CN118336715A - Optimization method and equipment for flexible load participation in power grid regulation based on time-series coupling - Google Patents

Abstract

According to the optimization method and the equipment for the flexible load participating in the power grid regulation based on the time sequence coupling, the flexible load can be effectively scheduled and managed through the established optimization model, so that the flexible load can flexibly adjust the power output according to the power grid requirement, and therefore reliable support is provided when the load of the power system changes. Meanwhile, an optimization problem solving method considering the coupling relation of the adjacent time period feasible domains is provided. According to the method, the coupling relation among different time periods in the power system is considered, the information of the adjacent time periods is effectively utilized, and the scheduling scheme of the flexible load is optimized, so that the running efficiency and the stability of the power grid are improved to the greatest extent. The invention establishes the optimization model of the flexible load participating in the power grid, provides the optimization problem solving method for the coupling relation of the feasible domains in adjacent time intervals, provides more reliable and efficient support for the operation of the power system, and promotes the development of the power industry to the intelligent and flexible directions.

Description

Optimization method and equipment for flexible load participation in power grid regulation based on time-series coupling

Technical Field

The present invention relates to the field of electric power technology, and in particular to an optimization method and device for flexible loads participating in power grid regulation based on timing coupling.

Background technique

At present, renewable energy power generation with random and volatile output has developed rapidly in my country, but the resulting absorption problem has become a major problem that plagues the power engineering industry. Inadequate absorption of new energy will lead to unnecessary abandonment of wind and solar power, affecting the economic efficiency of power grid operation, and in severe cases, affecting the safe and stable operation of the power grid. Generally speaking, there are three ideas. One is to study the high-precision prediction method of intermittent power sources to reduce the prediction error, so as to directly reduce the backup arrangement. The second is to optimize the power generation plan based on the probability and error of renewable energy. However, the above two methods are considered from the perspective of power supply, following the traditional idea of "power generation tracking load". In the future, power grid operation control needs to change this idea and change the traditional power generation dispatching to "source-load dual-side dispatching". Therefore, the third method is to use load regulation capability to balance the fluctuation of intermittent power sources, which has become a research hotspot at home and abroad.

However, traditional grid regulation methods often fail to fully consider the characteristics of flexible loads, resulting in low power system operation efficiency, large load fluctuations, etc. To address this challenge, a method for establishing an optimization model for flexible loads participating in the grid is proposed.

Summary of the invention

The present invention proposes an optimization method, device and storage medium for flexible loads participating in power grid regulation based on timing coupling, which can solve at least one of the technical problems in the background technology.

To achieve the above object, the present invention adopts the following technical solutions:

An optimization method for flexible loads participating in power grid regulation based on time sequence coupling, wherein the following steps are performed by a computer device:

(1) Obtaining basic information of flexible loads participating in grid regulation, including but not limited to the following data: the upper and lower limits of the adjustable power of the flexible loads, the upward and downward adjustment speeds of the adjustable power of the flexible loads, the minimum shutdown holding time and the minimum startup holding time of the flexible loads, the unit electricity subsidy price of the flexible loads participating in grid regulation, and the basic subsidy fee for a single regulation.

(2) On the basis of satisfying the flexible load operation characteristics, the regulation cost of the flexible load participating in the grid regulation is reduced as much as possible, that is, on the basis of satisfying the safe operation, the economical operation is ensured. An optimization model for the flexible load participating in the grid regulation is established.

(3) Solve the optimization model established in step (2) and propose a method for solving the optimization problem that takes into account the coupling relationship between the feasible domains of adjacent time periods.

Furthermore, the step (2) specifically includes the following steps:

(2a) The constraints for establishing the optimization model for flexible loads to participate in grid regulation are as follows:

1) The sum of the adjustable active power of the flexible load in any period should be equal to the expected value given by the power dispatching department, that is:

Where: N is the number of flexible loads; t is any time period; Pi _,t is the active power of the i-th flexible load in time period t; ui _,t is the status flag of the i-th flexible load participating in grid regulation in time period t, ui _,t = 1, it means that the flexible load participates in grid regulation in time period t; otherwise, it means that the flexible load does not participate in grid regulation in time period t. _Pd,t is the expected value of participating load regulation given by the power dispatching department.

2) The flexible load will be subject to the upper and lower limits of its own regulating power at any time, namely:

in: and are the maximum and minimum values of the adjustable capacity of the i-th flexible load respectively.

3) The adjustment rate of the flexible load cannot be constrained, and the upward adjustment speed and the downward adjustment speed cannot exceed their maximum values, that is:

in: and They are the upward adjustment speed and the downward adjustment speed for flexible loads respectively.

4) To prevent the flexible load from frequently participating in the regulation, its state must be maintained for a certain period of time. That is, when it responds to the grid regulation, it must maintain a certain period of time according to the given power; once it exits the regulation mode, it must also maintain a certain period of time before participating in the response again. The following constraints must be met:

Where: TO _i is the minimum holding time for flexible load i to participate in grid regulation. During this time period, its active load operates within the power regulation range declared in advance, that is, it meets the constraints of formula (2). In order to prevent frequent regulation operations on flexible load i, it is required to maintain at least a period of time after participating in grid regulation, during which no regulation instructions are accepted. This period of time is defined as TS _i .

5) In order to cope with emergencies, the adjustment of flexible loads is required to have a certain reserve capacity, namely:

Where: ρ is the hot standby coefficient

(2b) The objective function of the optimization model for flexible loads to participate in grid regulation is as follows:

Wherein: α _i,t is the unit electricity subsidy price of flexible load i in period t. Due to the load operation characteristics and regional factors, the unit electricity subsidy price α _i,t of each flexible load in different periods may not be exactly the same; β _i is the basic subsidy cost for a single regulation of flexible load i in the entire regulation time T.

Furthermore, the step (3) specifically includes the following steps:

(3a) The Lagrangian relaxation of the original problem is expressed as follows:

Among them: u and P represent the column vector of the state flags of N flexible loads participating in grid regulation and the column vector of the regulated power in the T time period, respectively. λ represents the Lagrange multiplier column vector corresponding to the power balance constraint; μ and ν represent the Lagrange multiplier column vector corresponding to the start and stop time constraints of each flexible load at each moment, respectively. The specific expressions are as follows:

(3b) Considering that solving the optimal solution of the original problem is to solve the lower bound of the dual problem, the dual problem of the optimization problem described by formula (7) is expressed as:

Where: Ω _t is the feasible area of P _t , _ut in period t.

The feasible domain Ω _t is divided into the following two parts:

Where: φ _t is an N-dimensional state variable set of flexible loads whose elements are 0 or 1 and participate in the adjustment.

At the same time, considering the maximum and minimum output constraints and climbing rate constraints of the flexible load participating in the regulation, we have:

(3c) Based on solving the unit commitment problem in different time periods, the feasible domain of time period t is constrained by the state of the units in the previous time period and the previous time period. At the same time, the size of the time interval is related to the degree of constraint of the feasible domain. Therefore, the Lagrangian relaxation problem can be divided into two levels of optimization problems to be solved:

1) Solving the underlying problem:

2) Solving the upper-level problem:

Where: L ^* is the function value of the underlying optimization problem when the Lagrange multiplier is given.

(3d) Considering that the coupling between two adjacent time periods is reflected in the feasible domain, the optimization problems described by formula (12) and formula (13) can be solved separately in chronological order, and the minimum value of the two calculation results is taken as the optimal solution at time t. The steps are shown in Figure 2 of the accompanying drawings, and there are the following two solutions:

Solution 1: The initial state is set to the feasible domain Ω _t determined by the t-1 period; then, the sub-dual problem of the t period is solved to obtain the optimal solution; finally, the optimal solution of the next period t+1 is solved.

Solution 2: Set the initial state to time t-1, and determine the feasible domain Ω t+1 for time period _t+1 ; secondly, find the optimal solution for time period t+1; finally, find the optimal solution for the previous time period t.

Compare the results of the two solutions and select the last one as the optimal solution for the optimization problem in period t.

On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

It can be seen from the above technical solutions that the optimization method of the flexible load participating in the grid regulation based on time-sequence coupling of the present invention, the optimization model established is intended to integrate the flexibility and adjustability of the flexible load into the grid regulation process, so as to improve the economy and stability of the power system operation. Through this model, the flexible load can be effectively dispatched and managed, so that it can flexibly adjust the power output according to the grid demand, thereby providing reliable support when the power system load changes.

At the same time, for this optimization model, a method for solving the optimization problem taking into account the coupling relationship between the feasible domains of adjacent time periods is proposed. This method takes into account the coupling relationship between different time periods in the power system, effectively utilizes the information of adjacent time periods, and optimizes the dispatching scheme of flexible loads to maximize the operation efficiency and stability of the power grid.

In summary, the present invention establishes an optimization model for flexible loads to participate in the power grid and proposes a method for solving the optimization problem taking into account the coupling relationship between feasible domains of adjacent time periods, which will provide more reliable and efficient support for the operation of the power system and promote the development of the power industry towards intelligence and flexibility.

Specifically, compared with the prior art, the present invention has the following advantages:

1. An optimization model for flexible loads to participate in grid regulation is established, which fully considers the adjustable capacity of flexible loads and maximizes the effect of their participation in grid regulation. By making full use of the flexibility and adjustability of flexible loads, it is possible to more effectively cope with load fluctuations and imbalances in energy supply and demand in the power system, and improve the operating efficiency and stability of the power system.

2. A method for solving the optimization problem that takes into account the coupling relationship between the feasible domains of adjacent time periods is proposed for the established optimization model. By considering the coupling relationship between adjacent time periods, the optimization solution method can more accurately determine the dispatching scheme of flexible loads, improve the accuracy and effect of dispatching, and thus further improve the operating efficiency and economy of the power system.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a flow chart of the method of the present invention;

FIG2 is a flow chart of a solution method taking into account the coupling between two adjacent time periods.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

As shown in FIG1 , the optimization method for flexible loads participating in grid regulation based on the time series coupling method described in this embodiment includes the following steps:

(1) Obtaining basic information of flexible loads participating in grid regulation, including but not limited to the following data: the upper and lower limits of the adjustable power of the flexible loads, the upward and downward adjustment speeds of the adjustable power of the flexible loads, the minimum shutdown holding time and the minimum startup holding time of the flexible loads, the unit electricity subsidy price of the flexible loads participating in grid regulation, and the basic subsidy fee for a single regulation.

(2) On the basis of satisfying the flexible load operation characteristics, the regulation cost of the flexible load participating in the grid regulation is reduced as much as possible, that is, on the basis of satisfying the safe operation, the economical operation is ensured. An optimization model for the flexible load participating in the grid regulation is established.

(3) Solve the optimization model established in step (2) and propose a method for solving the optimization problem that takes into account the coupling relationship between the feasible domains of adjacent time periods.

Furthermore, the step (2) specifically includes the following steps:

(2a) The constraints for establishing the optimization model for flexible loads to participate in grid regulation are as follows:

1) The sum of the adjustable active power of the flexible load in any period should be equal to the expected value given by the power dispatching department, that is:

Where: N is the number of flexible loads; t is any time period; Pi _,t is the active power of the i-th flexible load in time period t; ui _,t is the status flag of the i-th flexible load participating in grid regulation in time period t, ui _,t = 1, it means that the flexible load participates in grid regulation in time period t; otherwise, it means that the flexible load does not participate in grid regulation in time period t. _Pd,t is the expected value of participating load regulation given by the power dispatching department.

2) The flexible load will be subject to the upper and lower limits of its own regulating power at any time, namely:

in: and are the maximum and minimum values of the adjustable capacity of the i-th flexible load respectively.

3) The adjustment rate of the flexible load cannot be constrained, and the upward adjustment speed and the downward adjustment speed cannot exceed their maximum values, that is:

in: and They are the upward adjustment speed and the downward adjustment speed for flexible loads respectively.

4) To prevent the flexible load from frequently participating in the regulation, its state must be maintained for a certain period of time. That is, when it responds to the grid regulation, it must maintain a certain period of time according to the given power; once it exits the regulation mode, it must also maintain a certain period of time before participating in the response again. The following constraints must be met:

Where: TO _i is the minimum holding time for flexible load i to participate in grid regulation. During this time period, its active load operates within the power regulation range declared in advance, that is, it meets the constraints of formula (2). In order to prevent frequent regulation operations on flexible load i, it is required to maintain at least a period of time after participating in grid regulation, during which no regulation instructions are accepted. This period of time is defined as TS _i .

5) In order to cope with emergencies, the adjustment of flexible loads is required to have a certain reserve capacity, namely:

Where: ρ is the hot standby coefficient

(2b) The objective function of the optimization model for flexible loads to participate in grid regulation is as follows:

Wherein: α _i,t is the unit electricity subsidy price of flexible load i in period t. Due to the load operation characteristics and regional factors, the unit electricity subsidy price α _i,t of each flexible load in different periods may not be exactly the same; β _i is the basic subsidy cost for a single regulation of flexible load i in the entire regulation time T.

Wherein, the step (3) specifically comprises the following steps:

(3a) The Lagrangian relaxation of the original problem is expressed as follows:

Among them: u and P represent the column vector of the state flags of N flexible loads participating in grid regulation and the column vector of the regulated power in the T time period, respectively. λ represents the Lagrange multiplier column vector corresponding to the power balance constraint; μ and ν represent the Lagrange multiplier column vector corresponding to the start and stop time constraints of each flexible load at each moment, respectively. The specific expressions are as follows:

(3b) Considering that solving the optimal solution of the original problem is to solve the lower bound of the dual problem, the dual problem of the optimization problem described by formula (7) is expressed as:

Where: Ω _t is the feasible area of P _t , _ut in period t.

The feasible domain Ω _t is divided into the following two parts:

Where: φ _t is an N-dimensional state variable set of flexible loads whose elements are 0 or 1 and participate in the adjustment.

At the same time, considering the maximum and minimum output constraints and climbing rate constraints of the flexible load participating in the regulation, we have:

(3c) Based on solving the unit commitment problem in different time periods, the feasible domain of time period t is constrained by the state of the units in the previous time period and the previous time period. At the same time, the size of the time interval is related to the degree of constraint of the feasible domain. Therefore, the Lagrangian relaxation problem can be divided into two levels of optimization problems to be solved:

1) Solving the underlying problem:

2) Solving the upper-level problem:

Where: L ^* is the function value of the underlying optimization problem when the Lagrange multiplier is given.

(3d) Considering that the coupling between two adjacent time periods is reflected in the feasible domain, the optimization problems described by formula (12) and formula (13) can be solved separately in chronological order, and the minimum value of the two calculation results is taken as the optimal solution at time t. The steps are shown in Figure 2 of the accompanying drawings, and there are the following two solutions:

Solution 1: The initial state is set to the feasible domain Ω _t determined by the t-1 period; then, the sub-dual problem of the t period is solved to obtain the optimal solution; finally, the optimal solution of the next period t+1 is solved.

Solution 2: Set the initial state to time t-1, and determine the feasible domain Ω t+1 for time period _t+1 ; secondly, find the optimal solution for time period t+1; finally, find the optimal solution for the previous time period t.

Compare the results of the two solutions and select the last one as the optimal solution for the optimization problem in period t.

In summary, traditional grid regulation methods often fail to fully consider the adjustable characteristics of flexible loads, resulting in low power system operation efficiency, large load fluctuations and other problems. The present invention fully considers the adjustable capacity of flexible loads, establishes an optimization model for flexible loads to participate in grid regulation, and innovatively proposes a method for solving optimization problems that takes into account the coupling relationship between feasible domains of adjacent time periods. This method takes into account the coupling relationship between different time periods in the power system, effectively utilizes the information of adjacent time periods, and optimizes the scheduling plan of flexible loads to maximize the operation efficiency and economy of the power grid.

On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

In another embodiment provided in the present application, a computer program product including instructions is also provided. When the computer program product is executed on a computer, the computer executes any optimization method for flexible loads participating in grid regulation based on timing coupling in the above embodiments.

It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

The embodiment of the present application also provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus.

Memory, used to store computer programs;

The processor is used to implement the above-mentioned optimization method for flexible loads participating in power grid regulation based on timing coupling when executing the program stored in the memory.

The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

The communication interface is used for communication between the above electronic device and other devices.

The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

The above-mentioned processor 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 gates or transistor logic devices, and discrete hardware components.

In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (5)

1. The optimization method for the flexible load to participate in the power grid regulation based on the time sequence coupling is characterized by comprising the following steps of,

S1, acquiring basic information of flexible loads participating in power grid regulation, wherein the basic information comprises the following data: the upper and lower power adjustable limits of the flexible load, the upward and downward adjusting speeds of the adjustable power of the flexible load, the minimum shutdown and start-up holding time of the flexible load, the unit electric energy subsidy price of the flexible load participating in the power grid adjustment and the single adjustment basic subsidy cost;

S2, establishing an optimization model of the flexible load participating in power grid regulation; on the basis of meeting the flexible load operation characteristic, reducing the adjustment cost of the flexible load participating in the power grid adjustment;

and S3, solving the optimization model established in the step S2, and providing an optimization problem solving method considering the adjacent time period feasible domain coupling relation.

2. The optimization method for time sequence coupling-based flexible load participation power grid regulation according to claim 1, wherein: the step S2 specifically includes the following steps:

(2a) The constraint conditions for establishing an optimization model of the flexible load participating in the power grid regulation are as follows:

1) The sum of the adjustable active power of the flexible load at any time period is equal to the expected value given by the power dispatching department, namely:

Wherein: n is the number of flexible loads; t is any period of time; p _i,t is the active power of the ith flexible load t period; u _i,t is a state flag of the ith flexible load participating in power grid regulation in the t period, and u _i,t =1, and indicates that the flexible load participates in power grid regulation in the t period; otherwise, the flexible load is indicated not to participate in the power grid regulation in the period t; p _d,t is the expected value given by the power dispatching department to participate in load regulation;

2) The flexible load will be constrained by the upper and lower limits of self-regulated power at any time period, namely:

u_i,tP_i ^min≤P_i,t≤u_i,tP_i ^max(2)

Wherein: p _i ^max and P _i ^min are the maximum and minimum values, respectively, of the ith flexible load adjustable capacity;

3) The rate of adjustment of the flexible load cannot be constrained and the up-adjustment speed and the down-adjustment speed cannot exceed their maximum values, namely:

Wherein: And The flexible load adjusts the speed upwards and adjusts the speed downwards respectively;

4) To prevent the flexible load from frequently participating in the adjustment, its state must be maintained for a certain time; i.e. it must be maintained for a certain time at a given power when it responds to grid regulation; once the regulation mode is exited, the regulation mode is also required to be kept for a certain time to participate in response again; the following constraints need to be satisfied:

wherein: TO _i is the minimum holding time of the flexible load i participating in the power grid regulation, and in the period, the active load operates in a power regulation range declared in advance, namely, the constraint of the formula (2) is satisfied; to prevent frequent regulation operations of the flexible load i, it is required to remain at least for a period of time after participating in the grid regulation, during which no regulation command is accepted, this period of time being defined as TS _i;

5) In order to cope with an emergency situation, the adjustment of the flexible load is required to have a certain spare capacity, namely:

wherein: ρ is the thermal reserve coefficient

(2B) The objective function of the optimization model of the flexible load participating in the power grid regulation is established as follows:

Wherein: alpha _i,t is the price of the unit electric energy patch of the flexible load i in the t period, and is limited by the load operation characteristics and factors such as the region, and the price alpha _i,t of the unit electric energy patch of each flexible load in different periods is not necessarily identical; beta _i is the single adjustment base subsidy cost of the flexible load i over the adjustment time T.

3. The optimization method for time sequence coupling-based flexible load participation power grid regulation according to claim 1, wherein: the step S3 specifically comprises the following steps:

(3a) The Lagrange relaxation problem of the original problem is expressed as follows:

wherein: u and P respectively represent column vectors of state marks of N flexible loads participating in power grid regulation and column vectors of regulation power in a period of T; λ represents a corresponding lagrange multiplier column vector that satisfies a power balance constraint; μ and ν respectively represent Lagrangian multiplier column vectors corresponding to the meeting of the flexible load start-stop time constraint at each moment; the specific expression is as follows:

(3b) Considering solving the original problem optimal solution, i.e. solving the lower bound of the dual problem, the dual problem of the optimization problem described by equation (7) is expressed as:

Wherein: omega _t is the feasible region of the t period P _t、u_t;

The feasible region Ω _t is divided into two parts:

wherein: phi _t is an N-dimensional state variable set of which the element is 0 or 1 and the flexible load participates in adjustment;

meanwhile, considering the maximum and minimum output constraint and the climbing rate constraint of the flexible load participating in adjustment, the following are:

(3c) On the basis of solving the unit combination problem in a time-sharing manner, the feasible domain of the t time period is restricted by the unit state of the previous time period and the previous time period; meanwhile, the time interval size is related to the limit of the feasible region;

Then two layers of optimization problem solving are divided:

1) Solving the bottom layer problem:

2) Solving the upper layer problem:

Wherein: l ^* is the function value of the bottom layer optimization problem when Lagrangian multiplier is given;

(3d) Considering that the coupling between two adjacent time periods is embodied in a feasible domain, the optimization problem described by the formula (12) and the formula (13) is solved respectively in time sequence, and the two operation results are compared to take the minimum value as the optimal solution at the moment t.

4. A method of optimizing the participation of a flexible load in power grid regulation based on time series coupling as claimed in claim 3, wherein: step (3 d) has two schemes:

scheme 1: the initial state is set as a feasible region omega _t determined by a t-1 period; then, solving sub-dual problems of the t period to obtain an optimal solution; finally, solving the optimal solution of the next time period t+1;

scheme 2: setting an initial state as a t-1 moment, and determining a feasible region omega _t+1 of a t+1 period; secondly, solving an optimal solution for the t+1 time period; finally, solving an optimal solution of the last time period t;

comparing the results of the two schemes, and selecting the last optimal solution as the optimal problem t period.

5. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method of any of claims 1 to 4.