CN117077980A - Carbon emission scheduling methods, devices and electronic equipment - Google Patents

Abstract

The disclosure provides a carbon emission scheduling method, a carbon emission scheduling device and electronic equipment. The specific implementation scheme is as follows: constructing a power demand response model based on the change relation between the carbon emission intensity of each node in the electricity carbon price and consumption demand side and the power demand of the corresponding node; constructing a power generation cost function based on a change relation between the price of electric carbon, the carbon emission intensity of each generator set in the power generation side and the power generation cost of the power generation side; determining constraint conditions of a power generation cost function based on the power demand response model; solving the minimum value of the power generation cost function based on constraint conditions to obtain a carbon emission scheduling scheme of the predicted carbon emission intensity of the generator set on the power generation side along with the change of the predicted electric carbon price; and carrying out carbon emission scheduling on the power generation side based on the carbon emission scheduling scheme. By adopting the technical scheme disclosed by the invention, the carbon emission of the power grid can be reduced.

Description

Carbon emission scheduling methods, devices and electronic equipment

Technical field

The present disclosure relates to the field of power control technology. The present disclosure specifically relates to a carbon emission scheduling method, device and electronic equipment.

Background technique

The application of Industrial Internet of Things (IIoTs) brings advanced measurement and communication technologies to the power grid, such as network technology and smart meters, making the power grid smarter than ever before. The widespread use of smart devices has significantly enhanced the observability and controllability of the demand side of smart grids, and provided powerful hardware and data support for the development of demand response (DR). DR refers to changes in a customer's electricity usage compared to their normal consumption patterns when electricity prices change over time or as an inducement to reduce electricity use when wholesale electricity market prices are high or system reliability is threatened.

DR programs typically include price-based demand response (PBDR) and incentive-based DR (IBDR). In a PBDR scheme, consumers respond to real-time prices based on a demand elasticity model and modify their demand by shifting flexible loads from peak to off-peak hours to flatten the load curve. PBDR plans are commonly used in day-ahead and real-time market clearing, economic dispatch, and grid planning. Successful PBDR can help the electricity market set effective electricity prices, improve economic efficiency, and reduce environmental costs and carbon emissions.

Contents of the invention

The present disclosure provides a carbon emission scheduling method, device and electronic equipment, which can solve the above problems.

According to one aspect of the present disclosure, a carbon emission scheduling method is provided, including:

Based on the changing relationship between the electricity carbon price, the carbon emission intensity of each node on the consumer demand side, and the power demand of the corresponding node, a power demand response model is constructed;

Construct a power generation cost function based on the changing relationship between the electricity carbon price, the carbon emission intensity of each generating unit on the power generation side, and the power generation cost on the power generation side;

Based on the power demand response model, determine constraints on the power generation cost function;

Based on the constraints, solve the minimum value of the power generation cost function to obtain a carbon emission scheduling plan in which the predicted carbon emission intensity of the generating units on the power generation side changes with the predicted electricity carbon price;

Based on the carbon emission scheduling plan, carbon emission scheduling is performed on the power generation side.

According to another aspect of the present disclosure, a carbon emission scheduling device is provided, including:

The demand response model building module is used to construct a power demand response model based on the changing relationship between the electricity carbon price, the carbon emission intensity of each node on the consumer demand side, and the power demand of the corresponding node;

A power generation cost function building module, configured to construct a power generation cost function based on the changing relationship between the electricity carbon price, the carbon emission intensity of each generating unit on the power generation side, and the power generation cost on the power generation side;

A constraint determination module, configured to determine the constraints of the power generation cost function based on the power demand response model;

A carbon emission plan determination module is used to solve the minimum value of the power generation cost function based on the constraint conditions, and obtain a carbon emission scheduling plan in which the predicted carbon emission intensity of the generating units in the power generation side changes with the predicted electricity carbon price. ;

A carbon emission scheduling module is used to perform carbon emission scheduling on the power generation side based on the carbon emission scheduling plan.

According to another aspect of the present disclosure, an electronic device is provided, including:

at least one processor; and

A memory communicatively connected to the at least one processor; wherein,

The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute any carbon emission scheduling method in the embodiment of the present disclosure.

According to the technology of the present disclosure, a power demand response model is constructed based on the changing relationship between the electricity carbon price, the carbon emission intensity of each node on the consumer demand side, and the power demand of the corresponding node; at the same time, based on the electricity carbon price, The changing relationship between the carbon emission intensity of each generating unit on the power generation side and the power generation cost on the power generation side is used to construct a power generation cost function; thus, based on the power demand response model, the constraints of the power generation cost function are determined, and then based on the Constraint conditions, solve the power generation cost function to minimize the power generation cost, and obtain a carbon emission plan in which the predicted carbon emission intensity changes with the predicted electricity carbon price. In this way, according to such a carbon emission scheduling plan, carbon emission scheduling on the power generation side can take into account the power demand on the consumer demand side, thereby reducing carbon emissions.

It should be understood that what is described in this section is not intended to identify key or important features of the embodiments of the disclosure, nor is it intended to limit the scope of the disclosure. Other features of the present disclosure will become readily understood from the following description.

Description of the drawings

The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. in:

Figure 1 is a flow chart of a carbon emission scheduling method according to an embodiment of the present disclosure;

Figure 2 is a structural block diagram of a carbon emission scheduling device according to an embodiment of the present disclosure;

Figure 3 is a block diagram of an electronic device used to implement the carbon emission scheduling method according to an embodiment of the present disclosure.

Detailed ways

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the present disclosure are included to facilitate understanding and should be considered to be exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the disclosure. Also, descriptions of well-known functions and constructions are omitted from the following description for clarity and conciseness.

Figure 1 is a flow chart of a carbon emission scheduling method according to an embodiment of the present disclosure.

As shown in Figure 1, the carbon emission scheduling method can include:

S110, build a power demand response model based on the changing relationship between the electricity carbon price, the carbon emission intensity of each node on the consumer demand side, and the power demand of the corresponding node;

S120: Construct a power generation cost function based on the changing relationship between the electricity carbon price, the carbon emission intensity of each generating unit on the power generation side, and the power generation cost on the power generation side;

S130, based on the power demand response model, determine the constraints of the power generation cost function;

S140, based on the constraint conditions, solve the power generation cost function to minimize the power generation cost, and obtain a carbon emission scheduling plan in which the predicted carbon emission intensity changes with the predicted electricity carbon price;

S150, based on the carbon emission scheduling plan, perform carbon emission scheduling on the power generation side.

Understandably, the power demand response model is obtained by fitting actual data such as the actual electricity carbon price at time t, the carbon emission intensity of each node on the consumer demand side at time t, and the power demand of each node.

Understandably, by using the conduction formula between the carbon emission intensity of each node at time t and the actual electricity carbon price at time t in the consumer demand side, as well as the relationship between the power demand of each node and the conduction formula, derivation is performed to obtain the power demand. Response model.

In practical applications, the cost of carbon emissions caused by electricity production and consumption can be calculated using the following formula:

=/> .

in, Represents the carbon emission cost of the grid at time t,/> Represents the electricity carbon price of the power grid at time t,/> Indicates the carbon emission intensity of the power grid at time t,/> Indicates the output power of the power grid.

Understandably, the carbon emission dispatch plan is to maximize the benefits of the power grid within a period of time, which can also be called an economic dispatch model. Typically, economic dispatch models can divide a time period into multiple smaller time steps. Then, the power flow (OPF) model is used to calculate the scheduling results at each time step. The OPF model is designed to minimize short-term generation costs, calculate the output of each generator, and determine electricity prices for consumers using the well-known location marginal price method. Using the obtained market clearing results, the generator's carbon emissions and the consumer's carbon emissions can be calculated as follows:

;

.

in, Represents the carbon emissions of the generator at time t,/> Indicates the carbon emission intensity of the generator, as provided by the generator manufacturer,/> represents the output power of the generator at time t,/> Indicates the carbon emissions of consumers at time t,/> Indicates the carbon emission intensity of consumers at time t,/> Indicates the consumer’s electricity consumption at time t.

Among them, the consumer’s carbon emission intensity at time t is calculated as follows:

.

in, Represents the line power outflow distribution matrix of the generator at time t,/> Represents the carbon emission flow rate vector of the generator at time t.

Among them, the carbon emission flow rate vector of the generator at time t is calculated as follows:

.

According to the above formula, the carbon emission cost transmission function of consumers’ electricity consumption can be derived, as follows:

.

in, Represents the carbon emission cost of consumers at time t,/> Represents the price of electricity and carbon at time t,/> Indicates the carbon emission intensity of consumers at time t,/> Indicates the consumer’s electricity consumption at time t.

Then, divide the carbon emission cost transfer function of the consumer's electricity consumption by the consumer's electricity consumption to obtain the consumer's equivalent conduction carbon price, as follows:

.

in, represents the consumer’s conduction carbon price at time t.

Based on the above formula, the embodiment of the present disclosure proposes the concept of carbon emission cost conductivity. Among them, the carbon emission cost conductivity of the i-th node can be expressed as follows:

.

in, Represents the carbon emission cost transmission difference of node i in the consumption demand side at time t,/> Expressed as the carbon emission intensity of node i in the consumption demand side at time t,/> represents the average carbon emission intensity of the power grid at time t, Indicates the generator set on the power generation side/> Output power at time t,/> Indicates generator set/> The carbon emission intensity of , G represents the number of generating units on the power generation side, /> represents the demand load power of node i in the consumer demand side at time t, and N represents the number of nodes in the consumer demand side.

In one implementation, a power demand response model is constructed by comprehensively considering the impact of the electricity carbon price and the carbon emission intensity of each node on the consumer demand side on the power demand of each node on the consumer demand side, as follows:

.

in, Represents the power demand increment of node i in the consumer demand side at time t,/> Represents the self-elasticity and cross-elasticity coefficients of node i on the consumption demand side at time t;/> Represents the reference load power of node i in the consumer demand side at time t;/> Indicates that node i in the consumption demand side is at time/> The comprehensive electricity carbon price,/> Represents the reference comprehensive electricity and carbon price of node i in the consumption demand side at time τ,/> Represents the carbon emission intensity of node i in the consumption demand side at time t,/> Represents the reference carbon emission intensity of node i in the consumption demand side at time t,/> Represents the price of electricity and carbon at time t,/> Represents the reference electricity carbon price at time t.

Understandably, the required power is the electrical carbon demand.

In this example, by analyzing the impact of the electricity carbon price and the carbon emission intensity of each node on the consumption demand side on the power demand of the corresponding node, a power demand response model based on the electricity carbon price can be obtained.

Furthermore, embodiments of the present disclosure can construct a corresponding carbon emission scheduling optimization model under the constraints of the power demand response model. The model can include objective functions and constraints. The objective function may be a power generation cost function on the power generation side, and the constraints at least include constraints between demand power and electricity carbon price constructed by a power demand response model.

The carbon emission dispatch optimization model aims to minimize the short-term power generation cost on the power generation side within 1 day. The power generation cost on the power generation side is the sum of the fuel cost and carbon emission cost of each generating unit on the generator side. The following will introduce the construction process of the power generation cost function, as follows:

In one embodiment, the power generation cost function is constructed based on the changing relationship between the electricity carbon price, the carbon emission intensity of each generating unit on the power generation side, and the power generation cost on the power generation side, including: based on each power generation unit on the power generation side. The changing relationship between the output power of the generating set and the fuel cost of the corresponding generating set is used to construct the fuel cost function of the generating set on the generating side; based on the electricity carbon price, the output power of the generating set on the generating side, and the carbon content of the generating set The product of the three emission intensity is equal to the equivalent relationship of the carbon emission cost of the generating unit, and the carbon emission cost function of the generating unit on the power generation side is determined; based on the bidding factor of each generating unit, as well as the fuel cost function and carbon emissions of each generating unit Cost function, construct the power generation cost function on the power generation side.

It can be understood that a third-order polynomial is used to fit the change of the fuel cost of each generating unit on the power generation side with the output power of the corresponding generating unit on the power generation side, and the corresponding curve is obtained. The function corresponding to the curve is used as Fuel cost function for generating units on the generation side.

It can be understood that the carbon emission cost function of the generating unit on the power generation side is determined through the calculation formula of carbon emission cost provided above.

It can be understood that by multiplying the bidding factor, fuel cost and carbon emission cost of each generating unit, and then summing the obtained products, the power generation cost function of the power generation side is obtained.

It can be understood that the bidding factors of each generating unit may be the same or different.

The following formulas are used to express the fuel cost function, carbon emission cost function and power generation cost function, as follows:

In one implementation, the fuel cost function is:

.

in, Indicates the generator set on the power generation side/> The fuel cost required to generate electricity at time t,/> Indicates the generator set on the power generation side/> Output power at time t,/> Indicates the generator set on the power generation side/> The first-order cost coefficient of ,/> Indicates the generator set on the power generation side/> The second-order cost coefficient of ,/> Indicates the generator set on the power generation side/> The third-order cost coefficient of .

In one implementation, the carbon emission cost function is:

.

in, Indicates generator set/> The carbon emission cost of generating electricity at time t,/> Represents the price of electricity and carbon at time t,/> Indicates generator set/> Carbon emission intensity at time t,/> Generator set/> output power at time t.

In one implementation, the power generation cost function is:

.

in, Indicates the power generation cost of the power generation side in a time period consisting of T times t, G is the number of generating units on the power generation side,/> is the bidding factor,/> Indicates the generator set on the power generation side/> The fuel cost required to generate electricity at time t, Indicates generator set/> The carbon emission cost of generating electricity at time t.

In one implementation, the constraints of the power demand response model on the power generation cost function may include the following conditions:

;

;

.

Among them, G represents the number of generating units on the power generation side, N represents the number of nodes on the consumer demand side, Indicates the generator set on the power generation side/> Output power at time t,/> Represents the demand load power of node i in the consumer demand side at time t,/> Represents the reference demand load power of node i in the consumer demand side at time t,/> Represents the power demand increment of node i in the consumer demand side at time t.

When solving for the minimum value of the power generation cost function, the above constraints are substituted into the solution process so that the predicted electricity carbon price and the predicted carbon emission intensity of the generating unit can comply with the constraints of the power demand response model.

In one implementation, for the reference comprehensive electricity carbon price of the node on the consumer demand side in the power demand response model, the following formula can be used to calculate the time for node i on the consumer demand side. Reference comprehensive electricity carbon price/> :

.

In one implementation, for the reference carbon emission intensity of nodes on the consumption demand side in the power demand response model, the following formula is used to calculate the reference carbon emission intensity of node i on the consumption demand side at time t :

.

in, Indicates that node i in the consumption demand side is at time/> The reference demand load power, expressed, /> Without involving the constraints of the power demand response model, the power generation cost function is solved to minimize the power generation cost. The obtained node i on the consumption demand side is at time/> Forecast electricity carbon price,/> It represents the predicted carbon emission intensity of node i on the consumption demand side at time t obtained by solving the power generation cost function to minimize the power generation cost without involving the constraints of the power demand response model.

In addition, when solving the minimum value of the power generation cost function, it is also necessary to satisfy the constraints related to the power grid, as follows:

;

;

;

;

;

.

in, Indicates branches in the power grid/> Power flow at time t,/> Indicates branches in the power grid/> Lower limit power capacity at time t,/> Indicates branches in the power grid/> Upper limit power capacity at time t,/> Indicates the generator set on the power generation side/> Output power at time t,/> Indicates the generator set on the power generation side/> The lower limit output power,/> Indicates the generator set on the power generation side/> The upper limit of output power,/> Indicates the generator set on the power generation side/> Output power at time t-1,/> Indicates the generator set on the power generation side/> The output power rising rate limit value,/> Indicates the generator set on the power generation side/> The output power decrease rate limit value,/> Indicates the wind energy generating unit on the power generation side/> Output power at time t,/> Indicates the wind energy generating unit on the power generation side/> Predicted output power at time t,/> Indicates the solar generator set on the power generation side/> Output power at time t,/> Indicates the solar generator set on the power generation side/> Predicted output power at time t.

For the power demand response model, the constraints can also be as follows:

.

in, Represents the power demand increment of node i in the consumer demand side at time t,/> Indicates the power demand increment limit value of node i in the consumer demand side at time t.

According to the above implementation, a power demand response model based on electricity carbon price is used to solve the minimum value of the power generation cost function on the power generation side, and a carbon emission scheduling plan is obtained that predicts the electricity carbon price changes with the predicted carbon emission intensity. The carbon emission scheduling plan is used Carbon emission dispatching on the power generation side can reduce carbon emissions.

Figure 2 is a structural diagram of a carbon emission scheduling device according to an embodiment of the present disclosure.

As shown in Figure 2, the carbon emission scheduling device may include:

The demand response model building module 210 is used to construct a power demand response model based on the changing relationship between the electricity carbon price, the carbon emission intensity of each node on the consumer demand side, and the power demand of the corresponding node;

The power generation cost function construction module 220 is used to construct a power generation cost function based on the changing relationship between the electricity carbon price, the carbon emission intensity of each generating unit on the power generation side, and the power generation cost on the power generation side;

The constraint determination module 230 is configured to determine the constraints of the power generation cost function based on the power demand response model;

The carbon emission plan determination module 240 is used to solve the power generation cost function to minimize the power generation cost based on the constraint conditions, and obtain a carbon emission scheduling plan in which the predicted carbon emission intensity changes with the predicted electricity carbon price;

The carbon emission scheduling module 250 is used to perform carbon emission scheduling on the power generation side based on the carbon emission scheduling plan.

For descriptions of specific functions and examples of each module and sub-module of the device in the embodiment of the present disclosure, please refer to the relevant description of the corresponding steps in the above method embodiment, and will not be described again here.

In the technical solution of this disclosure, the acquisition, storage and application of user personal information involved are in compliance with relevant laws and regulations and do not violate public order and good customs.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

3 illustrates a schematic block diagram of an example electronic device 600 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to refer to various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are examples only and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in FIG. 3 , the device 600 includes a computing unit 601 that can execute according to a computer program stored in a read-only memory (ROM) 602 or loaded from a storage unit 608 into a random access memory (RAM) 603 Various appropriate actions and treatments. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. Computing unit 601, ROM 602 and RAM 603 are connected to each other via bus 604. An input/output (I/O) interface 605 is also connected to bus 604 .

Multiple components in device 600 are connected to I/O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of displays, speakers, etc.; storage unit 608, such as magnetic disk, optical disk, etc. ; and communication unit 609, such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices through computer networks such as the Internet and/or various telecommunications networks.

Computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 601 executes each method and process described above, such as a carbon emission scheduling method. For example, in some embodiments, a carbon emission scheduling method may be implemented as a computer software program, which is tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 600 via ROM 602 and/or communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of a carbon emission scheduling method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform a carbon emission scheduling method in any other suitable manner (eg, by means of firmware).

Various implementations of the systems and techniques described above may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip implemented in a system (SOC), load programmable logic device (CPLD), computer hardware, firmware, software, and/or a combination thereof. These various embodiments may include implementation in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor The processor, which may be a special purpose or general purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device. An output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions specified in the flowcharts and/or block diagrams/ The operation is implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wires based electrical connection, laptop disk, hard drive, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having: a display device (eg, a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user ); and a keyboard and pointing device (e.g., a mouse or a trackball) through which a user can provide input to the computer. Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and may be provided in any form, including acoustic input, speech input, or tactile input) to receive input from the user.

The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., A user's computer having a graphical user interface or web browser through which the user can interact with implementations of the systems and technologies described herein), or including such backend components, middleware components, or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communications network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

Computer systems may include clients and servers. Clients and servers are generally remote from each other and typically interact over a communications network. The relationship of client and server is created by computer programs running on corresponding computers and having a client-server relationship with each other. The server can be a cloud server, a distributed system server, or a server combined with a blockchain.

It should be understood that various forms of the process shown above may be used, with steps reordered, added or deleted. For example, each step described in the present disclosure can be executed in parallel, sequentially, or in a different order. As long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, there is no limitation here.

The above-mentioned specific embodiments do not constitute a limitation on the scope of the present disclosure. It will be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions are possible depending on design requirements and other factors. Any modifications, equivalent substitutions, improvements, etc. made within the principles of this disclosure shall be included in the protection scope of this disclosure.

Claims (10)

1. A carbon emission scheduling method, comprising:

constructing a power demand response model based on the change relation between the carbon emission intensity of each node in the electricity carbon price and consumption demand side and the power demand of the corresponding node;

constructing a power generation cost function based on a change relation between the electric carbon price, the carbon emission intensity of each generator set in the power generation side and the power generation cost of the power generation side;

determining a constraint condition of the power generation cost function based on the power demand response model;

solving a minimum value of the power generation cost function based on the constraint condition to obtain a carbon emission scheduling scheme of the predicted carbon emission intensity of the generator set in the power generation side along with the change of the predicted electric carbon price;

and carrying out carbon emission scheduling on the power generation side based on the carbon emission scheduling scheme.

2. The method of claim 1, wherein the power demand response model is:

；

wherein,representing the power demand increase of node i at time t in said consumer demand side, +.>Representing self-elasticity and cross-elasticity coefficients of the node i at time t in the consumer demand side; />Representing a reference load power of a node i in the consumer demand side at a time t; />Representing that node i is at time +.>Is of (1)Carbon price->A reference integrated electricity carbon price representing the node i in the consumer demand side at time τ, +.>Representing the carbon emission intensity of node i at time t in the consumer demand side, +.>Representing a reference carbon emission intensity of node i at time t in the consumer demand side,electric carbon price representing time t, +.>The reference electricity carbon price at time t is indicated.

3. The method of claim 1, wherein the constructing a power generation cost function based on a changing relationship between the electricity-carbon price, the carbon emission intensity of each generator set in a power generation side, and the power generation cost of the power generation side comprises:

constructing a fuel cost function of the generator sets in the generating side based on a change relation between the output power of each generator set in the generating side and the fuel cost of the corresponding generator set;

determining a carbon emission cost function of the generator set in the generator side based on the electric carbon price, wherein the product of the output power of the generator set in the generator side and the carbon emission intensity of the generator set is equal to the equivalent relation of the carbon emission cost of the generator set;

a power generation cost function of the power generation side is constructed based on the bidding factors of the respective power generation sets, and the fuel cost function and the carbon emission cost function of the respective power generation sets.

4. A method according to claim 3, wherein the fuel cost function is:

；

wherein,representing a generator set in the power generation side +.>Fuel cost for power generation at time t, < >>Representing a generator set in the power generation side +.>Output power at time t, +.>Representing a generator set in the power generation side +.>First order cost coefficient of>Representing a generator set in the power generation side +.>Is>Representing a generator set in the power generation side +.>Third-order cost coefficients of (2).

5. A method according to claim 3, wherein the carbon emission cost function is:

；

wherein,representing a generator set->Carbon emission cost of power generation at time t +.>The price of electrical carbon at time t is indicated,representing a generator set->Carbon emission intensity at time t, +.>Generator set->Output power at time t.

6. A method according to claim 3, wherein the power generation cost function is:

；

wherein,representing the power generation side at time T groups of T timesGenerating cost in the generated time period, G is the number of generating sets in the generating side, +.>For bidding factors>Representing a generator set in the power generation side +.>Fuel cost for power generation at time t, < >>Representing a generator set->Carbon emission costs for power generation at time t.

7. The method of claim 1, wherein the constraint comprises:

；

；

；

wherein G represents the number of generator sets in the power generation side, N represents the number of nodes in the consumer demand side,representing a generator set in the power generation side +.>Output power at time t, +.>Representing the demand load power of node i at time t in said consumer demand side, +.>Representing the reference demand load power of node i at time t in said consumer demand side, +.>Representing the power demand increase at time t for node i in the consumer demand side.

8. The method according to claim 2, characterized in that the node i in the consumer demand side is calculated at time using the formulaReference integrated electric carbon price->：

；

Calculating the reference carbon emission intensity of the node i at the time t in the consumption demand side by adopting the following formula：

；

Wherein,representing that node i is at time +.>Is expressed by +.>Solving the power generation cost function for a minimum power generation cost without involving constraints of the power demand response model, the resulting node i in the consumer demand side being at time +.>Is>Representing the predicted carbon emission intensity at time t for node i in the consumer demand side obtained by solving the power generation cost function for the minimum power generation cost without involving constraints of the power demand response model.

9. A carbon emission scheduling device, characterized by comprising:

the demand response model construction module is used for constructing a power demand response model based on the change relation between the electric carbon price and the carbon emission intensity of each node in the consumption demand side and the power demand of the corresponding node;

a power generation cost function construction module for constructing a power generation cost function based on a change relation between the carbon price of electricity, the carbon emission intensity of each generator set in the power generation side and the power generation cost of the power generation side;

a constraint condition determining module for determining a constraint condition of the power generation cost function based on the power demand response model;

the carbon emission scheme determining module is used for solving the minimum value of the power generation cost function based on the constraint condition to obtain a carbon emission scheduling scheme of the predicted carbon emission intensity of the generator set on the power generation side along with the change of the predicted electric carbon price;

and the carbon emission scheduling module is used for performing carbon emission scheduling on the power generation side based on the carbon emission scheduling scheme.

10. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor;

wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.