulation mileage won in the bidding. The discharge power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The charging power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The electricity price for energy storage systems participating in the spot electricity market during time period t. The discharge power of the energy storage system s in the frequency regulation ancillary service market during time period t. The maximum power of energy storage system s, The charging power of the energy storage system s in the frequency regulation ancillary service market during time period t. For discharge efficiency, , All are binary variables. For charging efficiency, This refers to the maximum charging power of the energy storage system in both the frequency regulation ancillary services market and the spot electricity market. This represents the maximum discharge power of the energy storage system s in both the frequency regulation ancillary services market and the spot electricity market. The state of charge of the energy storage system. This is the lower limit of the state of charge of the energy storage system. This represents the upper limit of the state of charge of the energy storage system. This refers to the state of charge of the energy storage system during the initial scheduling period. This refers to the state of charge of the energy storage system during the final dispatch period. Let t be the frequency regulation capacity declared by the energy storage system s at time t. Let t be the frequency regulation mileage declared by the energy storage system s at time t.

[0018] Furthermore, in step S2, the objective function of the aforementioned lower-level system optimization model includes:

[0019] Objective function: ,

[0020] Power balance constraints: ,

[0021] Frequency modulation capacity and mileage requirements constraints: ,

[0022] In the formula, T represents the total time, and I represents the number of thermal power units. For thermal power unit i participating in the spot electricity market during time period t, the electricity generation price is declared. For thermal power unit i, the amount of electricity won in the spot electricity market during period t is [the amount of electricity won in the spot electricity market]. For thermal power unit i during time period t, the frequency regulation capacity price is declared in the frequency regulation market. For thermal power unit i during time period t, the frequency regulation capacity won in the frequency regulation market. For thermal power unit i during time period t, the declared frequency regulation mileage price in the frequency regulation market. For thermal power unit i during time period t, the frequency regulation mileage won in the frequency regulation market. The discharge price of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The discharge power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The charging price of energy storage system s in the frequency regulation ancillary services market and the spot electricity market during time period t. The charging power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The price of the frequency regulation capacity declared by the energy storage system s at time t. Let t be the frequency regulation capacity declared by the energy storage system s at time t. The price for the frequency regulation mileage declared by the energy storage system s at time t. The winning frequency regulation mileage declared by the energy storage system s at time t. Let t represent the discharge power of thermal power unit i in the frequency regulation ancillary service market during time period t. Let t represent the charging power of thermal power unit i in the frequency regulation ancillary service market during time period t. The discharge power of the energy storage system s in the frequency regulation ancillary service market during time period t. The charging power of the energy storage system s in the frequency regulation ancillary service market during time period t. The discharge power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The charging power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. Let t be the load power of the system during time period t. For Lagrange multipliers, To win bids for frequency modulation capacity in the frequency modulation ancillary services market, The price for clearing out frequency modulation capacity in the frequency modulation ancillary services market. To win bids for FM mileage in the FM ancillary services market, The price is set for clearing out FM mileage in the FM ancillary services market.

[0023] Furthermore, in step S4, the method for solving the bi-level optimization model is to use KKT conditions and duality theory to convert the bi-level optimization model into a single-level model for solution.

[0024] Secondly, this application provides an electronic device, comprising:

[0025] Memory, used to store one or more programs;

[0026] processor;

[0027] When one or more of the above programs are executed by the above processor, a method for the operation and optimized scheduling of an energy storage system oriented towards the needs of a power system, as described in any of the first aspects above, is implemented.

[0028] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for the operation and optimized scheduling of an energy storage system oriented towards the needs of a power system, as described in any of the first aspects above.

[0029] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0030] This invention provides a method for the operation and optimized scheduling of energy storage systems oriented towards the needs of power systems. By constructing a two-layer programming model with the upper layer aiming to maximize energy storage revenue and the lower layer aiming to minimize the sum of system electricity purchase cost and ancillary service fees, it significantly improves the economic benefits of energy storage systems under the electricity market, reduces the cost of energy storage systems, and makes full use of resources to achieve precise control of energy storage systems. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating the steps of an energy storage system operation and optimized scheduling method for power system needs according to the present invention.

[0033] Figure 2 This is a schematic structural block diagram of an electronic device according to an embodiment of the present invention.

[0034] Icons: 101, memory; 102, processor; 103, communication interface. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, 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. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0036] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0037] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0038] It should be noted that, in this document, the term "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0039] Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0040] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the various embodiments and features described below can be combined with each other.

[0041] Example 1

[0042] Please see Figure 1 , Figure 1 The diagram shows the steps of an energy storage system operation and optimized scheduling method for power system needs, as provided in an embodiment of this application.

[0043] In a first aspect, this application provides a method for the operation and optimized scheduling of an energy storage system to meet the needs of a power system, which includes the following steps:

[0044] S1. Obtain the revenue of the energy storage system, the system's electricity purchase cost, and the ancillary service fees under the electricity market environment, and establish an upper-level energy storage system optimization model with the goal of maximizing the revenue of the energy storage system.

[0045] S2. Establish a lower-level system optimization model with the objective function of minimizing the sum of system electricity purchase cost and ancillary service fees;

[0046] S3. Based on the optimization models of the upper-level energy storage system and the lower-level system, a two-level optimization model is established through two-level planning (BLP).

[0047] S4. Solve the two-layer optimization model and output the energy storage system operation and optimization scheduling decisions.

[0048] In a preferred implementation, in step S1, the revenue of the energy storage system includes revenue from the spot electricity market and revenue from the frequency regulation ancillary services market; the revenue from the frequency regulation ancillary services market includes revenue from frequency regulation capacity and revenue from frequency regulation mileage.

[0049] In a preferred embodiment, in step S1, the objective function and constraints of the upper-level energy storage system optimization model include:

[0050] Objective function: , , ,

[0051] Energy storage system charge and discharge power constraints: , , , , , ,

[0052] Energy storage system state of charge constraints: , , ,

[0053] Energy storage system application capacity constraints: ,

[0054] In the formula, C represents the total revenue of the energy storage system. fre For the revenue of the frequency regulation ancillary services market for energy storage systems, C e For spot electricity market revenue, S is the total capacity of the energy storage system, and T is the total time. For energy storage system s to participate in the frequency regulation ancillary services market during time period t, a frequency regulation capacity bid is required. For energy storage system s, the frequency regulation capacity won in the frequency regulation ancillary service market during time period t. The frequency regulation mileage bid for energy storage system s to participate in the frequency regulation ancillary services market during time period t. The mileage of the energy storage system s participating in the frequency regulation ancillary services market during time period t is the frequency regulation mileage won in the bidding. The discharge power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The charging power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The electricity price for energy storage systems participating in the spot electricity market during time period t. The discharge power of the energy storage system s in the frequency regulation ancillary service market during time period t. The maximum power of energy storage system s, The charging power of the energy storage system s in the frequency regulation ancillary service market during time period t. For discharge efficiency, , All are binary variables. For charging efficiency, This refers to the maximum charging power of the energy storage system in both the frequency regulation ancillary services market and the spot electricity market. This represents the maximum discharge power of the energy storage system s in both the frequency regulation ancillary services market and the spot electricity market. The state of charge of the energy storage system. This is the lower limit of the state of charge of the energy storage system. This represents the upper limit of the state of charge of the energy storage system. This refers to the state of charge of the energy storage system during the initial scheduling period. This refers to the state of charge of the energy storage system during the final dispatch period. Let t be the frequency regulation capacity declared by the energy storage system s at time t. Let t be the frequency regulation mileage declared by the energy storage system s at time t.

[0055] In a preferred implementation, in step S2, the objective function of the lower-level system optimization model includes:

[0056] Objective function: ,

[0057] Power balance constraints: ,

[0058] Frequency modulation capacity and mileage requirements constraints: ,

[0059] In the formula, T represents the total time, and I represents the number of thermal power units. For thermal power unit i participating in the spot electricity market during time period t, the electricity generation price is declared. For thermal power unit i, the amount of electricity won in the spot electricity market during period t is [the amount of electricity won in the spot electricity market]. For thermal power unit i during time period t, the frequency regulation capacity price is declared in the frequency regulation market. For thermal power unit i during time period t, the frequency regulation capacity won in the frequency regulation market. For thermal power unit i during time period t, the declared frequency regulation mileage price in the frequency regulation market. For thermal power unit i during time period t, the frequency regulation mileage won in the frequency regulation market. The discharge price of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The discharge power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The charging price of energy storage system s in the frequency regulation ancillary services market and the spot electricity market during time period t. The charging power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The price of the frequency regulation capacity declared by the energy storage system s at time t. Let t be the frequency regulation capacity declared by the energy storage system s at time t. The price for the frequency regulation mileage declared by the energy storage system s at time t. The winning frequency regulation mileage declared by the energy storage system s at time t. Let t represent the discharge power of thermal power unit i in the frequency regulation ancillary service market during time period t. Let t represent the charging power of thermal power unit i in the frequency regulation ancillary service market during time period t. The discharge power of the energy storage system s in the frequency regulation ancillary service market during time period t. The charging power of the energy storage system s in the frequency regulation ancillary service market during time period t. The discharge power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The charging power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. Let t be the load power of the system during time period t. For Lagrange multipliers, To win bids for frequency modulation capacity in the frequency modulation ancillary services market, The price for clearing out frequency modulation capacity in the frequency modulation ancillary services market. To win bids for FM mileage in the FM ancillary services market, The price is set for clearing out FM mileage in the FM ancillary services market.

[0060] As a preferred implementation, in step S4, the method for solving the bi-layer optimization model uses KKT conditions and duality theory to convert the bi-layer optimization model into a single-layer model for solution.

[0061] Example 2

[0062] Please see Figure 2 , Figure 2 This is a schematic structural block diagram of an electronic device provided in an embodiment of this application.

[0063] An electronic device includes a memory 101, a processor 102, and a communication interface 103. The memory 101, processor 102, and communication interface 103 are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules. The processor 102 executes the software programs and modules stored in the memory 101 to perform various functional applications and data processing. The communication interface 103 can be used for signaling or data communication with other node devices.

[0064] The memory 101 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0065] The processor 102 can be an integrated circuit chip with signal processing capabilities. The processor 102 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0066] It is understood that the structure shown in the figure is for illustrative purposes only. A method for operating and optimizing the scheduling of energy storage systems to meet the needs of power systems may include more or fewer components than those shown in the figure, or have a different configuration. The components shown in the figure can be implemented using hardware, software, or a combination thereof.

[0067] In the embodiments provided in this application, it should be understood that the disclosed methods can also be implemented in other ways. The embodiments described above are merely illustrative. For example, the flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0068] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0069] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0070] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0071] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for the operation and optimized scheduling of energy storage systems oriented towards the needs of power systems, characterized in that, Includes the following steps: S1. Obtain the revenue of the energy storage system, the system's electricity purchase cost, and the ancillary service fees under the electricity market environment, and establish an upper-level energy storage system optimization model with the goal of maximizing the revenue of the energy storage system. S2. Establish a lower-level system optimization model with the objective function of minimizing the sum of system electricity purchase cost and ancillary service fees; S3. Based on the optimization models of the upper-level energy storage system and the lower-level system, a two-level optimization model is established through two-level planning (BLP). S4. Solve the two-layer optimization model and output the energy storage system operation and optimization scheduling decisions.

2. The method for operation and optimized scheduling of energy storage systems oriented towards power system demand as described in claim 1, characterized in that, In step S1, the revenue of the energy storage system includes revenue from the spot electricity market and revenue from the frequency regulation ancillary services market; the revenue from the frequency regulation ancillary services market includes revenue from frequency regulation capacity and revenue from frequency regulation mileage.

3. The method for operation and optimized scheduling of energy storage systems oriented towards power system demand as described in claim 2, characterized in that, In step S1, the objective function and constraints of the upper-level energy storage system optimization model include: Objective function: , , , Energy storage system charge and discharge power constraints: , , , , ， , Energy storage system state of charge constraints: , , , Energy storage system application capacity constraints: , In the formula, C represents the total revenue of the energy storage system. fre For the revenue of the frequency regulation ancillary services market for energy storage systems, C e For spot electricity market revenue, S is the total capacity of the energy storage system, and T is the total time. For energy storage system s to participate in the frequency regulation ancillary services market during time period t, a frequency regulation capacity bid is required. For energy storage system s, the frequency regulation capacity won in the frequency regulation ancillary service market during time period t. The frequency regulation mileage bid for energy storage system s to participate in the frequency regulation ancillary services market during time period t. The mileage of the energy storage system s participating in the frequency regulation ancillary services market during time period t is the frequency regulation mileage won in the bidding. The discharge power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The charging power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The electricity price for energy storage systems participating in the spot electricity market during time period t. The discharge power of the energy storage system s in the frequency regulation ancillary service market during time period t. The maximum power of energy storage system s, The charging power of the energy storage system s in the frequency regulation ancillary service market during time period t. For discharge efficiency, , All are binary variables. For charging efficiency, This refers to the maximum charging power of the energy storage system in both the frequency regulation ancillary services market and the spot electricity market. This represents the maximum discharge power of the energy storage system s in both the frequency regulation ancillary services market and the spot electricity market. The state of charge of the energy storage system. This represents the lower limit of the state of charge (SOC) of an energy storage system. This represents the upper limit of the state of charge of the energy storage system. This refers to the state of charge of the energy storage system during the initial scheduling period. This refers to the state of charge of the energy storage system during the final dispatch period. Let t be the frequency regulation capacity declared by the energy storage system s at time t. Let t be the frequency regulation mileage declared by the energy storage system s at time t.

4. The method for operation and optimized scheduling of energy storage systems oriented towards power system demand as described in claim 1, characterized in that, In step S2, the objective function of the lower-level system optimization model includes: Objective function: , Power balance constraints: , Frequency modulation capacity and mileage requirements constraints: , In the formula, T represents the total time, and I represents the number of thermal power units. For thermal power unit i participating in the spot electricity market during time period t, the electricity generation price is declared. For thermal power unit i, the amount of electricity won in the spot electricity market during period t is [the amount of electricity won in the spot electricity market]. For thermal power unit i during time period t, the frequency regulation capacity price is declared in the frequency regulation market. For thermal power unit i during time period t, the frequency regulation capacity won in the frequency regulation market. For thermal power unit i during time period t, the declared frequency regulation mileage price in the frequency regulation market. For thermal power unit i during time period t, the frequency regulation mileage won in the frequency regulation market. The discharge price of energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The discharge power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The charging price of energy storage system s in the frequency regulation ancillary services market and the spot electricity market during time period t. The charging power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The price of the frequency regulation capacity declared by the energy storage system s at time t. Let t be the frequency regulation capacity declared by the energy storage system s at time t. The price for the frequency regulation mileage declared by the energy storage system s at time t. The winning frequency regulation mileage declared by the energy storage system s at time t. Let t represent the discharge power of thermal power unit i in the frequency regulation ancillary service market during time period t. Let t represent the charging power of thermal power unit i in the frequency regulation ancillary service market during time period t. The discharge power of the energy storage system s in the frequency regulation ancillary service market during time period t. The charging power of the energy storage system s in the frequency regulation ancillary service market during time period t. The discharge power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. The charging power of the energy storage system s in the frequency regulation ancillary service market and the spot electricity market during time period t. Let t be the load power of the system during time period t. For Lagrange multipliers, To win bids for frequency modulation capacity in the frequency modulation ancillary services market, The price for clearing out frequency modulation capacity in the frequency modulation ancillary services market. To win bids for FM mileage in the FM ancillary services market, The price is set for clearing out FM mileage in the FM ancillary services market.

5. The method for operation and optimized scheduling of energy storage systems oriented towards power system demand as described in claim 1, characterized in that, In step S4, the method for solving the bi-level optimization model is to use KKT conditions and duality theory to convert the bi-level optimization model into a single-level model for solution.

6. An electronic device, characterized in that, include: Memory, used to store one or more programs; processor; When the processor executes the one or more programs, it implements a method for the operation and optimized scheduling of an energy storage system oriented towards the needs of a power system, as described in any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements a method for the operation and optimized scheduling of an energy storage system oriented towards the needs of a power system, as described in any one of claims 1-5.

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