A method, device and equipment for actively responding to carbon emission reduction by multi-energy users

Abstract

The present invention discloses a method, device and equipment for multi-energy users to actively respond to carbon emission reduction, including: constructing a dynamic carbon emission factor calculation model for power grid nodes, a dynamic carbon emission factor calculation model for gas grid nodes and a dynamic carbon emission factor calculation model for heat network nodes; dividing to obtain four types of responsive carbon emission reduction; respectively constructing objective functions; respectively setting constraints corresponding to each type of responsive carbon emission reduction; based on the dynamic carbon emission factor calculation model for power grid nodes, the dynamic carbon emission factor calculation model for gas grid nodes, the dynamic carbon emission factor calculation model for heat network nodes, and the constraints of each type of responsive carbon emission reduction, respectively solving the objective function of the corresponding type of responsive carbon emission reduction to obtain the carbon emission result of the responsive carbon emission reduction type. The present invention belongs to the field of carbon emission reduction. The present invention takes into account the behavioral motivations of users from many aspects, and can make the estimation of carbon emission reduction results more accurate.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission reduction, and in particular to a method, device and equipment for active response of multi-energy users to carbon emission reduction. Background Art

[0002] As the global climate change problem intensifies, carbon emission reduction has become an important policy goal of governments around the world. In a multi-energy system, users can adjust their energy usage behavior based on real-time dynamic carbon emission factors to achieve overall carbon emission reduction. By integrating different energy forms and using intelligent control technology, the multi-energy system can achieve real-time carbon emission monitoring and dynamic scheduling of each node.

[0003] At present, the research on the user's active response to carbon emission reduction model in multi-energy systems is relatively single and cannot fully consider the user's behavioral motivation. Therefore, it is urgent to conduct in-depth research on the user response model to achieve more accurate carbon emission assessment. Summary of the invention

[0004] The present invention solves the technical problems in the prior art of a single user active response to carbon emission reduction model and inaccurate carbon emission reduction estimation by providing a method, device and equipment for multi-energy users to actively respond to carbon emission reduction, thereby achieving the technical effect of more accurate carbon emission assessment.

[0005] In a first aspect, the present invention provides a method for multi-energy users to actively respond to carbon emission reduction, the method comprising:

[0006] Construct the dynamic carbon emission factor calculation model for power grid nodes, the dynamic carbon emission factor calculation model for gas network nodes, and the dynamic carbon emission factor calculation model for heat network nodes;

[0007] The response carbon emission reduction behaviors of several users were classified into four types, including pure price-driven, irrational price-driven, pure carbon emission-driven, and irrational carbon emission-driven.

[0008] Construct the objective functions corresponding to pure price-guided type, irrational price-guided type, pure carbon emission-guided type and irrational carbon emission-guided type respectively;

[0009] Set constraints corresponding to each type of carbon reduction response respectively;

[0010] Based on the dynamic carbon emission factor calculation model of power grid nodes, the dynamic carbon emission factor calculation model of gas grid nodes, the dynamic carbon emission factor calculation model of heat grid nodes, and the constraints of each response carbon emission reduction type, the objective function of the corresponding response carbon emission reduction type is solved respectively to obtain the carbon emission result of the response carbon emission reduction type.

[0011] Furthermore, the calculation model of the dynamic carbon emission factor of the power grid node includes:

[0012]

[0013] in, A node in the regional power grid b exist t The carbon emission factor at the time, Nodes for regional power grids b Generators injecting positive active power, Nodes for regional power grids b The set of transmission lines injecting positive active power, For generator sets h exist t The effort of the moment; For generator sets h exist t The carbon emission factor at the time, For transmission lines l exist t The carbon emission factor at the time, For transmission lines l exist t The trend of the moment, is a collection of power grid nodes.

[0014] Furthermore, the calculation model of the dynamic carbon emission factor of the gas grid node includes:

[0015]

[0016] in, Node in the regional gas network g exist t The carbon emission factor at the time, To the node g Gas source assembly for natural gas injection, To the node g A collection of pipelines for injecting natural gas, For natural gas pipelines p exist t The export carbon flow rate at time, Natural gas source w exist t The effort of the moment, For t Natural gas pipeline p The outlet airflow, For natural gas source w The carbon emission factor, is the gas network node set, It is the unit conversion constant between 1 cubic meter of natural gas and 1MWh of electricity.

[0017] Furthermore, the calculation model of dynamic carbon emission factor of heating network nodes includes:

[0018]

[0019] in, Node in the regional heating network n exist t The carbon emission factor at the time, For Node n The injection pipeline collection, For pipeline k exist t The export carbon flow rate at time, is the specific heat of water, For pipeline k exist t The mass flow rate at the moment, For pipeline k exist t The outlet temperature at time, It is a collection of heating network nodes.

[0020] Furthermore, regarding the pure price-driven model, it includes:

[0021] Pure price-guided objective function:

[0022]

[0023] in, is a purely price-guided objective function, T is the time range, is the type of energy, which includes electricity, gas and heat, For energy The unit price, is the unit cost of carbon emissions, For energy exist t The carbon emission factor at the time, For energy exist t The amount of time used;

[0024] Pure price-driven constraints:

[0025] ;

[0026] ;

[0027] ;

[0028] ;

[0029] in, The actual heat provided to the device, is the total heat requirement, For energy At the moment t The maximum output of Yes Equipment i At the moment t Output capacity, For equipment i At the moment t The maximum output capacity of For equipment i At the moment t The minimum output capacity.

[0030] Furthermore, regarding the irrational price-driven type, it includes:

[0031] Irrational price-guided objective functions include:

[0032]

[0033] in, is the objective function guided by irrational price, For expectations, To indicate user behavior t An uncertain factor at the moment;

[0034] Irrational price-guided constraints include pure price-guided constraints;

[0035] Pure price-driven constraints also include:

[0036]

[0037]

[0038] in, For budget, The maximum limit for carbon emissions.

[0039] Furthermore, regarding the pure carbon emission-oriented type, it includes:

[0040] The objective function of pure carbon emission guidance includes

[0041]

[0042] in, It is a pure carbon emission guided objective function;

[0043] Pure carbon emission-oriented constraints include:

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] in, for T The total energy consumption at a given time, for t The total energy consumption at the time, for t The energy source with the lowest carbon emission factor is chosen at all times.

[0050] Furthermore, regarding the irrational carbon emission-leading type, it includes:

[0051] Regarding the objective function of irrational carbon emission guidance, it includes:

[0052]

[0053] in, is the objective function guided by irrational carbon emissions, is the risk tolerance parameter, for t The standard deviation of time, For decision variables t Expectations of the moment;

[0054] Regarding the constraints on irrational carbon emissions, they include:

[0055]

[0056]

[0057] in, for T Total energy consumption at a given time.

[0058] In a second aspect, the present invention provides a multi-energy user active response carbon emission reduction device, the device comprising:

[0059] The first construction module is used to construct a dynamic carbon emission factor calculation model for power grid nodes, a dynamic carbon emission factor calculation model for gas grid nodes, and a dynamic carbon emission factor calculation model for heat grid nodes;

[0060] The classification module is used to classify the responsive carbon emission reduction behaviors of several users, and obtain four responsive carbon emission reduction types, including: pure price-driven type, irrational price-driven type, pure carbon emission-driven type, and irrational carbon emission-driven type;

[0061] The second construction module is used to construct the objective functions corresponding to the pure price-guided type, the irrational price-guided type, the pure carbon emission-guided type and the irrational carbon emission-guided type respectively;

[0062] A constraint module is used to set the constraint conditions corresponding to each response carbon emission reduction type;

[0063] The result calculation module is used to solve the objective function of the corresponding response carbon emission reduction type based on the dynamic carbon emission factor calculation model of the power grid node, the dynamic carbon emission factor calculation model of the gas network node, the dynamic carbon emission factor calculation model of the heat network node, and the constraint conditions of each response carbon emission reduction type, and obtain the carbon emission result of the response carbon emission reduction type.

[0064] In a third aspect, the present invention provides an electronic device, characterized in that it includes:

[0065] processor;

[0066] a memory for storing processor-executable instructions;

[0067] The processor is configured to execute to implement a method for multiple energy users to actively respond to carbon emission reduction as provided in the first aspect.

[0068] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0069] The present invention provides a method for multi-energy users to actively respond to carbon emission reduction, the method comprising: constructing a dynamic carbon emission factor calculation model for power grid nodes, a dynamic carbon emission factor calculation model for gas grid nodes, and a dynamic carbon emission factor calculation model for heat grid nodes; classifying the responsive carbon emission reduction behaviors of several users into types, and obtaining a total of four responsive carbon emission reduction types, including: pure price-guided type, irrational price-guided type, pure carbon emission-guided type, and irrational carbon emission-guided type; respectively constructing objective functions corresponding to the pure price-guided type, irrational price-guided type, pure carbon emission-guided type, and irrational carbon emission-guided type; respectively setting constraints corresponding to each responsive carbon emission reduction type; based on the dynamic carbon emission factor calculation model for power grid nodes, the dynamic carbon emission factor calculation model for gas grid nodes, the dynamic carbon emission factor calculation model for heat grid nodes, and the constraints of each responsive carbon emission reduction type, respectively solving the objective functions of the corresponding responsive carbon emission reduction types, and obtaining the carbon emission results of the responsive carbon emission reduction types.

[0070] Compared with the traditional multi-energy user active response carbon emission reduction method and system, the pure price type and pure carbon emission type user active response carbon emission reduction model comprehensively considers the discussion of user behavior uncertainty, introduces the normal distribution to consider the uncertainty of user behavior, and considers the user active response carbon emission reduction model more comprehensively and vividly, making the carbon emission reduction estimate more accurate. By constructing four different user active response carbon emission reduction models, the present invention comprehensively considers the two main needs of user energy consumption, and adds uncertainty analysis to user energy consumption behavior, making the carbon emission reduction estimate more comprehensive and reasonable.

[0071] The present invention can reflect the real-time changes in carbon emission levels of each node in the power grid, gas grid and heat grid, so as to achieve accurate tracking and quantification of real-time carbon emission factors. By introducing dynamic carbon emission factors, users can make timely adjustments to their own energy consumption behaviors according to the carbon emission levels in different time periods, thereby minimizing carbon emissions and responding to national and regional emission reduction targets. The present invention constructs active response carbon emission reduction models for four different user types, taking into account the needs, behavioral characteristics and response capabilities of different users. Through a multi-level and multi-dimensional user active response mechanism, the present invention achieves accurate carbon emission control in a multi-energy network and provides flexible carbon emission reduction decision support for various types of users.

[0072] In addition, the present invention also extends the proportional sharing principle to the gas and heat network systems to track different forms of energy flow and their carbon emissions in multi-energy systems, making the carbon emission calculation results of the gas and heat networks more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0074] Figure 1 A schematic flow chart of a method for active carbon emission reduction response by a multi-energy user provided by the present invention. DETAILED DESCRIPTION

[0075] The embodiment of the present invention solves the technical problems in the prior art that the user's active response to carbon emission reduction model is single and the carbon emission reduction estimation is inaccurate by providing a method for multi-energy users to actively respond to carbon emission reduction.

[0076] The technical solution of the present invention is to solve the above technical problems, and the overall idea is as follows:

[0077] A method for multi-energy users to actively respond to carbon emission reduction, the method comprising: constructing a dynamic carbon emission factor calculation model for power grid nodes, a dynamic carbon emission factor calculation model for gas grid nodes, and a dynamic carbon emission factor calculation model for heat grid nodes; classifying the responsive carbon emission reduction behaviors of several users into four responsive carbon emission reduction types, including: pure price-guided type, irrational price-guided type, pure carbon emission-guided type, and irrational carbon emission-guided type; respectively constructing objective functions corresponding to the pure price-guided type, irrational price-guided type, pure carbon emission-guided type, and irrational carbon emission-guided type; respectively setting constraints corresponding to each responsive carbon emission reduction type; based on the dynamic carbon emission factor calculation model for power grid nodes, the dynamic carbon emission factor calculation model for gas grid nodes, the dynamic carbon emission factor calculation model for heat grid nodes, and the constraints of each responsive carbon emission reduction type, respectively solving the objective function of the corresponding responsive carbon emission reduction type to obtain the carbon emission result of the responsive carbon emission reduction type.

[0078] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0079] First of all, the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0080] It should be noted that the same characters in the present invention have the same meaning.

[0081] The present invention provides Figure 1 A method for multiple energy users to actively respond to carbon emission reduction is shown, comprising steps S11-S15:

[0082] Regarding step S11, a dynamic carbon emission factor calculation model for power grid nodes, a dynamic carbon emission factor calculation model for gas grid nodes, and a dynamic carbon emission factor calculation model for heat grid nodes are constructed.

[0083] The dynamic carbon emission factor of a grid node refers to the amount of carbon dioxide emissions corresponding to each unit of electricity generated or consumed by a certain node (such as a power station, substation, etc.) in the power system at a certain moment.

[0084] The present invention provides a calculation model for the dynamic carbon emission factor of a power grid node, including:

[0085]

[0086] in, A node in the regional power grid b exist tThe carbon emission factor at the time, Nodes for regional power grids b Generators injecting positive active power, Nodes for regional power grids b The set of transmission lines injecting positive active power, For generator sets h exist t The effort of the moment; For generator sets h exist t The carbon emission factor at the time, For transmission lines l exist t The carbon emission factor at the time, For transmission lines l exist t The trend of the moment, is the set of power grid nodes. b It can be any node in the regional power grid.

[0087] The dynamic carbon emission factor of a gas grid node refers to the carbon dioxide emissions corresponding to the transmission or consumption of a unit volume (usually standard cubic meter Nm³) or unit energy (such as kilowatt-hour kWh) of natural gas at a certain node in the natural gas network (such as a gas transmission station, gas distribution station, etc.) at a certain moment.

[0088] The present invention provides a calculation model for the dynamic carbon emission factor of a gas grid node, including:

[0089]

[0090] in, Node in the regional gas network g exist t The carbon emission factor at the time, To the node g Gas source assembly for natural gas injection, To the node g A collection of pipelines for injecting natural gas, For natural gas pipelines p exist t The export carbon flow rate at time, Natural gas source w exist t The effort of the moment, For t Natural gas pipeline p The outlet airflow, Natural gas source w The carbon emission factor is the carbon emission generated by burning a unit of natural gas; Indicates 1km 3The unit conversion constant between natural gas and 1MWh of electricity, i.e. the energy value (calorific value) of a unit of natural gas, It is a collection of gas network nodes. g It can be any node in the regional gas network.

[0091] The dynamic carbon emission factor of a heat network node refers to the carbon dioxide emissions corresponding to a unit of heat (usually expressed in gigajoules (GJ) or kilowatt-hours (kWh)) generated or delivered by a node in the water supply network at a certain moment.

[0092] The present invention provides a calculation model for the dynamic carbon emission factor of a heat network node, including:

[0093]

[0094] in, Node in the regional heating network n exist t The carbon emission factor at the time, For Node n The injection pipeline collection, For pipeline k exist t The export carbon flow rate at time, is the specific heat of water, For pipeline k exist t The mass flow rate at the moment, For pipeline k exist t The outlet temperature at time, It is a collection of nodes in the heating network. n It can be any node in the district heating network.

[0095] Regarding step S12, the responsive carbon emission reduction behaviors of several users are classified into four types, including: pure price-guided type, irrational price-guided type, pure carbon emission-guided type and irrational carbon emission-guided type.

[0096] In order to simulate the real user environment, the present invention takes into account the market incentive environment and the response behavior model of different users in the multi-energy system to the carbon emission factor. The present invention divides the user's response to carbon emission reduction behavior into four types: pure price-guided, irrational price-guided, pure carbon emission-guided and irrational carbon emission-guided.

[0097] Pure price-guided type: Pure price-guided users actively respond to carbon emission reduction behaviors. Their characteristic is that they only consider maximizing individual benefits without considering their own carbon emission levels.

[0098] Irrational price-driven: Irrational price-driven users expect to reduce energy costs, but their response behavior decisions are random.

[0099] Pure carbon emission-oriented type: The characteristic of pure carbon emission-oriented user response behavior is that it only considers minimizing carbon emission levels without considering its own economic benefits;

[0100] Irrational carbon emission guided type: Irrational carbon emission guided type users’ response behavior expects to reduce energy carbon emissions, but their response behavior decisions are random.

[0101] Regarding step S13, objective functions corresponding to the pure price-guided type, the irrational price-guided type, the pure carbon emission-guided type and the irrational carbon emission-guided type are constructed respectively.

[0102] Pure price-guided users actively respond to carbon emission reduction behaviors. Their characteristic is that they only consider the maximization of individual benefits without considering their own carbon emission levels. Therefore, the objective function of pure price-guided users can represent the user's energy minimization cost at a certain moment, including:

[0103]

[0104] in, is a purely price-guided objective function, T is the time range, is the type of energy, which includes electricity, gas and heat (provided by the above-mentioned power grid, gas grid and heat grid), For energy The unit price, is the unit cost of carbon emissions, For energy exist t The carbon emission factor at the time, For energy exist t The usage of the moment.

[0105] Irrational price-guided user response behavior expects to reduce energy consumption costs, but their response behavior decisions are random. In order to improve the accuracy of the model, the normal distribution is introduced to simulate the random decisions of such users.

[0106] Energy consumption by users with irrational price guidance It obeys the normal distribution and can be expressed as:

[0107]

[0108] in, is the decision variable in t The expected value at the moment (i.e., the average choice made by users when there is no uncertainty); yes t The standard deviation of the time represents the volatility of the decision; It means that the user behavior is t An uncertain factor at the moment, representing the fluctuations in users’ decisions in different situations.

[0109] Irrational price-driven users make volatile choices based on certain factors (such as price fluctuations, demand changes, policies, etc.) when making decisions, and their decisions will revolve around a mean. Random fluctuations. After the introduction of normal distribution, the objective function of irrational price guidance includes:

[0110]

[0111] in, is the objective function guided by irrational price, For expectations, To indicate user behavior t An uncertain factor at the moment.

[0112] The characteristic of pure carbon emission-oriented user response behavior is that it only considers minimizing carbon emission levels without considering its own economic benefits. The objective function of pure carbon emission-oriented user response behavior is expressed in T Minimize carbon emissions from energy use at all times, including:

[0113]

[0114] in, It is a pure carbon emission-guided objective function.

[0115] Irrational carbon emission-oriented users expect to reduce carbon emissions from energy consumption, but their response behavior decisions are random. In order to improve the accuracy of the model, the normal distribution is also introduced to simulate the random decisions of such users:

[0116]

[0117] The objective function of non-rational carbon emission guidance includes:

[0118]

[0119] in, is the objective function guided by irrational carbon emissions, is the risk tolerance parameter, for t The standard deviation of time, For decision variables t Expectations of the moment.

[0120] The behaviors of different users in actively responding to carbon emission reduction were explored through pure price guidance, irrational price guidance, pure carbon emission guidance and irrational carbon emission guidance. Users can adjust their energy usage behaviors according to real-time dynamic carbon emission factors, optimize the load of the power grid, gas grid and heat grid, thereby achieving overall carbon emission reduction.

[0121] Regarding step S14, the constraint conditions corresponding to each response carbon emission reduction type are set respectively.

[0122] Purely price-driven constraints

[0123] The purely price-driven objective function needs to satisfy the heat load balance and energy supply constraints, as follows:

[0124] Pure price-driven constraints:

[0125] ;

[0126] ;

[0127] ;

[0128] ;

[0129] in, The actual heat provided to the device, is the total heat requirement, For energy At the moment t The maximum output of Yes Equipment i At the moment t Output capacity, For equipment i At the moment t The maximum output capacity, For equipment i At the moment t The minimum output capacity.

[0130] By minimizing the total cost and satisfying the pure price-guided constraints, the pure price-guided users can use electricity, gas and heat at the lowest cost at a certain moment in the multi-energy system.

[0131]

Irrational price-driven constraints

[0132] In addition to the above-mentioned pure price-guided constraints, the non-rational price-guided objective function constraints may also include:

[0133] Users may have budget constraints, and the cost of energy consumption should be within their budget. Assume that the budget is , then the budget constraint can be expressed as:

[0134]

[0135] Users may also need to meet the upper limit of carbon emissions to ensure that their consumption behavior complies with environmental protection policies. Assume that the maximum limit of carbon emissions is :

[0136]

[0137] in, For budget, The maximum limit for carbon emissions.

[0138] [Pure carbon emission-oriented constraints]

[0139] In the pure carbon emission-oriented constraint, it is assumed that the energy consumption behavior of the pure carbon emission-oriented user has been determined (i.e., the total energy consumption at a certain time The constraints are as follows:

[0140]

[0141] The above formula represents that the total consumption of each energy source of a pure carbon emission-oriented user meets the predetermined total consumption .

[0142]

[0143] The above formula indicates that in each period t , pure carbon emission oriented users only choose one energy source, and the total energy consumed at that moment must be equal to a fixed (Total energy consumption at each moment), therefore, pure carbon emission-oriented users must choose appropriate energy at each moment based on the carbon emission factors of different energy sources.

[0144]

[0145] The above formula indicates that the goal of a pure carbon emission-oriented user is t Choose the energy source with the lowest carbon emission factor.

[0146]

[0147] The above formula represents that for each moment t , an energy source must be selected so that The corresponding energy has the lowest carbon emission factor.

[0148]

[0149] The above formula represents a non-negativity constraint.

[0150] Under the constraints of pure carbon emission-oriented for T The total energy consumption at a given time, for t The total energy consumption at the time, for t The energy source with the lowest carbon emission factor is chosen at all times.

[0151]

Irrational carbon emission-guided constraints

[0152] Regarding the constraints on irrational carbon emissions, they include:

[0153]

[0154]

[0155] in, for T Total energy consumption at a given time.

[0156] In addition, irrational carbon emission-inducing constraints can also include:

[0157]

[0158]

[0159]

[0160] Regarding step S15, based on the dynamic carbon emission factor calculation model of power grid nodes, the dynamic carbon emission factor calculation model of gas network nodes, the dynamic carbon emission factor calculation model of heat network nodes, and the constraints of each response carbon emission reduction type, the objective function of the corresponding response carbon emission reduction type is solved respectively to obtain the carbon emission result of the response carbon emission reduction type.

[0161] For example, when it is necessary to solve the objective function of the irrational carbon emission guided type, the objective function of the irrational carbon emission guided type can be solved according to the dynamic carbon emission factor calculation model of the power grid node, the dynamic carbon emission factor calculation model of the gas network node, the dynamic carbon emission factor calculation model of the heat network node, and the constraint conditions of the irrational carbon emission guided type.

[0162] In summary, the present invention provides a method for multi-energy users to actively respond to carbon emission reduction, the method comprising: constructing a dynamic carbon emission factor calculation model for power grid nodes, a dynamic carbon emission factor calculation model for gas grid nodes, and a dynamic carbon emission factor calculation model for heat grid nodes; classifying the responsive carbon emission reduction behaviors of several users into types, and obtaining a total of four responsive carbon emission reduction types, including: pure price-guided type, irrational price-guided type, pure carbon emission-guided type, and irrational carbon emission-guided type; respectively constructing objective functions corresponding to the pure price-guided type, irrational price-guided type, pure carbon emission-guided type, and irrational carbon emission-guided type; respectively setting constraints corresponding to each responsive carbon emission reduction type; based on the dynamic carbon emission factor calculation model for power grid nodes, the dynamic carbon emission factor calculation model for gas grid nodes, the dynamic carbon emission factor calculation model for heat grid nodes, and the constraints of each responsive carbon emission reduction type, respectively solving the objective functions of the corresponding responsive carbon emission reduction types to obtain the carbon emission results of the responsive carbon emission reduction types.

[0163] Compared with the traditional multi-energy user active response carbon emission reduction method and system, the pure price type and pure carbon emission type user active response carbon emission reduction model comprehensively considers the discussion of user behavior uncertainty, introduces the normal distribution to consider the uncertainty of user behavior, and considers the user active response carbon emission reduction model more comprehensively and vividly, making the carbon emission reduction estimate more accurate. By constructing four different user active response carbon emission reduction models, the present invention comprehensively considers the two main needs of user energy consumption, and adds uncertainty analysis to user energy consumption behavior, making the carbon emission reduction estimate more comprehensive and reasonable.

[0164] The present invention can reflect the real-time changes in carbon emission levels of each node in the power grid, gas grid and heat grid, so as to achieve accurate tracking and quantification of real-time carbon emission factors. By introducing dynamic carbon emission factors, users can make timely adjustments to their own energy consumption behaviors according to the carbon emission levels in different time periods, thereby minimizing carbon emissions and responding to national and regional emission reduction targets. The present invention constructs active response carbon emission reduction models for four different user types, taking into account the needs, behavioral characteristics and response capabilities of different users. Through a multi-level and multi-dimensional user active response mechanism, the present invention achieves accurate carbon emission control in a multi-energy network and provides flexible carbon emission reduction decision support for various types of users.

[0165] In addition, the present invention also extends the proportional sharing principle to the gas and heat network systems to track different forms of energy flow and their carbon emissions in multi-energy systems, making the carbon emission calculation results of the gas and heat networks more accurate.

[0166] Based on the same inventive concept, the present invention also provides a multi-energy user active response carbon emission reduction device, the device comprising:

[0167] The first construction module is used to construct a dynamic carbon emission factor calculation model for power grid nodes, a dynamic carbon emission factor calculation model for gas grid nodes, and a dynamic carbon emission factor calculation model for heat grid nodes;

[0168] The classification module is used to classify the responsive carbon emission reduction behaviors of several users, and obtain four responsive carbon emission reduction types, including: pure price-driven type, irrational price-driven type, pure carbon emission-driven type, and irrational carbon emission-driven type;

[0169] The second construction module is used to construct the objective functions corresponding to the pure price-guided type, the irrational price-guided type, the pure carbon emission-guided type and the irrational carbon emission-guided type respectively;

[0170] A constraint module is used to set the constraint conditions corresponding to each response carbon emission reduction type;

[0171] The result calculation module is used to solve the objective function of the corresponding response carbon emission reduction type based on the dynamic carbon emission factor calculation model of the power grid node, the dynamic carbon emission factor calculation model of the gas network node, the dynamic carbon emission factor calculation model of the heat network node, and the constraint conditions of each response carbon emission reduction type, and obtain the carbon emission result of the response carbon emission reduction type.

[0172] Based on the same inventive concept, the present invention also provides an electronic device as shown, including:

[0173] processor;

[0174] a memory for storing processor-executable instructions;

[0175] The processor is configured to execute to implement a method for multi-energy users to actively respond to carbon emission reduction as provided above.

[0176] Since the electronic device introduced in this embodiment is an electronic device used to implement the information processing method in the embodiment of the present invention, based on the information processing method introduced in the embodiment of the present invention, a person skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present invention is not described in detail here. As long as the electronic device used by a person skilled in the art to implement the information processing method in the embodiment of the present invention, it belongs to the scope of protection of the present invention.

[0177] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0178] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0179] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0181] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0182] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for multi-energy users to actively respond to carbon emission reduction, characterized in that: The method comprises: Construct the dynamic carbon emission factor calculation model for power grid nodes, the dynamic carbon emission factor calculation model for gas network nodes, and the dynamic carbon emission factor calculation model for heat network nodes; The response carbon emission reduction behaviors of several users were classified into four types, including pure price-driven, irrational price-driven, pure carbon emission-driven, and irrational carbon emission-driven. Constructing objective functions corresponding to the pure price-guided type, the irrational price-guided type, the pure carbon emission-guided type, and the irrational carbon emission-guided type respectively; Set constraints corresponding to each type of carbon reduction response respectively; Based on the dynamic carbon emission factor calculation model of the power grid node, the dynamic carbon emission factor calculation model of the gas network node, the dynamic carbon emission factor calculation model of the heat network node, and the constraint conditions of each response carbon emission reduction type, respectively solve the objective function of the corresponding response carbon emission reduction type to obtain the carbon emission result of the response carbon emission reduction type; The calculation model of dynamic carbon emission factors of power grid nodes includes: in, A node in the regional power grid b exist t The carbon emission factor at the time, Nodes for regional power grids b Generators injecting positive active power, Nodes for regional power grids b The set of transmission lines injecting positive active power, For generator sets h exist t The effort of the moment; For generator sets h exist t The carbon emission factor at the time, For transmission lines l exist t The carbon emission factor at the time, For transmission lines l exist t The trend of the moment, is a set of power grid nodes; The calculation model of dynamic carbon emission factors of gas grid nodes includes: in, Node in the regional gas network g exist t The carbon emission factor at the time, To the node g Gas source assembly for natural gas injection, To the node g A collection of pipelines for injecting natural gas, For natural gas pipelines p exist t The export carbon flow rate at time, Natural gas source w exist t The effort of the moment, For t Natural gas pipeline p The outlet airflow, Natural gas source w The carbon emission factor, is the gas network node set, It is the unit conversion constant between 1 cubic meter of natural gas and 1MWh of electricity; The calculation model of dynamic carbon emission factors of heating network nodes includes: in, Node in the regional heating network n exist t The carbon emission factor at the time, For Node n The injection pipeline collection, For pipeline k exist t The export carbon flow rate at time, is the specific heat of water, For pipeline k exist t The mass flow rate at the moment, For pipeline k exist t The outlet temperature at time, is the set of nodes in the heating network; Regarding the pure price-driven model, it includes: Pure price-guided objective function: in, is a purely price-guided objective function, T is the time range, is the type of energy, which includes electricity, gas and heat, For energy The unit price, is the unit cost of carbon emissions, For energy exist t The carbon emission factor at the time, For energy exist t The amount of time used; Pure price-driven constraints: ； ； ； ； in, The actual heat provided to the device, is the total heat requirement, For energy At the moment t The maximum output of Yes Equipment i At the moment t The output capacity, For equipment i At the moment t The maximum output capacity, For equipment i At the moment t Minimum output capacity; Regarding irrational price guidance, including: Irrational price-guided objective functions include: in, is the objective function guided by irrational price, For expectations, To indicate user behavior t An uncertain factor at the moment; Irrational price-guided constraints include pure price-guided constraints; Pure price-driven constraints also include: in, For budget, The maximum limit for carbon emissions; Regarding the pure carbon emission-oriented type, it includes: The objective function of pure carbon emission guidance includes in, It is a pure carbon emission guided objective function; Pure carbon emission-oriented constraints include: in, for T The total energy consumption at a given time, for t The total energy consumption at the time, for t The energy source with the lowest carbon emission factor is selected at all times; Regarding irrational carbon emission guidance, including: Regarding the objective function of irrational carbon emission guidance, it includes: in, is the objective function guided by irrational carbon emissions, is the risk tolerance parameter, for t The standard deviation of time, For decision variables t Expectations of the moment; Regarding the constraints on irrational carbon emissions, they include: in, for T Total energy consumption at a given time.

2. A multi-energy user active response carbon emission reduction device, characterized in that: The method for actively responding to carbon emission reduction by multiple energy users as described in claim 1 comprises: The first construction module is used to construct a dynamic carbon emission factor calculation model for power grid nodes, a dynamic carbon emission factor calculation model for gas grid nodes, and a dynamic carbon emission factor calculation model for heat grid nodes; The classification module is used to classify the responsive carbon emission reduction behaviors of several users, and obtain four responsive carbon emission reduction types, including: pure price-driven type, irrational price-driven type, pure carbon emission-driven type, and irrational carbon emission-driven type; The second construction module is used to respectively construct the objective functions corresponding to the pure price-guided type, the irrational price-guided type, the pure carbon emission-guided type and the irrational carbon emission-guided type; A constraint module is used to set the constraint conditions corresponding to each response carbon emission reduction type; The result calculation module is used to solve the objective function of the corresponding response carbon emission reduction type based on the dynamic carbon emission factor calculation model of the power grid node, the dynamic carbon emission factor calculation model of the gas network node, the dynamic carbon emission factor calculation model of the heat network node, and the constraint conditions of each response carbon emission reduction type, so as to obtain the carbon emission result of the response carbon emission reduction type.

3. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; Wherein, the processor is configured to execute to implement a multi-energy user active response to carbon emission reduction method as described in claim 1.

Images