Power grid dispatching method and device based on carbon emission, terminal equipment and storage medium

Abstract

The invention discloses a power grid dispatching method and device based on carbon emission, terminal equipment and a storage medium, and belongs to the technical field of electric power systems.The method comprises the steps that the power grid average carbon intensity of an energy storage unit in a power grid system at the charging moment is determined; determining the equivalent carbon emission intensity of the energy storage unit according to the average carbon intensity of the power grid and the charging power time sequence data of the energy storage unit; building a multi-main-body game model by taking minimization of total carbon emission of the system and maximization of main-body income as objective functions; according to the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each main body, solving a multi-main body game model to obtain a carbon responsibility allocation coefficient of each main body; and generating a cooperative scheduling strategy according to the carbon responsibility allocation coefficient of each main body, and regulating and controlling the carbon emission intensity of each main body according to a scheduling instruction corresponding to the cooperative scheduling strategy. According to the method, the difference of different main bodies in the aspect of carbon emission characteristics is comprehensively considered, and the power grid dispatching effect can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a carbon emission-based power grid dispatching method, apparatus, terminal equipment, and storage medium. Background Art

[0002] As the world actively responds to climate change and vigorously promotes carbon emission reduction, carbon emission management in the power system has become a key issue. As the concept of sustainable development deepens, it is increasingly important to accurately clarify the carbon emission quotas of various entities in the power system to facilitate grid dispatch.

[0003] At present, the existing carbon emission-based grid dispatching method usually simply allocates the system's overall carbon emissions to each entity according to the proportion of electricity consumption to achieve grid dispatching, ignoring the differences in carbon emission characteristics among different entities. The unreasonable allocation of carbon responsibility among the entities leads to poor grid dispatching results. Summary of the Invention

[0004] The present invention provides a carbon emission-based power grid dispatching method, apparatus, terminal device and storage medium, which can solve the technical problem in the prior art that the differences in carbon emission characteristics of different entities are ignored, the carbon responsibility of the entities is unreasonable, and the resulting poor power grid dispatching effect.

[0005] The present invention provides a carbon emission-based power grid dispatching method, comprising:

[0006] Determine the average grid carbon intensity of energy storage units in the grid system at the time of charging;

[0007] Determining the equivalent carbon emission intensity of the energy storage unit based on the average carbon intensity of the power grid and the charging power time series data of the energy storage unit;

[0008] A multi-agent game model is constructed with the objective function of minimizing the total carbon emissions of the system and maximizing the benefits of the subject. The constraints of the multi-agent game model include carbon responsibility sharing coefficient constraints and output constraints.

[0009] Solving the multi-agent game model based on the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each entity to obtain the carbon responsibility sharing coefficient of each entity;

[0010] A collaborative scheduling strategy is generated according to the carbon responsibility sharing coefficient of each entity, and the carbon emission intensity of each entity is regulated by the scheduling instructions corresponding to the collaborative scheduling strategy.

[0011] Furthermore, determining the average carbon intensity of the power grid at the time of charging of the energy storage unit in the power grid system includes:

[0012] Based on the timestamp association method, the average carbon intensity of the grid at the time of charging of the energy storage unit in the grid system is determined.

[0013] Furthermore, determining the equivalent carbon emission intensity of the energy storage unit based on the average carbon intensity of the power grid and the charging power time series data of the energy storage unit includes:

[0014] The equivalent carbon emission intensity of the energy storage unit is determined according to the following equivalent carbon emission intensity formula:

[0015] CE ESS =Σ(P charge(t) ×C grid(t) ) / P discharge_total

[0016] Among them, CE ESS is the equivalent carbon emission intensity of the energy storage unit, P charge(t) is the charging power time series data of the energy storage unit, P discharge_total is the total charge capacity, C grid(t) is the average carbon intensity of the grid at the time t when the energy storage unit is charged.

[0017] Furthermore, the upper optimization goal of the multi-agent game model is to minimize the total carbon emissions of the system, and the lower optimization goal is to maximize the benefits of each agent;

[0018] Among them, the upper-level optimization target is constructed based on the carbon responsibility sharing coefficient of the subject, the carbon emission intensity of the subject and the power of the subject; the lower-level optimization target is constructed based on the unit output income of the subject and the cost function of the subject.

[0019] Furthermore, solving the multi-agent game model based on the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each entity to obtain the carbon responsibility sharing coefficient of each entity includes:

[0020] According to the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each subject, the alternating direction multiplier method is used to solve the multi-agent game model to obtain the carbon responsibility sharing coefficient of each subject.

[0021] Furthermore, the generation of a collaborative scheduling strategy based on the carbon responsibility sharing coefficient of each entity includes:

[0022] An asymmetric Nash bargaining model is constructed according to the carbon responsibility sharing coefficient, the asymmetric Nash bargaining model is solved to obtain a Nash bargaining solution, the carbon emission quota of each entity is generated according to the Nash bargaining solution, and a scheduling strategy is generated according to the carbon emission quota.

[0023] Furthermore, the determination of the carbon emission intensity of each entity includes:

[0024] The carbon emission intensity of each entity is determined based on the unit number, unit output, carbon emission coefficient, power transmission distribution factor and entity predicted load.

[0025] The present invention also provides a carbon emission-based power grid dispatching device, comprising:

[0026] An average carbon intensity determination module, used to determine the average carbon intensity of the grid at the time of charging of the energy storage unit in the grid system;

[0027] An equivalent carbon emission intensity determination module is used to determine the equivalent carbon emission intensity of the energy storage unit based on the average carbon intensity of the power grid and the charging power time series data of the energy storage unit;

[0028] A game model construction module is used to construct a multi-agent game model with the objective function of minimizing the total carbon emissions of the system and maximizing the benefits of the subject. The constraints of the multi-agent game model include carbon responsibility sharing coefficient constraints and output constraints.

[0029] a game model solving module, configured to solve the multi-agent game model based on the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each entity, and obtain a carbon responsibility sharing coefficient of each entity;

[0030] The power grid dispatching module is used to generate a coordinated dispatching strategy based on the carbon responsibility sharing coefficient of each entity, and to regulate the carbon emission intensity of each entity with the dispatching instructions corresponding to the coordinated dispatching strategy.

[0031] Another embodiment of the present invention also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the carbon emission-based power grid scheduling method of the present invention.

[0032] Another embodiment of the present invention further provides a computer-readable storage medium item, comprising: a stored computer program, which controls the device where the computer-readable storage medium is located to execute the steps of the carbon emission-based power grid scheduling method of the present invention when the computer program is running.

[0033] The following beneficial effects are achieved by implementing the present invention:

[0034] The present invention takes minimizing the total carbon emissions of the system and maximizing the benefits of the subjects as the objective functions, constructs a multi-agent game model, and defines the constraints of the multi-agent game model including the carbon responsibility sharing coefficient constraint and the output constraint. By solving the multi-agent game model, the carbon responsibility sharing coefficients corresponding to different subjects can be obtained, and the differences in carbon emission characteristics of different subjects are comprehensively considered for grid scheduling, avoiding the problem of poor grid scheduling effect caused by allocating the overall carbon emissions of the system to each subject according to the proportion of electricity consumption, thereby effectively improving the effect of grid scheduling.

[0035] Furthermore, the present invention incorporates energy storage units into the calculation of carbon emission entities, comprehensively considering the carbon emission sources in the power system, thereby effectively improving the accuracy of the calculation of the carbon responsibility sharing coefficient, which is beneficial to improving the reliability of the carbon emission scheduling of the power grid and effectively reducing the carbon emission intensity of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0037] Figure 1 This is a flow chart of a carbon emission-based power grid scheduling method provided by an embodiment of the present invention;

[0038] Figure 2 It is a structural diagram of a carbon emission-based power grid dispatching device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0041] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0042] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0043] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0044] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0045] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0046] See also Figure 1 To address the technical problem that the existing technology ignores the differences in carbon emission characteristics among different entities, resulting in unreasonable carbon responsibility allocation among the entities and poor grid scheduling, an embodiment of the present invention provides a grid scheduling method based on carbon emissions, including:

[0047] S1. Determine the average carbon intensity of the energy storage unit in the power grid system at the time of charging;

[0048] In an embodiment of the present invention, the average carbon intensity of the power grid of the power system, that is, the carbon emissions per unit of electrical energy, can be monitored in real time.

[0049] The embodiment of the present invention accurately records the average carbon intensity of the power grid at the time of charging of the energy storage unit, providing basic data for subsequent power grid scheduling.

[0050] S2. Determine the equivalent carbon emission intensity of the energy storage unit based on the average carbon intensity of the power grid and the charging power time series data of the energy storage unit;

[0051] The embodiment of the present invention can quantify the carbon emissions of the energy storage unit during the charging and discharging process by determining the equivalent carbon emission intensity of the energy storage unit, thereby providing a basis for subsequent carbon emission management.

[0052] S3. Taking the minimization of the system's total carbon emissions and the maximization of the subject's benefits as the objective function, a multi-agent game model is constructed. The constraints of the multi-agent game model include the carbon responsibility sharing coefficient constraint and the output constraint;

[0053] In the embodiment of the present invention, the carbon responsibility allocation coefficient is constrained as follows:

[0054] ∑γ i =1 and γ i ∝CE i ;

[0055] Among them, γ i is the carbon responsibility allocation coefficient of entity i, and the carbon intensity of entity i is proportional to the carbon responsibility allocation coefficient;

[0056] The expression of output constraint is as follows:

[0057] P min，i ≤P i ≤P max，i ;

[0058] Among them, P min，i is the minimum output, P max，i Maximum output.

[0059] In an embodiment of the present invention, by constructing a multi-agent game model, the output strategy of each agent can be effectively optimized to achieve a balance between minimizing the total system emissions and maximizing the agent's benefits. Moreover, through the constraint of the carbon responsibility sharing coefficient, the differences between different agents can be taken into account, the carbon emission responsibilities of each agent can be allocated, and the effect of power grid dispatching can be improved.

[0060] S4. Solve the multi-agent game model based on the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each entity to obtain the carbon responsibility allocation coefficient of each entity;

[0061] S5. Generate a collaborative scheduling strategy based on the carbon responsibility sharing coefficient of each entity, and use the scheduling instructions corresponding to the collaborative scheduling strategy to regulate the carbon emission intensity of each entity.

[0062] The embodiment of the present invention takes minimizing the total carbon emissions of the system and maximizing the benefits of the subjects as the objective functions, constructs a multi-subject game model, and defines the constraints of the multi-subject game model including the carbon responsibility sharing coefficient constraint and the output constraint. By solving the multi-subject game model, the carbon responsibility sharing coefficients corresponding to different subjects can be obtained, and the differences in carbon emission characteristics of different subjects are comprehensively considered for grid scheduling, avoiding the problem of poor grid scheduling effect caused by allocating the overall carbon emissions of the system to each subject according to the proportion of electricity consumption, thereby effectively improving the effect of grid scheduling.

[0063] In one embodiment, step S1, determining the average carbon intensity of the power grid at the time of charging of the energy storage unit in the power grid system, includes:

[0064] Based on the timestamp association method, the average carbon intensity of the grid at the time of charging of the energy storage unit in the grid system is determined.

[0065] In an embodiment of the present invention, the timestamps of all relevant devices in the power grid system are synchronized. On this basis, the charging and discharging status of the energy storage unit is recorded in real time, including the timestamps of the start and end of charging, the charging power, etc. The average carbon intensity of the power grid at the time of charging of the energy storage unit is further calculated based on the real-time recorded data.

[0066] By recording the average carbon intensity of the power grid at the time of charging, the embodiment of the present invention can accurately calculate the carbon emission intensity of the energy storage unit during the charging process, which helps to track the carbon footprint of the energy storage unit throughout its life cycle and provide data support for carbon emission management.

[0067] In one embodiment, step S2, determining the equivalent carbon emission intensity of the energy storage unit based on the average carbon intensity of the power grid and the charging power time series data of the energy storage unit, includes:

[0068] The equivalent carbon emission intensity of the energy storage unit is determined according to the following equivalent carbon emission intensity formula:

[0069] CE ESS =Σ(P charge(t) ×C grid(t) ) / P discharge_total

[0070] Among them, CE ESS is the equivalent carbon emission intensity of the energy storage unit, P charge(t) is the charging power time series data of the energy storage unit, P discharge_total is the total charge capacity, C grid(t) is the average carbon intensity of the grid at the time t when the energy storage unit is charged.

[0071] A specific embodiment of the present invention is as follows:

[0072] Charging period 1: 50MWh (carbon intensity 0.8), charging period 2: 30MWh (carbon intensity 0.6);

[0073] Equivalent carbon emission intensity = (50×0.8+30×0.6) / (50+30) = 0.725tCO2 / MWh.

[0074] In an embodiment of the present invention, the equivalent carbon emission intensity of the energy storage unit is determined based on the average carbon intensity of the power grid and the charging power time series data of the energy storage unit. The equivalent carbon emission intensity can be determined by comprehensively considering the carbon emission intensities of different time periods, thereby accurately reflecting the true carbon cost of energy storage discharge, which is conducive to improving the effect of power grid scheduling.

[0075] Furthermore, by calculating the equivalent carbon emission intensity of the energy storage charging and discharging process, the embodiments of the present invention can accurately trace the carbon footprint and effectively reduce the error in energy storage carbon accounting.

[0076] In one embodiment, the upper-level optimization goal of the multi-agent game model in step S3 is to minimize the total carbon emissions of the system, and the lower-level optimization goal is to maximize the benefits of each agent;

[0077] Among them, the upper-level optimization target is constructed based on the subject's carbon responsibility sharing coefficient, the subject's carbon emission intensity and the subject's power; the lower-level optimization target is constructed based on the subject's unit output income and the subject's cost function.

[0078] In this embodiment of the present invention, the upper-level optimization objective is expressed as follows:

[0079]

[0080] Where N is the total number of subjects, γ i is the carbon responsibility allocation coefficient of entity i, CE i is the carbon emission intensity of entity i, P i is the power of agent i.

[0081] The expression of the lower-level optimization objective is as follows:

[0082]

[0083] Among them, μ i is the unit output income of entity i (yuan / MWh), such as electricity sales income and subsidy income; c i is the unit output cost coefficient of entity i (yuan / MWh 2 ), reflecting the curvature of cost as output changes.

[0084] c i P i 2 is the cost function of subject i, which can be expanded to: Costi =a i P i 2 +b i P i +d i , a i 、b i and d i is the cost coefficient. The cost of entity i typically includes power generation costs (fuel costs for thermal power units and gas price costs for gas-fired units), operation and maintenance costs (energy storage charging and discharging losses, new energy station maintenance costs), and compensation costs (demand-side load interruption compensation).

[0085] The embodiment of the present invention takes minimizing the total carbon emissions of the system and maximizing the benefits of the subjects as the objective functions, constructs a multi-subject game model, and defines the constraints of the multi-subject game model including the carbon responsibility sharing coefficient constraint and the output constraint. By solving the multi-subject game model, the carbon responsibility sharing coefficients corresponding to different subjects can be obtained, and the differences in carbon emission characteristics of different subjects are comprehensively considered for grid scheduling, avoiding the problem of poor grid scheduling effect caused by allocating the overall carbon emissions of the system to each subject according to the proportion of electricity consumption, thereby effectively improving the effect of grid scheduling.

[0086] In one embodiment, step S4, solving a multi-agent game model based on the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each entity to obtain the carbon responsibility sharing coefficient of each entity, includes:

[0087] According to the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each entity, the alternating direction multiplier method is used to solve the multi-agent game model to obtain the carbon responsibility sharing coefficient of each entity.

[0088] In this embodiment of the present invention, step S4 may further include the following sub-steps:

[0089] S41. Introduce auxiliary variable z based on carbon emission responsibility coefficient and main power i =γ i *P i , and then construct the augmented Lagrangian function:

[0090]

[0091] in, is the Lagrange multiplier, and ρ is the penalty coefficient.

[0092] Based on the global variables at the current iteration number Each agent solves its own optimal output in parallel:

[0093]

[0094] Among them, P i (k+1) The optimal output for each entity.

[0095] S42. Update carbon responsibility allocation coefficient γ i , to minimize the total carbon emissions of the system;

[0096] Get the optimal output P of each subject i (k+1) After that, update the global variables

[0097]

[0098] S43, Lagrange multiplier update:

[0099]

[0100] Here, k represents the number of iterations, and the parameter superscript k+1 represents the parameter obtained after k+1 iterations.

[0101] S44, iterative update;

[0102] If the maximum number of iterations is met, or If ε=1e-4, the iteration is terminated; otherwise, the process returns to step S41.

[0103] The embodiment of the present invention incorporates energy storage units into the calculation of carbon emission entities and comprehensively considers the carbon emission sources in the power system, thereby effectively improving the accuracy of the calculation of the carbon responsibility sharing coefficient, which is beneficial to improving the reliability of the carbon emission scheduling of the power grid and effectively reducing the carbon emission intensity of the system.

[0104] In one embodiment, step S5, generating a collaborative scheduling strategy based on the carbon responsibility sharing coefficients of each entity, includes:

[0105] An asymmetric Nash bargaining model is constructed based on the carbon responsibility sharing coefficient, and the Nash bargaining solution is obtained by solving the asymmetric Nash bargaining model. The carbon emission quota of each entity is generated based on the Nash bargaining solution, and the scheduling strategy is generated based on the carbon emission quota.

[0106] In this embodiment of the present invention, the expression of the asymmetric Nash bargaining model is as follows:

[0107]

[0108] Among them, u i is the main income, d i is the minimum benefit threshold, i.e. the negotiation breakdown point, ω i is the bargaining power weight.

[0109] In the embodiment of the present invention, the bargaining power weight ωi and carbon responsibility sharing coefficient γ i Negatively correlated, ω i ∝1-γ i , the lower the carbon responsibility, the stronger the bargaining power of the entity.

[0110] In the embodiment of the present invention, the carbon responsibility allocation coefficient γ i It can also serve as a constraint and benefit distribution weight.

[0111] By solving the asymmetric bargaining model, the embodiment of the present invention can obtain an allocation plan that satisfies Pareto optimality. The allocation plan includes the optimal benefit of the subject and the corresponding carbon emission quota of the subject. The optimal benefit of the subject implies its corresponding carbon emission quota of the subject. When the optimal benefit of the subject is determined, the corresponding carbon emission quota of the subject can be derived.

[0112] In one embodiment, the determination of the carbon emission intensity of each entity includes:

[0113] The carbon emission intensity of each entity is determined based on the unit number, unit output, carbon emission coefficient, power transmission distribution factor and entity predicted load.

[0114] In the embodiment of the present invention, the expression of carbon emission intensity is as follows:

[0115] C node =Σ(G i ×EF i ×PTDF i→node ) / P node ;

[0116] Among them, C node is the carbon emission intensity of the main node, i is the unit number, G i Output for the unit, EF i is the carbon emission coefficient, which can be called from the unit carbon emission coefficient database, PTDF is the power transmission distribution factor, P node Forecast load for nodes.

[0117] In one embodiment, before step S1 , operation data of each entity in the power grid system may be obtained for use in subsequent calculations.

[0118] Among them, the various entities in the power grid system include the power generation side, energy storage side, load side and trading entities. The operating data of the power generation side includes thermal power plants, gas power plants, wind farms, photovoltaic power plants, real-time output of units, start and stop status and climbing rate; the operating data of the energy storage side includes battery energy storage, pumped storage power stations, charging and discharging power, SOC (state of charge) and cycle efficiency; the operating data of the load side includes interruptible loads, virtual power plants (aggregators), adjustable load capacity, response delay time, and compensation price.

[0119] The implementation of the present invention has the following beneficial effects:

[0120] The embodiment of the present invention takes minimizing the total carbon emissions of the system and maximizing the benefits of the subjects as the objective functions, constructs a multi-subject game model, and defines the constraints of the multi-subject game model including the carbon responsibility sharing coefficient constraint and the output constraint. By solving the multi-subject game model, the carbon responsibility sharing coefficients corresponding to different subjects can be obtained, and the differences in carbon emission characteristics of different subjects are comprehensively considered for grid scheduling, avoiding the problem of poor grid scheduling effect caused by allocating the overall carbon emissions of the system to each subject according to the proportion of electricity consumption, thereby effectively improving the effect of grid scheduling.

[0121] Furthermore, the embodiments of the present invention incorporate energy storage units into the calculation of carbon emission entities, comprehensively considering the carbon emission sources in the power system, thereby effectively improving the accuracy of the calculation of the carbon responsibility sharing coefficient, which is beneficial to improving the reliability of the carbon emission scheduling of the power grid and effectively reducing the carbon emission intensity of the system.

[0122] See also Figure 2 Based on the same inventive concept as the above embodiment, the present invention further provides a carbon emission-based power grid dispatching device, comprising:

[0123] The average carbon intensity determination module 10 is used to determine the average carbon intensity of the power grid at the time of charging of the energy storage unit in the power grid system;

[0124] An equivalent carbon emission intensity determination module 20 is configured to determine the equivalent carbon emission intensity of the energy storage unit based on the average carbon intensity of the power grid and the charging power time series data of the energy storage unit;

[0125] A game model construction module 30 is used to construct a multi-agent game model with the objective function of minimizing the total carbon emissions of the system and maximizing the benefits of the subject. The constraints of the multi-agent game model include carbon responsibility sharing coefficient constraints and output constraints.

[0126] The game model solving module 40 is used to solve the multi-agent game model based on the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each entity to obtain the carbon responsibility allocation coefficient of each entity;

[0127] The power grid dispatching module 50 is used to generate a coordinated dispatching strategy according to the carbon responsibility sharing coefficient of each entity, and to regulate the carbon emission intensity of each entity with the dispatching instructions corresponding to the coordinated dispatching strategy.

[0128] In one embodiment, the average carbon intensity determination module 10 is further configured to:

[0129] Based on the timestamp association method, the average carbon intensity of the grid at the time of charging of the energy storage unit in the grid system is determined.

[0130] In one embodiment, the equivalent carbon emission intensity determination module 20 is further configured to:

[0131] The equivalent carbon emission intensity of the energy storage unit is determined according to the following equivalent carbon emission intensity formula:

[0132] CE ESS =Σ(P charge(t) ×C grid(t) ) / P discharge_total

[0133] Among them, CE ESS is the equivalent carbon emission intensity of the energy storage unit, P charge(t) is the charging power time series data of the energy storage unit, P discharge_total is the total charge capacity, C grid(t) is the average carbon intensity of the grid at the time t when the energy storage unit is charged.

[0134] In one embodiment, the upper-level optimization goal of the multi-agent game model is to minimize the total carbon emissions of the system, and the lower-level optimization goal is to maximize the benefits of each agent;

[0135] Among them, the upper-level optimization target is constructed based on the subject's carbon responsibility sharing coefficient, the subject's carbon emission intensity and the subject's power; the lower-level optimization target is constructed based on the subject's unit output income and the subject's cost function.

[0136] In one embodiment, the game model solving module 40 is further configured to:

[0137] According to the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each entity, the alternating direction multiplier method is used to solve the multi-agent game model to obtain the carbon responsibility sharing coefficient of each entity.

[0138] In one embodiment, the power grid dispatching module 50 is further configured to:

[0139] An asymmetric Nash bargaining model is constructed based on the carbon responsibility sharing coefficient, and the Nash bargaining solution is obtained by solving the asymmetric Nash bargaining model. The carbon emission quota of each entity is generated based on the Nash bargaining solution, and the scheduling strategy is generated based on the carbon emission quota.

[0140] In one embodiment, the carbon emission-based power grid dispatching device further includes a carbon emission intensity determination module, which is configured to:

[0141] The carbon emission intensity of each entity is determined based on the unit number, unit output, carbon emission coefficient, power transmission distribution factor and entity predicted load.

[0142] It can be understood that the above-mentioned device embodiment corresponds to the method embodiment of the present invention, which can implement any one of the above-mentioned method embodiments of the present invention to provide a carbon emission-based power grid scheduling method.

[0143] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. Furthermore, in the drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which may be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement the present invention without inventive effort.

[0144] Based on the above-mentioned embodiment of the carbon emission-based power grid dispatching method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the carbon emission-based power grid dispatching method of any embodiment of the present invention is implemented.

[0145] For example, in this embodiment, the computer program may be divided into one or more modules, one or more of which are stored in a memory and executed by a processor to implement the present invention. One or more module elements may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device.

[0146] The terminal device can be a computing device such as a desktop computer, notebook computer, PDA, or cloud server. The terminal device may include, but is not limited to, a processor and memory.

[0147] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0148] Based on the above-mentioned method embodiments, another embodiment of the present invention provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the carbon emission-based power grid scheduling method described in any one of the above-mentioned method embodiments of the present invention.

[0149] Wherein, the module / unit integrated in the device / terminal equipment, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0150] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for power grid dispatching based on carbon emissions, characterized in that: include: Determine the average grid carbon intensity of energy storage units in the grid system at the time of charging; Determining the equivalent carbon emission intensity of the energy storage unit based on the average carbon intensity of the power grid and the charging power time series data of the energy storage unit; A multi-agent game model is constructed with the objective function of minimizing the total carbon emissions of the system and maximizing the benefits of the subject. The constraints of the multi-agent game model include carbon responsibility sharing coefficient constraints and output constraints. Solving the multi-agent game model based on the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each entity to obtain the carbon responsibility sharing coefficient of each entity; A collaborative scheduling strategy is generated according to the carbon responsibility sharing coefficient of each entity, and the carbon emission intensity of each entity is regulated by the scheduling instructions corresponding to the collaborative scheduling strategy.

2. The carbon emission-based power grid dispatching method according to claim 1, wherein: Determining the average carbon intensity of the power grid at the time of charging of the energy storage unit in the power grid system includes: Based on the timestamp association method, the average carbon intensity of the grid at the time of charging of the energy storage unit in the grid system is determined.

3. The carbon emission-based power grid dispatching method according to claim 1, wherein: Determining the equivalent carbon emission intensity of the energy storage unit according to the average carbon intensity of the power grid and the charging power time series data of the energy storage unit includes: The equivalent carbon emission intensity of the energy storage unit is determined according to the following equivalent carbon emission intensity formula: WHAT ESS =Σ(P charge(t) ×C grid(t) ) / P discharge_total Among them, CE ESS is the equivalent carbon emission intensity of the energy storage unit, P charge(t) is the charging power time series data of the energy storage unit, P discharge_total is the total charge capacity, C grid(t) is the average carbon intensity of the grid at the time t when the energy storage unit is charged.

4. The carbon emission-based grid dispatching method according to claim 1, wherein: The upper optimization goal of the multi-agent game model is to minimize the total carbon emissions of the system, and the lower optimization goal is to maximize the benefits of each agent; Among them, the upper-level optimization target is constructed based on the carbon responsibility sharing coefficient of the subject, the carbon emission intensity of the subject and the power of the subject; the lower-level optimization target is constructed based on the unit output income of the subject and the cost function of the subject.

5. The carbon emission-based power grid dispatching method according to claim 1, wherein: Solving the multi-agent game model based on the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each entity to obtain the carbon responsibility sharing coefficient of each entity includes: According to the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each subject, the alternating direction multiplier method is used to solve the multi-agent game model to obtain the carbon responsibility sharing coefficient of each subject.

6. The carbon emission-based power grid dispatching method according to claim 1, wherein: The generation of a collaborative scheduling strategy based on the carbon responsibility sharing coefficients of each entity includes: An asymmetric Nash bargaining model is constructed according to the carbon responsibility sharing coefficient, the asymmetric Nash bargaining model is solved to obtain a Nash bargaining solution, the carbon emission quota of each entity is generated according to the Nash bargaining solution, and a scheduling strategy is generated according to the carbon emission quota.

7. The carbon emission-based power grid dispatching method according to claim 1, wherein: The determination of the carbon emission intensity of each entity includes: The carbon emission intensity of each entity is determined based on the unit number, unit output, carbon emission coefficient, power transmission distribution factor and entity predicted load.

8. A power grid dispatching device based on carbon emissions, characterized in that: include: An average carbon intensity determination module, used to determine the average carbon intensity of the grid at the time of charging of the energy storage unit in the grid system; An equivalent carbon emission intensity determination module is used to determine the equivalent carbon emission intensity of the energy storage unit based on the average carbon intensity of the power grid and the charging power time series data of the energy storage unit; A game model construction module is used to construct a multi-agent game model with the objective function of minimizing the total carbon emissions of the system and maximizing the benefits of the subject. The constraints of the multi-agent game model include carbon responsibility sharing coefficient constraints and output constraints. a game model solving module, configured to solve the multi-agent game model based on the equivalent carbon emission intensity of the energy storage unit and the carbon emission intensity of each entity, and obtain a carbon responsibility sharing coefficient of each entity; The power grid dispatching module is used to generate a coordinated dispatching strategy based on the carbon responsibility sharing coefficient of each entity, and to regulate the carbon emission intensity of each entity with the dispatching instructions corresponding to the coordinated dispatching strategy.

9. A terminal device, characterized in that: The system comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the carbon emission-based power grid scheduling method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that include: A stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the carbon emission-based power grid scheduling method according to any one of claims 1 to 7.

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