Source-network-load-storage multi-agent cooperative scheduling method and device based on carbon emission, terminal equipment and storage medium

Abstract

The invention discloses a source-network-load-storage multi-agent cooperative scheduling method and device based on carbon emission, terminal equipment and a storage medium, and belongs to the field of power systems. The method comprises the following steps: constructing a thermal power generating unit direct carbon emission model, a new energy unit full life cycle carbon emission model, a load indirect carbon emission model, an energy storage system carbon emission model and a cross-region transmission carbon emission model; based on a thermal power generating unit direct carbon emission model, a new energy unit full life cycle carbon emission model, a load indirect carbon emission model, an energy storage system carbon emission model and a cross-region transmission carbon emission model, a multi-target optimization model is constructed by taking minimization of system total carbon emission, minimization of carbon emission intensity and minimization of operation cost as targets; the multi-objective optimization model is solved; and scheduling according to a solving result. By implementing the method, the problem that a comprehensive carbon emission model considering the source, the network, the load and the storage is lacked in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a method, device, terminal equipment and storage medium for coordinated dispatching of multiple entities of sources, grids, loads and storage based on carbon emissions. Background Art

[0002] With the advancement of large-scale grid integration of renewable energy, power system dispatch faces challenges such as economic efficiency, stability, and low-carbonization. Existing carbon management methods are relatively simple, such as limiting thermal power output through carbon quotas or introducing carbon trading costs. These methods fail to incorporate a comprehensive carbon emission linkage model encompassing power generation, grid, load, and storage. For example, some approaches reduce direct carbon emissions by optimizing thermal power units, while ignoring the impact of renewable energy's full lifecycle carbon emissions, indirect carbon emissions from loads, and carbon transfer from energy storage charging and discharging. This results in power system dispatch based on carbon emission constraints being poorly robust and unable to adapt to real-world scenarios. Summary of the Invention

[0003] The present invention provides a method, device, terminal equipment and storage medium for multi-agent coordinated scheduling of sources, networks, loads and storage based on carbon emissions, which can solve the problem in the existing technology of lacking a comprehensive carbon emission model that considers sources, networks, loads and storage.

[0004] An embodiment of the present invention provides a method for coordinated scheduling of multiple entities including source, grid, load and storage based on carbon emissions, including:

[0005] Obtain the type and combustion efficiency of each thermal power unit in the power system, obtain the phased carbon emission factor of each new energy unit in the power system, obtain the load type and load power consumption of each load in the power system, obtain the dynamic carbon emission factor of the power grid in the power system, obtain the energy storage type, charging efficiency and cumulative number of charge and discharge cycles of each energy storage system in the power system, obtain the cross-regional transmission carbon emission factor, power generation share and regional carbon price factor of each sending-end regional power source in the power system, and obtain the aging status of the power system lines;

[0006] Based on the type of thermal power unit, combustion efficiency, phased carbon emission factors, load type, load power consumption, grid dynamic carbon emission factors, energy storage type, charging efficiency, cumulative number of charge and discharge cycles, inter-regional transmission carbon emission factors, power generation ratio, regional carbon price factors and line aging, a direct carbon emission model for thermal power units, a full life cycle carbon emission model for new energy units, an indirect carbon emission model for loads, a carbon emission model for energy storage systems and an inter-regional transmission carbon emission model are constructed;

[0007] Based on the direct carbon emission model of thermal power units, the carbon emission model of new energy units throughout their life cycle, the indirect carbon emission model of loads, the carbon emission model of energy storage systems, and the carbon emission model of inter-regional transmission, a multi-objective optimization model was constructed with the goals of minimizing the total carbon emissions of the system, minimizing the carbon emission intensity, and minimizing the operating costs. The decision variables included the power generation of thermal power units, the power generation of new energy units, the charging power of the energy storage system, and the transmission power of the main grid.

[0008] Under the constraints of carbon emission dual control, power balance, equipment operating limit, and cross-regional transmission carbon flow, the multi-objective optimization model is solved to obtain the target thermal power generation power of each thermal power unit, the target new energy power generation power of each new energy unit, the target charging power of each energy storage system, the target load power consumption of each load, and the target main grid transmission power;

[0009] Based on the target power generation power of thermal power units, target power generation power of new energy units, target charging power, target load power consumption and target main grid transmission power, multiple entities of source, grid, load and storage are coordinated and dispatched.

[0010] Furthermore, according to the type of thermal power unit, combustion efficiency, phased carbon emission factor, load type, load power consumption, grid dynamic carbon emission factor, energy storage type, charging efficiency, cumulative number of charge and discharge cycles, inter-regional transmission carbon emission factor, power generation ratio, regional carbon price factor and line aging, a direct carbon emission model for thermal power units, a full life cycle carbon emission model for new energy units, an indirect carbon emission model for loads, a carbon emission model for energy storage systems and an inter-regional transmission carbon emission model are constructed, including:

[0011] Construct a direct carbon emission model for thermal power units based on the type and combustion efficiency of each thermal power unit;

[0012] Based on the phased carbon emission factors of each new energy unit, a carbon emission model for the entire life cycle of the new energy unit is constructed;

[0013] Construct a load indirect carbon emission model based on the load type, load power consumption and dynamic carbon emission factor of each load;

[0014] Construct a carbon emission model for the energy storage system based on the energy storage type, charging efficiency, cumulative charge and discharge cycles, and dynamic carbon emission factor of the power grid.

[0015] An inter-regional transmission carbon emission model is constructed based on the inter-regional transmission carbon emission factor of each sending-end regional power source, the proportion of power generation of each sending-end regional power source, the regional carbon price factor of each sending-end regional power source and the line aging status.

[0016] Furthermore, the direct carbon emission model of the thermal power unit is expressed as follows:

[0017]

[0018] Where C coal,a represents the direct carbon emissions of the a-th thermal power unit; P coal,a represents the power generation capacity of the a-th thermal power unit; α type,a C represents the thermal power unit type correction coefficient of the a-th thermal power unit; fuel,a represents the carbon content per unit calorific value of the fuel of the a-th thermal power unit; η comb,a represents the combustion efficiency of the a-th thermal power unit;

[0019] The expression of the carbon emission model of the new energy unit throughout its life cycle is:

[0020]

[0021] Where C renew,b represents the carbon emissions of the b-th new energy unit throughout its life cycle; P renew,b represents the power generation capacity of the new energy unit b; EF stage,b,k represents the phased carbon emission factor of the b-th new energy unit in the k-th stage, where k = 1 is the manufacturing stage, k = 2 is the transportation stage, k = 3 is the operation stage, and k = 4 is the retirement stage; β region,b represents the regional correction factor of the b-th new energy unit;

[0022] The expression of the load indirect carbon emission model is:

[0023] C load,c =∑ t (L c,t ×EF grid,t ×γ type,c );

[0024] Where C load,c represents the indirect carbon emissions of the cth load; L c,t Indicates the load power consumption of the cth load in time period t; EF grid,t represents the dynamic carbon emission factor of the power system in time period t; γ type,c represents the load type weight coefficient of the cth load;

[0025] The expression of the carbon emission model of the energy storage system is:

[0026]

[0027] Where C ess,d represents the total carbon emissions of the d-th energy storage system; Pch,d,t represents the charging power of the d-th energy storage system in time period t; η ch,d represents the charging efficiency of the d-th energy storage system in time period t; δ cycle,d represents the cycle life attenuation coefficient of the d-th energy storage system; N cycle,d Represents the cumulative number of charge and discharge cycles of the d-th energy storage system; ∈ type,d represents the energy storage type correction coefficient of the d-th energy storage system;

[0028] The expression of the inter-regional transmission carbon emission model is:

[0029] C trans,e =∑ t (P trans ×(EF source,e ×λ e )×ζ carbon,e ×(α loss +θ age ));

[0030] Where C trans,e represents the total amount of carbon emissions from cross-regional transmission of power sources in the e-th sending region; P trans Indicates the main network transmission power; EF source,e represents the inter-regional transmission carbon emission factor of the power supply in the e-th sending region; λ e represents the proportion of power generation in the e-th sending-end area; ζ carbon,e represents the regional carbon price factor of the e-th sending-end regional power source; α loss represents the line basic loss rate; θ age Indicates the line aging loss correction factor.

[0031] Furthermore, the expression of the multi-objective optimization model is:

[0032]

[0033] min C0=C gen +C carbon -R carbon ;

[0034]

[0035] R carbon =(C cap -C T )×p carbon ;

[0036] Where C T represents the total carbon emissions of the system; A represents the number of thermal power units; B represents the number of new energy units; C represents the number of loads; D represents the number of energy storage systems; E represents the number of power sources in the sending area; Ic Indicates carbon emission intensity; E T represents the total power generation of the system; C0 represents the system operating cost; C gen represents the cost of power generation; C carbon represents the carbon quota cost; R carbon Represents carbon credit income; EF coal,a represents the carbon emission factor of the thermal power unit a; p carbon represents the carbon price; C cap Indicates the total amount of carbon quota.

[0037] Furthermore, the expression of the dual control constraint of carbon emissions is:

[0038]

[0039] Where, Indicates the upper limit of total carbon emissions; I cap represents the carbon emission intensity threshold;

[0040] The expression of the power balance constraint is:

[0041]

[0042] Where, P loss Indicates the total power loss of the system;

[0043] The expression of the equipment operation limit constraint is:

[0044] P coal,a,min ≤P coal,a ≤P coal,a,max ;

[0045] 0≤P renew,b ≤P renew,b,max ;

[0046]

[0047] -P trans,max ≤P trans ≤P trans,max ;

[0048] Where, P coal,a,min represents the minimum output of the a-th thermal power unit; P coal,a,max represents the maximum output of the a-th thermal power unit; P renew,b,max Indicates the maximum available power of the b-th new energy unit; SOC d Indicates the state of charge of the d-th energy storage system; SOC d,min Indicates the lower limit of the energy storage charge state of the d-th energy storage system; SOC d,max The upper limit of the energy storage state of charge of the d-th energy storage system; P ess,drepresents the power of the d-th energy storage system; P ch,d,max represents the maximum charging power of the d-th energy storage system; P dis,d,max represents the maximum discharge power of the d-th energy storage system; P trans,max Indicates the maximum transmission power of the main network;

[0049] The expression of the cross-regional carbon flow constraint is:

[0050] C trans,e ≤C trans,e,max ;

[0051] Where C trans,e,max It represents the upper limit of carbon emissions from inter-regional transmission of power sources in the e-th sending region.

[0052] Furthermore, solving the multi-objective optimization model includes:

[0053] The multi-objective optimization model is solved using the NSGA-III algorithm.

[0054] Furthermore, the coordinated scheduling of multiple entities of source, grid, load and storage based on the target power generation power of thermal power units, target power generation power of new energy units, target charging power, target load power consumption and target main grid transmission power includes:

[0055] dispatching each thermal power unit according to a target thermal power unit power generation power of each thermal power unit so that the power generation power of the thermal power unit meets the target thermal power unit power generation power;

[0056] Dispatch each new energy unit according to its target power generation capacity;

[0057] Dispatch each energy storage system based on its target charging power;

[0058] Dispatch each load according to its target load power;

[0059] According to the target main grid transmission power, the power transmission control equipment, reactive power regulation equipment and power electronic equipment in the power system are dispatched.

[0060] Another embodiment of the present invention further provides a source-grid-load-storage multi-agent collaborative scheduling device based on carbon emissions, comprising: a data acquisition module, a model building module, a model solving module and a scheduling module;

[0061] The data acquisition module is used to obtain the type and combustion efficiency of each thermal power unit in the power system, obtain the phased carbon emission factor of each new energy unit in the power system, obtain the load type and load power consumption of each load in the power system, obtain the dynamic carbon emission factor of the power grid of the power system, obtain the energy storage type, charging efficiency and cumulative number of charge and discharge cycles of each energy storage system in the power system, obtain the cross-regional transmission carbon emission factor, power generation share and regional carbon price factor of each sending-end regional power source in the power system, and obtain the line aging status of the power system;

[0062] The model construction module is used to construct a direct carbon emission model of the thermal power unit, a full life cycle carbon emission model of the new energy unit, an indirect carbon emission model of the load, a carbon emission model of the energy storage system, and a cross-regional transmission carbon emission model based on the type of thermal power unit, combustion efficiency, phased carbon emission factor, load type, load power consumption, dynamic carbon emission factor of the power grid, energy storage type, charging efficiency, cumulative number of charge and discharge cycles, cross-regional transmission carbon emission factor, power generation proportion, regional carbon price factor, and line aging; based on the direct carbon emission model of the thermal power unit, the full life cycle carbon emission model of the new energy unit, the indirect carbon emission model of the load, the carbon emission model of the energy storage system, and the cross-regional transmission carbon emission model, with the goal of minimizing the total carbon emissions of the system, minimizing the carbon emission intensity, and minimizing the operating cost, a multi-objective optimization model is constructed with the power generation power of the thermal power unit, the power generation power of the new energy unit, the charging power of the energy storage system, and the main grid transmission power as decision variables;

[0063] The model solving module is used to solve the multi-objective optimization model under the constraints of carbon emission dual control, power balance, equipment operation limit, and cross-regional transmission carbon flow, to obtain the target thermal power generation power of each thermal power unit, the target new energy unit generation power of each new energy unit, the target charging power of each energy storage system, the target load power consumption of each load, and the target main grid transmission power;

[0064] The scheduling module is used to coordinate the scheduling of multiple entities including source, grid, load and storage according to the target power generation power of thermal power units, target power generation power of new energy units, target charging power, target load power consumption and target main grid transmission power.

[0065] 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 source-grid-load-storage multi-subject collaborative scheduling method based on carbon emissions as described in the present invention.

[0066] Another embodiment of the present invention also provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to execute the steps of the source-grid-load-storage multi-subject collaborative scheduling method based on carbon emissions of the present invention.

[0067] The following beneficial effects are achieved by implementing the present invention:

[0068] The present invention obtains data related to the power system, and based on the obtained data, constructs a direct carbon emission model for thermal power units, a full life cycle carbon emission model for new energy units, an indirect carbon emission model for loads, a carbon emission model for energy storage systems, and a cross-regional transmission carbon emission model. Among them, the direct carbon emission model for thermal power units and the full life cycle carbon emission model for new energy units are targeted at the electricity production link, i.e., the "source" link; the cross-regional transmission carbon emission model is targeted at the electricity transmission link, i.e., the "grid" link; the indirect carbon emission model for loads is targeted at the electricity consumption link, i.e., the "load" link; and the carbon emission model for energy storage systems is targeted at the electricity storage link, i.e., the "storage" link. It can be seen that the present invention comprehensively considers the four key links of source, grid, load, and storage, and constructs a comprehensive carbon emission model based on the four links, which solves the problem that the existing technology lacks a comprehensive carbon emission model that considers source, grid, load, and storage, improves the robustness of power system scheduling, and can adapt to more actual scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] 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.

[0070] Figure 1 This is a flow chart of a method for coordinated scheduling of multiple entities of source, grid, load and storage based on carbon emissions provided by one embodiment of the present invention;

[0071] Figure 2 This is a structural diagram of a carbon emission-based source-grid-load-storage multi-agent collaborative scheduling device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0072] 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.

[0073] 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 term "include" 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.

[0074] 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.

[0075] See also Figure 1 To address the problem in the prior art of lacking a comprehensive carbon emission model that considers power sources, power grids, loads, and storage, an embodiment of the present invention provides a carbon emission-based multi-agent coordinated scheduling method for power sources, power grids, loads, and storage, including:

[0076] S1. Obtain the type and combustion efficiency of each thermal power unit in the power system, obtain the phased carbon emission factor of each new energy unit in the power system, obtain the load type and load power consumption of each load in the power system, obtain the dynamic carbon emission factor of the power grid in the power system, obtain the energy storage type, charging efficiency and cumulative number of charge and discharge cycles of each energy storage system in the power system, obtain the cross-regional transmission carbon emission factor, power generation share and regional carbon price factor of each sending-end regional power source in the power system, and obtain the line aging status of the power system.

[0077] It should be noted that the power system includes several thermal power units, several new energy units, several loads and several energy storage systems.

[0078] In another embodiment, after obtaining the power system related data, a data cleaning operation, a missing value filling operation, and a standardization operation are performed on the obtained data;

[0079] The data cleaning operation includes: using data cleaning algorithms such as isolation forest detection of outliers and sliding window smoothing to process noise to eliminate abnormal data;

[0080] The missing value filling operation includes: filling missing values ​​by linear interpolation or time series prediction based on ARIMA model;

[0081] The standardization operation includes: using Z-score to standardize and unify data of different dimensions.

[0082] S2. Based on the type of thermal power units, combustion efficiency, phased carbon emission factors, load type, load power consumption, grid dynamic carbon emission factors, energy storage type, charging efficiency, cumulative number of charge and discharge cycles, inter-regional transmission carbon emission factors, power generation ratio, regional carbon price factors and line aging, a direct carbon emission model for thermal power units, a full life cycle carbon emission model for new energy units, an indirect carbon emission model for loads, a carbon emission model for energy storage systems and a cross-regional transmission carbon emission model are constructed.

[0083] In a preferred embodiment, the direct carbon emission model of the thermal power unit, the carbon emission model of the new energy unit throughout its life cycle, the indirect carbon emission model of the load, the carbon emission model of the energy storage system, and the carbon emission model of the inter-regional transmission are constructed based on the type of thermal power unit, combustion efficiency, carbon emission factor by stage, load type, load power consumption, dynamic carbon emission factor of the power grid, energy storage type, charging efficiency, cumulative number of charge and discharge cycles, carbon emission factor of inter-regional transmission, proportion of power generation, regional carbon price factor, and line aging, including:

[0084] Construct a direct carbon emission model for thermal power units based on the type and combustion efficiency of each thermal power unit;

[0085] Based on the phased carbon emission factors of each new energy unit, a carbon emission model for the entire life cycle of the new energy unit is constructed;

[0086] Construct a load indirect carbon emission model based on the load type, load power consumption and dynamic carbon emission factor of each load;

[0087] Construct a carbon emission model for the energy storage system based on the energy storage type, charging efficiency, cumulative charge and discharge cycles, and dynamic carbon emission factor of the power grid.

[0088] An inter-regional transmission carbon emission model is constructed based on the inter-regional transmission carbon emission factor of each sending-end regional power source, the proportion of power generation of each sending-end regional power source, the regional carbon price factor of each sending-end regional power source and the line aging status.

[0089] It should be noted that the direct carbon emission model of thermal power units is used to quantify the direct carbon emissions generated by thermal power units due to the combustion of fossil fuels, and dynamically reflects the impact of unit type, fuel characteristics and combustion efficiency on carbon emissions;

[0090] The full life cycle carbon emission model for new energy units covers the carbon emissions of new energy equipment such as wind power and photovoltaic power generation, from manufacturing, transportation, operation to retirement, avoiding the one-sided focus on the power generation stage.

[0091] The load indirect carbon emission model is used to dynamically link user electricity demand with the grid's carbon emission intensity, quantifying the load side's indirect responsibility for system carbon emissions.

[0092] The energy storage system carbon emission model is used to quantify the impact of energy storage charging and discharging behavior on carbon emissions, taking into account the carbon intensity of charging sources, efficiency losses, and lifespan degradation;

[0093] The inter-regional transmission carbon emission model is used to quantify the carbon emissions of inter-regional power transmission, reflecting the power supply structure at the sending end, line losses and regional carbon price differences.

[0094] In a preferred embodiment, the direct carbon emission model of the thermal power unit is expressed as:

[0095]

[0096] Where C coal,a represents the direct carbon emissions of the a-th thermal power unit; P coal,a represents the power generation capacity of the a-th thermal power unit; α type,a C represents the thermal power unit type correction coefficient of the a-th thermal power unit; fuel,a represents the carbon content per unit calorific value of the fuel of the a-th thermal power unit; η comb,a represents the combustion efficiency of the a-th thermal power unit;

[0097] The expression of the carbon emission model of the new energy unit throughout its life cycle is:

[0098]

[0099] Where C renew,b represents the carbon emissions of the b-th new energy unit throughout its life cycle; P renew,b represents the power generation capacity of the new energy unit b; EF stage,b,k represents the phased carbon emission factor of the b-th new energy unit in the k-th stage, where k = 1 is the manufacturing stage, k = 2 is the transportation stage, k = 3 is the operation stage, and k = 4 is the retirement stage; β region,b represents the regional correction factor of the b-th new energy unit;

[0100] The expression of the load indirect carbon emission model is:

[0101] C load,c =∑ t (L c,t ×EF grid,t ×γ type,c );

[0102] Where C load,c represents the indirect carbon emissions of the cth load; L c,t Indicates the load power consumption of the cth load in time period t; EF grid,t represents the dynamic carbon emission factor of the power system in time period t; γtype,c represents the load type weight coefficient of the cth load;

[0103] The expression of the carbon emission model of the energy storage system is:

[0104]

[0105] Where C ess,d represents the total carbon emissions of the d-th energy storage system; P ch,d,t represents the charging power of the d-th energy storage system in time period t; η ch,d represents the charging efficiency of the d-th energy storage system in time period t; δ cycle,d represents the cycle life attenuation coefficient of the d-th energy storage system; N cycle,d Represents the cumulative number of charge and discharge cycles of the d-th energy storage system; ∈ type,d represents the energy storage type correction coefficient of the d-th energy storage system;

[0106] The expression of the inter-regional transmission carbon emission model is:

[0107] C trans,e =∑ t (P trans ×(EF source,e ×λ e )×ζ carbon,e ×(α loss +θ age ));

[0108] Where C trans,e represents the total amount of carbon emissions from cross-regional transmission of power sources in the e-th sending region; P trans Indicates the main network transmission power; EF source,e represents the inter-regional transmission carbon emission factor of the power supply in the e-th sending region; λ e represents the proportion of power generation in the e-th sending-end area; ζ carbon,e represents the regional carbon price factor of the e-th sending-end regional power source; α loss represents the line basic loss rate; θ age Indicates the line aging loss correction factor.

[0109] It should be noted that the thermal power unit type correction factor α type,a Quantify the impact of different types of thermal power units on carbon emissions due to technological differences.

[0110] Combustion efficiency η comb,a Refers to the ratio of fuel chemical energy converted into effective heat energy, reflecting energy conversion loss; combustion efficiency can be calculated from fuel consumption rate, and the calculation process is as follows:

[0111] Fuel consumption rate F actualIndicates the fuel consumption per unit of electricity generated, If the unit is designed to have an efficiency η design The theoretical fuel consumption rate is: 3600 is the conversion factor of 1kWh = 3600kj, Q fuel is the chemical energy of the fuel; actual combustion efficiency η comb By comparing actual and theoretical fuel consumption rates:

[0112] The phased carbon emission factors are known quantities and can be obtained through: 1. The carbon emission factor database published by the International Energy Agency (IEA) or the country; 2. Calculation based on actual data such as the production process and transportation route of specific equipment; 3. Referencing the full life cycle carbon emission data of similar equipment in academic papers or technical reports.

[0113] Regional correction factor β region,b It is used to correct the differences in carbon emissions of new energy units in different regions throughout their life cycle. For example, if the new energy manufacturing in a certain region relies on high-carbon electricity, the β region,b It may be greater than 1. If the transportation distance in a certain area is short and clean energy is used, the β in the transportation stage region,b May be less than 1.

[0114] EF grid,t It represents the average carbon emission intensity per unit of electricity in the power grid during period t, which changes with the penetration rate of new energy. The calculation formula is:

[0115]

[0116] Where C coal,t,a represents the direct carbon emissions of the a-th thermal power unit in time period t; C renew,t,b represents the carbon emissions of the bth new energy unit in the entire life cycle during period t; C trans,t,e represents the total amount of carbon emissions from inter-regional transmission of the power source in the e-th sending region during time period t;

[0117] When the penetration rate of new energy increases, the proportion of thermal power decreases, and C coal,t,a Reduce, thereby reducing EF grid,t .

[0118] Load type weight coefficient γ type,c Used to quantify the differentiated responsibilities of different load types for grid carbon emissions; by presetting γ type,c The carbon responsibility of the load can be distinguished, guiding the scheduling strategy to give priority to low-carbon sensitive loads; for example, industrial load (high carbon emission sensitive) is 1.2; commercial load (medium sensitive): 1.0; residential load (low sensitive) is 0.8.

[0119] Cycle life attenuation coefficient δcycle,d It indicates the ratio of energy storage capacity attenuation caused by a single charge and discharge cycle, which is related to energy storage technology, material aging characteristics, and charge and discharge depth.

[0120] Energy storage type correction factor ∈ type,d Used to quantify the differences in carbon emissions over the entire life cycle of different energy storage technologies (such as lithium batteries, lead-acid batteries, and flywheel batteries), including the manufacturing, operation, and decommissioning stages, and embed the impact of energy storage types on carbon emissions into the model.

[0121] Inter-regional transmission carbon emission factor EF source,e It represents the carbon emission intensity per unit of electricity generation of the e-th sending regional power source. It is used to quantify the contribution of different power sources to inter-regional transmission carbon emissions. It is determined based on the actual power source type, fuel characteristics and regional energy structure, and usually comes from the national carbon emission database or measured data.

[0122] Line basic loss rate α loss It is usually related to the voltage level. Generally, the line basic loss rate of old lines is 0.1, and the line basic loss rate of new lines is 0. The calculation formula is:

[0123]

[0124] Where R represents the line resistance, V represents the real-time voltage, and P trans Indicates the transmitted active power; Q trans Indicates the transmitted reactive power.

[0125] S3. Based on the direct carbon emission model of thermal power units, the carbon emission model of new energy units throughout their life cycle, the indirect carbon emission model of loads, the carbon emission model of energy storage systems, and the carbon emission model of inter-regional transmission, with the goals of minimizing the total carbon emissions of the system, minimizing carbon emission intensity, and minimizing operating costs, and with the generated power of thermal power units, the generated power of new energy units, the charging power of the energy storage system, and the main grid transmission power as decision variables, a multi-objective optimization model is constructed.

[0126] In a preferred embodiment, the expression of the multi-objective optimization model is:

[0127]

[0128] min C0=C gen +C carbon -R carbon ;

[0129]

[0130] R carbon =(C cap -C T )×p carbon ;

[0131] Where C T represents the total carbon emissions of the system; A represents the number of thermal power units; B represents the number of new energy units; C represents the number of loads; D represents the number of energy storage systems; E represents the number of power sources in the sending area; I c Indicates carbon emission intensity; E T represents the total power generation of the system; C0 represents the system operating cost; C gen represents the cost of power generation; C carbon represents the carbon quota cost; R carbon Represents carbon credit income; EF coal,a represents the carbon emission factor of the thermal power unit a; p carbon represents the carbon price; C cap Indicates the total amount of carbon quota.

[0132] In a preferred embodiment, the expression of the carbon emission dual control constraint is:

[0133]

[0134] Where, Indicates the upper limit of total carbon emissions; I cap represents the carbon emission intensity threshold;

[0135] The expression of the power balance constraint is:

[0136]

[0137] Where, P loss Indicates the total power loss of the system;

[0138] The expression of the equipment operation limit constraint is:

[0139] P coal,a,min ≤P coal,a ≤P coal,a,max ;

[0140] 0≤P renew,b ≤P renew,b,max ;

[0141]

[0142] -P trans,max ≤P trans ≤P trans,max ;

[0143] Where, P coal,a,min represents the minimum output of the a-th thermal power unit; P coal,a,max represents the maximum output of the a-th thermal power unit; P renew,b,maxIndicates the maximum available power of the b-th new energy unit; SOC d Indicates the state of charge of the d-th energy storage system; SOC d,min Indicates the lower limit of the energy storage charge state of the d-th energy storage system; SOC d,max The upper limit of the energy storage state of charge of the d-th energy storage system; P ess,d represents the power of the d-th energy storage system; P ch,d,max represents the maximum charging power of the d-th energy storage system; P dis,d,max represents the maximum discharge power of the d-th energy storage system; P trans,max Indicates the maximum transmission power of the main network;

[0144] The expression of the cross-regional carbon flow constraint is:

[0145] C trans,e ≤C trans,e,max ;

[0146] Where C trans,e,max It represents the upper limit of carbon emissions from inter-regional transmission of power sources in the e-th sending region.

[0147] S4. Under the constraints of carbon emission dual control, power balance, equipment operation limit, and cross-regional transmission carbon flow, solve the multi-objective optimization model to obtain the target thermal power generation power of each thermal power unit, the target new energy power generation power of each new energy unit, the target charging power of each energy storage system, the target load power consumption of each load, and the target main grid transmission power.

[0148] In a preferred embodiment, solving the multi-objective optimization model includes:

[0149] The NSGA-III algorithm is used to solve the multi-objective optimization model.

[0150] It should be noted that in order to adapt to the multi-objective optimization model (total carbon emissions, intensity, and operating costs), the following improvements are made to the NSGA-III algorithm:

[0151] 1. Target space reconstruction: The three targets (C T , I C 、C O ) are normalized to the same dimension and the reference point distribution is dynamically adjusted based on the hypervolume index to ensure uniform coverage of the Pareto frontier;

[0152] 2. Constraint processing mechanism: adopt dynamic penalty function method to punish those who violate the dual control constraints of carbon emissions (C T >

[0153] C cap or I C >I cap) imposes exponential penalties on individuals, forcing the search direction to converge to the feasible region;

[0154] 3. Population initialization optimization: Generate the initial population based on historical scheduling data, give priority to low-carbon and high-economic solutions, and accelerate algorithm convergence;

[0155] 4. For continuous variables (such as thermal power output and energy storage charging and discharging power), simulated binary crossover (SBX) and Gaussian mutation are used, and for discrete variables (such as inter-regional transmission switch status), uniform crossover and random flipping mutation are used to balance the search breadth and depth.

[0156] Through the above improvements, NSGA-III can efficiently solve multi-objective conflict problems and output a Pareto optimal solution set that meets carbon emission constraints.

[0157] S5. Coordinated dispatch of multiple entities including source, grid, load and storage is carried out based on the target power generation of thermal power units, target power generation of new energy units, target charging power, target load power consumption and target main grid transmission power.

[0158] In another embodiment, a coordinated dispatch instruction is generated according to the target thermal power generation power, the target new energy power generation power, the target charging power, the target load power consumption, and the target main grid transmission power;

[0159] The coordinated dispatch instructions include: thermal power unit output plan, new energy consumption ratio, energy storage charging and discharging strategy, main grid cross-regional transmission power and load demand response instructions.

[0160] It should be noted that the output plan of thermal power units is used to dynamically adjust thermal power to balance carbon emissions and economy; the proportion of new energy consumption is used to maximize the proportion of wind and solar power generation and reduce the carbon intensity of the system; the energy storage charging and discharging strategy is used to charge during low-carbon periods and discharge during high-load periods to smooth out power fluctuations; the main grid's inter-regional transmission power is used to optimize inter-regional power interaction and give priority to purchasing electricity from low-carbon areas; the load demand response instruction is used to guide users to reduce peak electricity consumption through time-of-use electricity prices and reduce indirect carbon emissions.

[0161] In a preferred embodiment, the coordinated scheduling of multiple entities of source, grid, load and storage based on the target power generation power of thermal power units, target power generation power of new energy units, target charging power, target load power consumption and target main grid transmission power includes:

[0162] dispatching each thermal power unit according to a target thermal power unit power generation power of each thermal power unit so that the power generation power of the thermal power unit meets the target thermal power unit power generation power;

[0163] Dispatch each new energy unit according to its target power generation capacity;

[0164] Dispatch each energy storage system based on its target charging power;

[0165] Dispatch each load according to its target load power;

[0166] According to the target main grid transmission power, the power transmission control equipment, reactive power regulation equipment and power electronic equipment in the power system are dispatched.

[0167] It should be noted that power transmission control equipment includes transformers, switchgear, etc.; reactive power regulation equipment includes capacitors, reactors, etc.

[0168] The present invention, by constructing a source-grid-load-storage multi-subject carbon emission correlation model, combines carbon emission dual control targets with multi-objective optimization algorithms, and can collaboratively optimize low-carbon issues and economic issues, effectively improve the new energy absorption rate, and accurately quantify the dynamic carbon allocation of thermal power, new energy, load, and energy storage; through cross-regional carbon flow constraints and coordinated scheduling with the main grid, the system's total carbon emissions and carbon emission intensity are reduced, while reducing operating costs. The improved NSGA-III algorithm effectively solves the multi-objective balance problem of carbon emissions, intensity, and cost, generates scheduling instructions that include low carbon and stability, reduces power dependence on the upper power grid, and ensures supply and demand balance.

[0169] like Figure 2 As shown, based on the above method embodiment, a multi-agent coordinated scheduling device for source, grid, load and storage based on carbon emissions is provided, including: a data acquisition module, a model building module, a model solving module and a scheduling module;

[0170] The data acquisition module is used to obtain the type and combustion efficiency of each thermal power unit in the power system, obtain the phased carbon emission factor of each new energy unit in the power system, obtain the load type and load power consumption of each load in the power system, obtain the dynamic carbon emission factor of the power grid of the power system, obtain the energy storage type, charging efficiency and cumulative number of charge and discharge cycles of each energy storage system in the power system, obtain the cross-regional transmission carbon emission factor, power generation share and regional carbon price factor of each sending-end regional power source in the power system, and obtain the line aging status of the power system;

[0171] The model construction module is used to construct a direct carbon emission model of the thermal power unit, a full life cycle carbon emission model of the new energy unit, an indirect carbon emission model of the load, a carbon emission model of the energy storage system, and a cross-regional transmission carbon emission model based on the type of thermal power unit, combustion efficiency, phased carbon emission factor, load type, load power consumption, dynamic carbon emission factor of the power grid, energy storage type, charging efficiency, cumulative number of charge and discharge cycles, cross-regional transmission carbon emission factor, power generation proportion, regional carbon price factor, and line aging; based on the direct carbon emission model of the thermal power unit, the full life cycle carbon emission model of the new energy unit, the indirect carbon emission model of the load, the carbon emission model of the energy storage system, and the cross-regional transmission carbon emission model, with the goal of minimizing the total carbon emissions of the system, minimizing the carbon emission intensity, and minimizing the operating cost, a multi-objective optimization model is constructed with the power generation power of the thermal power unit, the power generation power of the new energy unit, the charging power of the energy storage system, and the main grid transmission power as decision variables;

[0172] The model solving module is used to solve the multi-objective optimization model under the constraints of carbon emission dual control, power balance, equipment operation limit, and cross-regional transmission carbon flow, to obtain the target thermal power generation power of each thermal power unit, the target new energy unit generation power of each new energy unit, the target charging power of each energy storage system, the target load power consumption of each load, and the target main grid transmission power;

[0173] The scheduling module is used to coordinate the scheduling of multiple entities including source, grid, load and storage according to the target power generation power of thermal power units, target power generation power of new energy units, target charging power, target load power consumption and target main grid transmission power.

[0174] It can be understood that the above-mentioned device embodiment corresponds to the method embodiment of the present invention, which can implement any of the above-mentioned method embodiments of the present invention to provide a source-grid-load-storage multi-agent collaborative scheduling method based on carbon emissions.

[0175] 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.

[0176] Based on the above-mentioned method embodiment, 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, it implements the carbon emission-based source-grid-load-storage multi-agent collaborative scheduling method of any embodiment of the present invention.

[0177] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The 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 the terminal device.

[0178] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0179] 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.

[0180] 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 source-grid-load-storage multi-agent collaborative scheduling method described in any one of the above-mentioned method embodiments of the present invention.

[0181] 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.

[0182] 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 multi-agent coordinated scheduling method for sources, grids, loads and storage based on carbon emissions, characterized by: include: Obtain the type and combustion efficiency of each thermal power unit in the power system, obtain the phased carbon emission factor of each new energy unit in the power system, obtain the load type and load power consumption of each load in the power system, obtain the dynamic carbon emission factor of the power grid in the power system, obtain the energy storage type, charging efficiency and cumulative number of charge and discharge cycles of each energy storage system in the power system, obtain the cross-regional transmission carbon emission factor, power generation share and regional carbon price factor of each sending-end regional power source in the power system, and obtain the aging status of the power system lines; Based on the type of thermal power unit, combustion efficiency, phased carbon emission factors, load type, load power consumption, grid dynamic carbon emission factors, energy storage type, charging efficiency, cumulative number of charge and discharge cycles, inter-regional transmission carbon emission factors, power generation ratio, regional carbon price factors and line aging, a direct carbon emission model for thermal power units, a full life cycle carbon emission model for new energy units, an indirect carbon emission model for loads, a carbon emission model for energy storage systems and an inter-regional transmission carbon emission model are constructed; Based on the direct carbon emission model of thermal power units, the carbon emission model of new energy units throughout their life cycle, the indirect carbon emission model of loads, the carbon emission model of energy storage systems, and the carbon emission model of inter-regional transmission, a multi-objective optimization model was constructed with the goals of minimizing the total carbon emissions of the system, minimizing the carbon emission intensity, and minimizing the operating costs. The decision variables included the power generation of thermal power units, the power generation of new energy units, the charging power of the energy storage system, and the transmission power of the main grid. Under the constraints of carbon emission dual control, power balance, equipment operating limit, and cross-regional transmission carbon flow, the multi-objective optimization model is solved to obtain the target thermal power generation power of each thermal power unit, the target new energy power generation power of each new energy unit, the target charging power of each energy storage system, the target load power consumption of each load, and the target main grid transmission power; Based on the target power generation power of thermal power units, target power generation power of new energy units, target charging power, target load power consumption and target main grid transmission power, multiple entities of source, grid, load and storage are coordinated and dispatched.

2. The carbon emission-based source-grid-load-storage multi-agent coordinated scheduling method according to claim 1 is characterized in that: According to the type of thermal power unit, combustion efficiency, phased carbon emission factor, load type, load power consumption, grid dynamic carbon emission factor, energy storage type, charging efficiency, cumulative number of charge and discharge cycles, inter-regional transmission carbon emission factor, power generation ratio, regional carbon price factor and line aging, a direct carbon emission model for thermal power units, a full life cycle carbon emission model for new energy units, an indirect carbon emission model for loads, a carbon emission model for energy storage systems and an inter-regional transmission carbon emission model are constructed, including: Construct a direct carbon emission model for thermal power units based on the type and combustion efficiency of each thermal power unit; Based on the phased carbon emission factors of each new energy unit, a carbon emission model for the entire life cycle of the new energy unit is constructed; Construct a load indirect carbon emission model based on the load type, load power consumption and dynamic carbon emission factor of each load; Construct a carbon emission model for the energy storage system based on the energy storage type, charging efficiency, cumulative charge and discharge cycles, and dynamic carbon emission factor of the power grid. An inter-regional transmission carbon emission model is constructed based on the inter-regional transmission carbon emission factor of each sending-end regional power source, the proportion of power generation of each sending-end regional power source, the regional carbon price factor of each sending-end regional power source and the line aging status.

3. The carbon emission-based source-grid-load-storage multi-agent coordinated scheduling method according to claim 2 is characterized in that: The direct carbon emission model of the thermal power unit is expressed as follows: Where C coal,a represents the direct carbon emissions of the a-th thermal power unit; P coal,a represents the power generation capacity of the a-th thermal power unit; α type,a C represents the thermal power unit type correction coefficient of the a-th thermal power unit; fuel,a Indicates the carbon content per unit calorific value of the fuel of the a-th thermal power unit; η comb,a represents the combustion efficiency of the a-th thermal power unit; The expression of the carbon emission model of the new energy unit throughout its life cycle is: Where C renew,b represents the carbon emissions of the b-th new energy unit throughout its life cycle; P renew,b represents the power generation capacity of the new energy unit b; EF stage,b,k represents the phased carbon emission factor of the b-th new energy unit in the k-th stage, where k = 1 is the manufacturing stage, k = 2 is the transportation stage, k = 3 is the operation stage, and k = 4 is the retirement stage; β region,b represents the regional correction factor of the b-th new energy unit; The expression of the load indirect carbon emission model is: C load,c =∑ t (L c,t ×EF grid,t ×γ type,c ); Where C load,c represents the indirect carbon emissions of the cth load; L c,t Indicates the load power consumption of the cth load in time period t; EF grid,t represents the dynamic carbon emission factor of the power system in time period t; γ type,c represents the load type weight coefficient of the cth load; The expression of the carbon emission model of the energy storage system is: Where C ess,d represents the total carbon emissions of the d-th energy storage system; P ch,d,t represents the charging power of the d-th energy storage system in time period t; η ch,d represents the charging efficiency of the d-th energy storage system in time period t; δ cycle,d represents the cycle life attenuation coefficient of the d-th energy storage system; N cycle,d Represents the cumulative number of charge and discharge cycles of the d-th energy storage system; ∈ type,d represents the energy storage type correction coefficient of the d-th energy storage system; The expression of the inter-regional transmission carbon emission model is: C trans,e =∑ t (P trans ×(EF source,e ×λ e )×ζ carbon,e ×(a loss +θ age )); Where C trans,e represents the total amount of carbon emissions from cross-regional transmission of power sources in the e-th sending region; P trans Indicates the main network transmission power; EF source,e represents the inter-regional transmission carbon emission factor of the power supply in the e-th sending region; λ e represents the proportion of power generation in the e-th sending-end area; ζ carbon,e represents the regional carbon price factor of the e-th sending-end regional power source; α loss represents the line basic loss rate; θ age Indicates the line aging loss correction factor.

4. The carbon emission-based source-grid-load-storage multi-agent coordinated scheduling method according to claim 3 is characterized in that: The expression of the multi-objective optimization model is: min C0=C gen +C carbon -R carbon ; R carbon =(C cap -C T )×p carbon ; Where C T represents the total carbon emissions of the system; A represents the number of thermal power units; B represents the number of new energy units; C represents the number of loads; D represents the number of energy storage systems; E represents the number of power sources in the sending area; I c Indicates carbon emission intensity; E T represents the total power generation of the system; C0 represents the system operating cost; C gen represents the cost of power generation; C carbon represents the carbon quota cost; R carbon Represents carbon credit income; EF coal,a represents the carbon emission factor of the thermal power unit a; p carbon represents the carbon price; C cap Indicates the total amount of carbon quota.

5. The carbon emission-based source-grid-load-storage multi-agent coordinated scheduling method according to claim 4 is characterized in that: The expression of the dual control constraint of carbon emissions is: Where, Indicates the upper limit of total carbon emissions; I cap represents the carbon emission intensity threshold; The expression of the power balance constraint is: Where, P loss Indicates the total power loss of the system; The expression of the equipment operation limit constraint is: P coal,a,min ≤P coal,a ≤P coal,a,max ; 0≤P renew,b ≤P renew,b,max ; -P trans,max ≤P trans ≤P trans,max ; Where, P coal,a,min represents the minimum output of the a-th thermal power unit; P coal,a,max represents the maximum output of the a-th thermal power unit; P renew,b,max Indicates the maximum available power of the b-th new energy unit; SOC d Indicates the state of charge of the d-th energy storage system; SOC d,min Indicates the lower limit of the energy storage charge state of the d-th energy storage system; SOC d,max The upper limit of the energy storage state of charge of the d-th energy storage system; P ess,d represents the power of the d-th energy storage system; P ch,d,max represents the maximum charging power of the d-th energy storage system; P dis,d,max represents the maximum discharge power of the d-th energy storage system; P trans,max Indicates the maximum transmission power of the main network; The expression of the cross-regional carbon flow constraint is: C trans,e ≤C trans,e,max ; Where C trans,e,max It represents the upper limit of carbon emissions from inter-regional transmission of power sources in the e-th sending region.

6. The carbon emission-based source-grid-load-storage multi-agent coordinated scheduling method according to claim 1 is characterized in that: Solving the multi-objective optimization model includes: The NSGA-III algorithm is used to solve the multi-objective optimization model.

7. The carbon emission-based source-grid-load-storage multi-agent coordinated scheduling method according to claim 1 is characterized in that: The coordinated scheduling of multiple entities of source, grid, load and storage based on the target power generation power of thermal power units, target power generation power of new energy units, target charging power, target load power consumption and target main grid transmission power includes: dispatching each thermal power unit according to a target thermal power unit power generation power of each thermal power unit so that the power generation power of the thermal power unit meets the target thermal power unit power generation power; Dispatch each new energy unit according to its target power generation capacity; Dispatch each energy storage system based on its target charging power; Dispatch each load according to its target load power; According to the target main grid transmission power, the power transmission control equipment, reactive power regulation equipment and power electronic equipment in the power system are dispatched.

8. A multi-agent coordinated dispatching device for source, grid, load and storage based on carbon emissions, characterized in that: include: Data acquisition module, model building module, model solving module and scheduling module; The data acquisition module is used to obtain the type and combustion efficiency of each thermal power unit in the power system, obtain the phased carbon emission factor of each new energy unit in the power system, obtain the load type and load power consumption of each load in the power system, obtain the dynamic carbon emission factor of the power grid of the power system, obtain the energy storage type, charging efficiency and cumulative number of charge and discharge cycles of each energy storage system in the power system, obtain the cross-regional transmission carbon emission factor, power generation share and regional carbon price factor of each sending-end regional power source in the power system, and obtain the line aging status of the power system; The model construction module is used to construct a direct carbon emission model of the thermal power unit, a full life cycle carbon emission model of the new energy unit, an indirect carbon emission model of the load, a carbon emission model of the energy storage system, and a cross-regional transmission carbon emission model based on the type of thermal power unit, combustion efficiency, phased carbon emission factor, load type, load power consumption, dynamic carbon emission factor of the power grid, energy storage type, charging efficiency, cumulative number of charge and discharge cycles, cross-regional transmission carbon emission factor, power generation proportion, regional carbon price factor, and line aging; based on the direct carbon emission model of the thermal power unit, the full life cycle carbon emission model of the new energy unit, the indirect carbon emission model of the load, the carbon emission model of the energy storage system, and the cross-regional transmission carbon emission model, with the goal of minimizing the total carbon emissions of the system, minimizing the carbon emission intensity, and minimizing the operating cost, a multi-objective optimization model is constructed with the power generation power of the thermal power unit, the power generation power of the new energy unit, the charging power of the energy storage system, and the main grid transmission power as decision variables; The model solving module is used to solve the multi-objective optimization model under the constraints of carbon emission dual control, power balance, equipment operation limit, and cross-regional transmission carbon flow, to obtain the target thermal power generation power of each thermal power unit, the target new energy unit generation power of each new energy unit, the target charging power of each energy storage system, the target load power consumption of each load, and the target main grid transmission power; The scheduling module is used to coordinate the scheduling of multiple entities including source, grid, load and storage according to the target power generation power of thermal power units, target power generation power of new energy units, target charging power, target load power consumption and target main grid transmission power.

9. A terminal device, characterized in that: The method 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 method for multi-agent coordinated scheduling of sources, grids, loads and storage based on carbon emissions as described in 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 running, the device where the computer-readable storage medium is located is controlled to execute the carbon emission-based source-grid-load-storage multi-agent collaborative scheduling method according to any one of claims 1 to 7.

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