CN117077980B - Carbon emission scheduling method and device and electronic equipment - Google Patents
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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
Technical Field
The present disclosure relates to the field of power control technology. The disclosure particularly relates to a carbon emission scheduling method, a carbon emission scheduling device and electronic equipment.
Background
The application of the industrial Internet of things (IIoTs) brings advanced measurement and communication technologies, such as network technologies and intelligent electric meters, to the power grid, so that the power grid is more intelligent than the prior art. The widespread use of smart devices significantly enhances the observability and controllability of the demand side of the smart grid and provides powerful hardware and data support for the development of Demand Response (DR). DR refers to a change in electricity usage by customers compared to their normal consumption pattern when electricity prices change over time or to induce a reduction in electricity usage when wholesale electricity markets are high in price or system reliability is compromised.
DR programs typically include Price Based Demand Response (PBDR) and Incentive Based DR (IBDR). In the PBDR program, consumers respond to real-time prices according to a demand elasticity model and modify their demands by shifting flexible loads to low-peak periods to flatten the load curve. PBDR planning is typically applied to day-to-day and real-time market clearing, economic dispatch, and grid planning. Successful PBDR can help the electricity market set effective electricity prices, improve economic efficiency, reduce environmental costs and carbon emissions.
Disclosure of Invention
The disclosure provides a carbon emission scheduling method, a carbon emission scheduling device and electronic equipment, which can solve the problems.
According to an aspect of the present disclosure, there is provided a carbon emission scheduling method including:
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.
According to another aspect of the present disclosure, there is provided a carbon emission scheduling apparatus including:
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.
According to another aspect of the present disclosure, there is provided an electronic device including:
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 any one of the carbon emission scheduling methods of the embodiments of the present disclosure.
According to the technology of the disclosure, a power demand response model is constructed based on a changing relationship between both of an electric carbon price and a carbon emission intensity of each node in a consumption demand side and a power demand of a corresponding node; meanwhile, a power generation cost function is constructed based on the change relation between the price of electric carbon and the carbon emission intensity of each generator set on the power generation side and the power generation cost on the power generation side; and then, based on the constraint condition, solving the power generation cost function to obtain a carbon emission scheme that the predicted carbon emission intensity changes along with the predicted carbon price. Thus, according to such a carbon emission scheduling scheme, carbon emission scheduling is performed on the power generation side, and the power demand on the consumption demand side can be considered, thereby reducing carbon emission.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
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The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a flow chart of a carbon emission scheduling method of an embodiment of the present disclosure;
FIG. 2 is a block diagram of a carbon emission scheduling device according to an embodiment of the present disclosure;
fig. 3 is a block diagram of an electronic device for implementing a carbon emission scheduling method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a flowchart of a carbon emission scheduling method according to an embodiment of the present disclosure.
As shown in fig. 1, the carbon emission scheduling method may include:
s110, 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;
s120, constructing a power generation cost function based on the change relation between the carbon price of electricity, the carbon emission intensity of each generator set on the power generation side and the power generation cost on the power generation side;
s130, determining constraint conditions of a power generation cost function based on the power demand response model;
s140, solving the power generation cost function based on constraint conditions to obtain a carbon emission scheduling scheme of the predicted carbon emission intensity along with the change of the predicted carbon price, wherein the power generation cost is minimum;
and S150, performing carbon emission scheduling on the power generation side based on the carbon emission scheduling scheme.
It will be appreciated that the power demand response model is fitted using the actual electrical carbon price at time t, the carbon emission intensity at time t for each node in the consumer demand side, and the power demand for each node.
It can be understood that the power demand response model is obtained by deriving the conduction formula of the carbon emission intensity of each node at time t and the actual electric carbon price at time t in the consumption demand side and the relationship between the power demand of each node and the conduction formula.
In practical applications, the carbon emission costs resulting from electricity production and consumption can be calculated using the following formula:
=/>。
wherein,carbon emission costs representing the grid time t +.>Represents the electricity carbon price of the grid at time t, < >>Representing the carbon emission intensity of the grid at time t, < >>Representing the output power of the grid.
It will be appreciated that the carbon emission scheduling scheme is a benefit that maximizes the grid over a period of time, which may also be referred to as an economic scheduling model. Typically, the economic dispatch model may divide the time period into a plurality of smaller time steps. Then, the scheduling result for each time step is calculated using a power flow (OPF) model. The OPF model aims to minimize short-term generation costs, calculate the output of each generator, and determine electricity prices for consumers using well-known location marginal price methods. Using the market clearing results obtained, the carbon emissions of the generator and the carbon emissions of the consumer can be calculated, in particular as follows:
；
。
wherein,represents the carbon emission of the generator at time t, < >>Representing the carbon emission intensity of the generator, which is provided by the generator manufacturer,/->Representing the output power of the generator at time t, +.>Representing the carbon emission of the consumer at time t, < >>Represents the intensity of carbon emission of the consumer at time t, < >>Indicating the consumer's power usage at time t.
Wherein the consumer's carbon emission intensity at time tThe calculation mode of (2) is as follows:
。
wherein,line power outflow distribution matrix representing generator at time t +.>A carbon emission flow rate vector representing the generator at time t.
Wherein the generator carbon emission flow velocity vector at time tThe calculation mode of (2) is as follows:
。
from the above equation, the carbon emission cost transfer function of consumer power consumption can be deduced as follows:
。
wherein,representing the carbon emission costs of the consumer at time t +.>Electric carbon price representing time t, +.>Represents the intensity of carbon emission of the consumer at time t, < >>Indicating the consumer's power usage at time t.
Then, dividing the carbon emission cost conduction function of the consumed power of the consumer by the electric power of the consumer to obtain the equivalent conduction carbon price of the consumer, wherein the equivalent conduction carbon price is represented by the following formula:
。
wherein,representing the conductive carbon price of the consumer at time t.
Based on the above formulas, embodiments of the present disclosure present concepts of carbon emission cost conductivity. The carbon emission cost conductivity of the ith node can be expressed as follows:
。
wherein,representing the difference in carbon emission cost conduction at time t for node i in the consumer demand side, +.>Expressed as the carbon emission intensity of node i in the consumer demand side at time t +.>Representing the average carbon emission intensity of the grid at time t,representing the generator set in the power generation side>Output power at time t, +.>Representing a generator set->G represents the number of generator sets in the power generation side, +.>Represents the demanded load power of node i in the consuming demand side at time t, and N represents the number of nodes in the consuming demand side.
In one embodiment, a power demand response model is constructed that comprehensively considers the effects of both the electric 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:
。
wherein,representing the power demand increase of node i at time t in the consumer demand side, +.>Representing the self-elasticity and cross-elasticity coefficients of node i at time t in the consumer demand side; />Representing a reference load power of a node i at time t in the consumer demand side; />Representing node i in the consumer demand side at time +.>Is of (2)Price of electric carbon->Reference integrated electricity-carbon price representing node i at time τ in consumer demand side, +.>Representing the carbon emission intensity of node i at time t in consumer demand side, +.>Reference carbon emission intensity representing node i at time t in consumer demand side, +.>Electric carbon price representing time t, +.>The reference electricity carbon price at time t is indicated.
It is understood that the power demand is an electrical carbon demand.
In this example, by analyzing the influence of both the electric carbon price and the carbon emission intensity of each node in the consumption demand side on the power demand of the corresponding node, respectively, a power demand response model based on the electric carbon price may be obtained.
Furthermore, the embodiment of the disclosure can construct a corresponding carbon emission scheduling optimization model under the constraint of the power demand response model. The model may include an objective function and constraint conditions. The objective function may be a power generation cost function of the power generation side, and the constraint condition at least includes a constraint condition between the required power and the electric carbon price constructed by the power requirement response model.
The carbon emission scheduling optimization model aims at minimizing short-term power generation costs 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 the carbon emission cost of each generator set on the power generator side. The construction process of the power generation cost function will be described as follows:
in one embodiment, the construction of the power generation cost function based on the relationship between the carbon price of electricity, the carbon emission intensity of each generator set on the power generation side, and the power generation cost on the power generation side includes: constructing a fuel cost function of the generator set on the power generation side based on the change relation between the output power of each generator set on the power generation 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 an equivalent relationship of the carbon emission cost of the generator set, the product of the output power of the generator set in the generator side and the carbon emission intensity of the generator set; and constructing a power generation cost function of the power generation side based on the bidding factors of the power generation units, the fuel cost function and the carbon emission cost function of the power generation units.
It can be understood that a third-order polynomial is adopted to fit the fuel cost of each generator set in the generating side along with the change condition of the output power of the corresponding generator set in the generating side, so as to obtain a corresponding curve, and a function corresponding to the curve is used as the fuel cost function of the generator set in the generating side.
It will be appreciated that the carbon emission cost function of the generator set in the power generation side is determined by the calculation formula of the carbon emission cost provided above.
It will be appreciated that the bidding factor, fuel cost and carbon emission cost for each genset are multiplied together and then the resulting products are summed to yield the power generation cost function on the power generation side.
It will be appreciated that the bid factors for each genset may or may not be the same.
The following formulas are used to represent the fuel cost function, the carbon emission cost function, and the power generation cost function, and are specifically as follows:
in one embodiment, the fuel cost function is:
。
wherein,representing the generator set in the power generation side>Fuel cost for power generation at time t, < >>Representing the generator set in the power generation side>Output power at time t, +.>Representing the generator set in the power generation side>First order cost coefficient of>Representing the generator set in the power generation side>Is>Representing the generator set in the power generation side>Third-order cost coefficients of (2).
In one embodiment, the carbon emission cost function is:
。
wherein,representing a generator set->Carbon emission cost of power generation at time t +.>Electric carbon price representing time t, +.>Representing a generator set->Carbon emission intensity at time t, +.>Generator set->Output power at time t.
In one embodiment, the power generation cost function is:
。
wherein,representing the power generation costs of the power generation side in a period of time consisting of T times T, G being the number of generator sets in the power generation side, +.>For bidding factors>Representing the generator set in the power generation side>The fuel cost required for power generation at time t,representing a generator set->Carbon emission costs for power generation at time t.
In one embodiment, the constraints of the power demand response model on the power generation cost function may include the following:
；
；
。
where G represents the number of gensets in the power generation side, N represents the number of nodes in the consumer demand side,representing the generator set in the power generation side>Output power at time t, +.>Representing the demand load power of node i at time t in the consumer demand side, +.>Reference demand load power representing node i in consumer demand side at time t,/>Representing the power demand increase at time t for node i in the consumer demand side.
When the minimum value of the power generation cost function is solved, the constraint conditions are substituted into the solving process to be calculated, so that the predicted electricity-carbon price obtained by solving and the predicted carbon emission intensity of the generator set can meet the constraint of the power demand response model.
In one embodiment, for a reference integrated electricity carbon price for a node in the consumer demand side in the power demand response model, the following formula may be used to calculate the time for node i in the consumer demand sideReference integrated electric carbon price->：
。
In one embodiment, for a reference carbon emission intensity of a node in the consumer demand side in the power demand response model, the reference carbon emission intensity of node i in the consumer demand side at time t is calculated using the following formula：
。
Wherein,representing node i in the consumer demand side at time +.>Is expressed by +.>Solving the power generation cost function for the 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 resulting consumer demand side, without constraints involving the power demand response model, solving the power generation cost function for the minimum power generation cost.
In addition, when solving the minimum value of the power generation cost function, constraint conditions related to the power grid also need to be satisfied, specifically as follows:
；
；
；
；
；
。
wherein,representing branches +.>Power flow at time t +.>Representing branches +.>Lower power capacity at time t, +.>Representing branches +.>Upper power capacity at time t, +.>Representing the generator set in the power generation side>Output power at time t, +.>Representing the generator set in the power generation side>Lower limit output power, +.>Representing the generator set in the power generation side>Upper limit output power, +.>Representing the generator set in the power generation side>Output at time t-1, +.>Representing the generator set in the power generation side>The limit value of the rise rate of the output power of +.>Representing the generator set in the power generation side>A limiting value of the rate of decrease of the output power, < +.>Represents a wind energy generator set in the power generation side>Output power at time t, +.>Represents a wind energy generator set in the power generation side>Predicted output power at time t, +.>Representing a solar power unit in the power generation side>Output power at time t, +.>Representing a solar power unit in the power generation side>Predicted output power at time t.
For the power demand response model, the constraints can also be as follows:
。
wherein,representing the power demand increase of node i at time t in the consumer demand side, +.>Representing the power demand delta limit value for node i at time t in the consumer demand side.
According to the embodiment, the power demand response model based on the electricity carbon price is adopted, the minimum value is solved for the electricity generation cost function of the electricity generation side, the carbon emission scheduling scheme for predicting the change of the electricity carbon price along with the predicted carbon emission intensity is obtained, and the carbon emission scheduling scheme is utilized for carrying out carbon emission scheduling on the electricity generation side, so that the carbon emission can be reduced.
Fig. 2 is a block diagram of a carbon emission scheduling device according to an embodiment of the present disclosure.
As shown in fig. 2, the carbon emission scheduling device may include:
a demand response model construction module 210, configured to construct a power demand response model based on a change relationship 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;
a power generation cost function construction module 220 for constructing a power generation cost function based on a variation relationship between the electricity-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;
a constraint condition determination module 230 for determining a constraint condition of the power generation cost function based on the power demand response model;
the carbon emission scheme determining module 240 is configured to solve the power generation cost function for a minimum power generation cost based on the constraint condition, so as to obtain a carbon emission scheduling scheme that predicts a change of carbon emission intensity along with a predicted electric carbon price;
and the carbon emission scheduling module 250 is used for performing carbon emission scheduling on the power generation side based on the carbon emission scheduling scheme.
For descriptions of specific functions and examples of each module and sub-module of the apparatus in the embodiments of the present disclosure, reference may be made to the related descriptions of corresponding steps in the foregoing method embodiments, which are not repeated herein.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 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 represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile apparatuses, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing apparatuses. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 3, the apparatus 600 includes a computing unit 601 that can perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Various components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a 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 via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes 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 tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When a computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of one 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 by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be 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 can 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. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions, improvements, etc. that are within the principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (7)
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;
performing carbon emission scheduling on the power generation side based on the carbon emission scheduling scheme;
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>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, +.>A reference electric carbon price representing time t;
wherein the constructing a power generation cost function based on a variation relationship 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 includes:
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;
constructing a power generation cost function of the power generation side based on the bidding factors of the power generation units, the fuel cost function and the carbon emission cost function of the power generation units;
wherein the constraint condition includes:
；
；
；
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.
2. The method of claim 1, 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).
3. The method of claim 1, 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.
4. The method of claim 1, wherein the power generation cost function is:
；
wherein,representing the electricity generation costs of the electricity generation side in a period of time consisting of T times T, G being the number of generator sets in the electricity generation 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 for generating electricity at time tAnd (5) discharging cost.
5. The method of claim 1, wherein 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.
6. 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;
the carbon emission scheduling module is used for performing carbon emission scheduling on the power generation side based on the carbon emission scheduling scheme;
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>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, +.>A reference electric carbon price representing time t;
wherein the constructing a power generation cost function based on a variation relationship 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 includes:
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;
constructing a power generation cost function of the power generation side based on the bidding factors of the power generation units, the fuel cost function and the carbon emission cost function of the power generation units;
wherein the constraint condition includes:
；
；
；
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.
7. 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 1-5.
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