Comprehensive energy scheduling method, device, equipment and medium

Abstract

The embodiment of the invention provides a comprehensive energy scheduling method and device, equipment and a medium. The method comprises the following steps: determining parameter models corresponding to various energy units in the power network, thereby determining the carbon potential of the corresponding nodes of the various energy units in the power network based on the parameter models, and determining the carbon emission distribution relation of different nodes in the power network according to the total carbon potential and the energy production efficiency of the different nodes. The method comprises the steps that firstly, the energy production efficiency, the operation cost, the carbon potential and the carbon emission constraint condition are output to a multi-target optimization algorithm based on fuzzy decision, then, the corresponding carbon emission constraint condition is determined based on a predetermined scheduling optimization target and the carbon potential so as to determine a follow-up optimization target and constraint, finally, the energy production efficiency, the operation cost, the carbon potential and the carbon emission constraint condition are output to the multi-target optimization algorithm based on fuzzy decision, and a corresponding energy scheduling strategy is output. The method is used for solving the problem that energy scheduling cannot give consideration to improvement of energy utilization efficiency and low-carbon emission requirements.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and in particular to a comprehensive energy dispatching method, device, equipment and medium. Background Technology

[0002] With the current global focus on sustainable development, Integrated Energy Systems (IES), as systems that integrate multiple energy forms, are becoming an important development direction in the energy sector. IES improves energy efficiency and promotes energy conservation and emission reduction by flexibly coordinating the supply, conversion, storage, and consumption of various energy sources such as electricity, heat, and gas. In IES, how to dispatch various energy sources is crucial to energy utilization efficiency and carbon emissions.

[0003] In related technologies, energy dispatching methods focus more on improving energy efficiency, neglecting the importance of low-carbon emission reduction. When faced with rapidly growing energy demand, this may result in energy systems achieving efficient operation while failing to effectively reduce carbon emissions, thus failing to meet the demand for cleaner energy consumption. Summary of the Invention

[0004] This application provides a comprehensive energy dispatching method, apparatus, equipment, and medium to address the problem in related technologies that energy dispatching cannot simultaneously meet the requirements of improving energy utilization efficiency and reducing carbon emissions.

[0005] In a first aspect, embodiments of this application provide a comprehensive energy dispatching method, including:

[0006] Determine the parameter models corresponding to various energy units in the power grid. The parameter models are used to determine the energy production efficiency, operating cost and carbon emission intensity of the corresponding types of energy units, including fossil fuel power generation units and renewable energy units.

[0007] Based on the parametric model, the carbon potential of various energy units at corresponding nodes in the power grid is determined, and the carbon potential is determined based on the carbon emission intensity.

[0008] Based on the predetermined scheduling optimization objectives and carbon potential, the corresponding carbon emission constraints are determined. The scheduling optimization objectives include the lowest operating cost of the power network and the lowest carbon emissions in the power network. The carbon emissions are determined based on the carbon potential.

[0009] The energy production efficiency, operating cost, carbon potential, and carbon emission constraints are output to a multi-objective optimization algorithm based on fuzzy decision-making, which outputs a corresponding energy dispatch strategy. The energy dispatch strategy is used to schedule the operating status of energy units.

[0010] In one possible implementation, the parameters in the parameter model corresponding to the fossil fuel generator set include the corresponding fuel type, carbon emission intensity, output power, and operating cost; the parameters in the parameter model corresponding to the renewable energy generator set include output power.

[0011] In one possible implementation, the carbon potential of various energy units at corresponding nodes in the power network is determined based on a parametric model. This includes: determining the output power and carbon emission intensity of each node based on the pre-determined corresponding nodes of each energy unit in the power network; determining the power distribution and carbon emission flow in the power network lines where each node is located based on the output power of each node, wherein the carbon emission flow is obtained based on the carbon emission intensity and the corresponding output power; and allocating the carbon emission flow corresponding to each node to the power network based on the proportional sharing principle to obtain the carbon potential of each node, wherein the carbon potential is calculated based on the carbon emission flow received by the node and the carbon emission flow generated by the node itself.

[0012] In one possible implementation, based on the principle of proportional sharing, the carbon emission flow corresponding to each node is allocated to the power network to obtain the carbon potential corresponding to each node, including: determining the output power and received carbon emission flow of each node; determining the carbon emission flow allocation ratio of each node based on the output power distribution ratio of each node, wherein the carbon emission flow allocation ratio is positively correlated with the output power distribution ratio; and determining the carbon potential corresponding to each node based on the total carbon emission flow of the line where each node is located and the carbon emission flow allocation ratio.

[0013] In one possible implementation, based on a predetermined scheduling optimization objective and carbon potential, corresponding carbon emission constraints are determined, including: determining the cost constraint corresponding to the lowest operating cost based on the operating cost of each node; determining the carbon emission constraints generated by the power network based on the various energy unit types in the power network; determining the corresponding load adjustment constraints based on the costs incurred when adjusting the electricity load; and determining the carbon emission constraints based on the cost constraints, carbon emission constraints, and load adjustment constraints.

[0014] In one possible implementation, energy production efficiency, operating cost, carbon potential, and carbon emission constraints are output to a multi-objective optimization algorithm based on fuzzy decision-making to output a corresponding energy scheduling strategy. This includes the following steps: outputting energy production efficiency, operating cost, carbon potential, and carbon emission constraints to a multi-objective optimization algorithm based on genetic algorithm to output a corresponding non-dominated solution set; and inputting the non-dominated solution set into the fuzzy decision-making algorithm to output an energy scheduling strategy.

[0015] In one possible implementation, the multi-objective optimization algorithm based on the genetic algorithm is a non-dominated sorting genetic algorithm.

[0016] Secondly, embodiments of this application provide an integrated energy dispatching device, comprising:

[0017] The acquisition module is used to determine the parameter models corresponding to various energy units in the power grid. The parameter models are used to determine the energy production efficiency, operating cost and carbon emission intensity of the corresponding type of energy unit. The energy units include fossil fuel power generation units and renewable energy units.

[0018] The processing module is used to determine the carbon potential of various energy units at corresponding nodes in the power grid based on a parametric model. The carbon potential is determined based on carbon emission intensity.

[0019] The analysis module is used to determine the corresponding carbon emission constraints based on the predetermined scheduling optimization objectives and carbon potential. The scheduling optimization objectives include the lowest operating cost of the power network and the lowest carbon emissions in the power network. The carbon emissions are determined based on the carbon potential.

[0020] The output module is used to output energy production efficiency, operating costs, carbon potential and carbon emission constraints to a multi-objective optimization algorithm based on fuzzy decision-making, and output the corresponding energy dispatch strategy. The energy dispatch strategy is used to schedule the working status of energy units.

[0021] In one possible implementation, the acquisition module specifically includes parameters in the parameter model corresponding to fossil fuel generator sets, including the corresponding fuel type, carbon emission intensity, output power, and operating cost; and parameters in the parameter model corresponding to renewable energy generator sets, including output power.

[0022] In one possible implementation, the processing module is specifically used to: determine the output power and carbon emission intensity of each node based on the pre-determined corresponding node of each energy unit in the power network; determine the power distribution and carbon emission flow in the power network line where each node is located based on the output power of each node, wherein the carbon emission flow is obtained based on the carbon emission intensity and the corresponding output power; and allocate the carbon emission flow corresponding to each node to the power network based on the proportional sharing principle to obtain the carbon potential corresponding to each node, wherein the carbon potential is calculated based on the carbon emission flow received by the node and the carbon emission flow generated by the node itself.

[0023] In one possible implementation, the processing module is specifically used to: determine the output power and received carbon emission flow of each node; determine the carbon emission flow allocation ratio of each node based on the output power distribution ratio of each node, wherein the carbon emission flow allocation ratio is positively correlated with the output power distribution ratio; and determine the carbon potential corresponding to each node based on the total carbon emission flow and carbon emission flow allocation ratio of the line to which each node is located.

[0024] In one possible implementation, the analysis module is specifically used to: determine the cost constraint corresponding to the lowest operating cost based on the operating cost of each node; determine the carbon emission constraint generated by the power network based on the various energy unit types in the power network; determine the corresponding load adjustment constraint based on the cost incurred when adjusting the electricity load; and determine the carbon emission constraint based on the cost constraint, carbon emission constraint, and load adjustment constraint.

[0025] In one possible implementation, the output module is specifically used to output energy production efficiency, operating cost, carbon potential and carbon emission constraints to a multi-objective optimization algorithm based on a genetic algorithm, and output the corresponding non-dominated solution set; input the non-dominated solution set into a fuzzy decision algorithm, and output an energy scheduling strategy.

[0026] In one possible implementation, the output module specifically includes a non-dominated sorting genetic algorithm as the multi-objective optimization algorithm based on the genetic algorithm.

[0027] Thirdly, embodiments of this application provide a control device, including: a memory and a processor;

[0028] The memory stores instructions that the computer executes;

[0029] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0030] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0031] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0032] The integrated energy dispatching method, apparatus, equipment, and medium provided in this application determine the parameter models corresponding to various energy units in the power network, and determine information such as operating costs, energy production efficiency, and carbon emissions of different types of energy units in the integrated energy system. Based on the parameter models, the carbon potential of each energy unit at its corresponding node in the power network can be determined. Then, based on the total carbon potential and the energy production efficiency of different nodes, the carbon emission allocation relationship of different nodes in the power network can be determined. Next, based on the pre-determined dispatching optimization objective and carbon potential, corresponding carbon emission constraints are determined to determine the subsequent optimization objective and constraints. Finally, the energy production efficiency, operating costs, carbon potential, and carbon emission constraints are output to a multi-objective optimization algorithm based on fuzzy decision-making, outputting the corresponding energy dispatching strategy. The resulting energy dispatching strategy can take into account the generation efficiency, operating costs, and carbon emissions of different types of energy, thereby ensuring that operating costs, dispatching costs, and other dispatching and system operation losses are minimized while satisfying the requirement of minimum carbon emissions. This provides the impetus for carbon emission-based energy dispatching, ensuring the realization of energy dispatching, while reducing the operating and dispatching costs of the integrated energy system, increasing its economic efficiency, and meeting the needs of balancing energy utilization efficiency and reducing carbon emissions. Attached Figure Description

[0033] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0034] Figure 1 An application scenario diagram of the integrated energy dispatching method provided in the embodiments of this disclosure;

[0035] Figure 2 A flowchart of an embodiment of the integrated energy dispatching method provided in this disclosure;

[0036] Figure 3 A flowchart of an integrated energy dispatching method provided in yet another embodiment of this disclosure;

[0037] Figure 4 A schematic diagram of the structure of an integrated energy dispatching device provided in yet another embodiment of this disclosure;

[0038] Figure 5 This is a schematic diagram of the structure of a control device provided in one embodiment of the present disclosure.

[0039] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0040] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0041] With the current global focus on sustainable development, the energy source for power grids is shifting from traditional single-energy sources (such as thermal power generation) to integrated energy systems (IES). IES, as a system integrating multiple energy forms, is becoming an important development direction in the energy sector. By flexibly coordinating the supply, conversion, storage, and consumption of various energy sources such as electricity, heat, and gas, IES improves energy efficiency and promotes energy conservation and emission reduction. However, since IES also include traditional fossil fuel energy, how to dispatch various energy sources within IES is crucial to energy utilization efficiency and carbon emissions.

[0042] In related technologies, energy dispatching methods focus more on improving energy efficiency, neglecting the importance of low-carbon emission reduction. Furthermore, energy dispatching operations themselves incur costs, and the cost of outputting the same power varies across different energy sources. Therefore, it is difficult to simultaneously reduce carbon emissions through energy dispatching and save on IES system operating costs while ensuring energy efficiency. When faced with rapidly growing energy demand, this may result in energy systems achieving efficient operation but failing to effectively reduce carbon emissions, thus failing to meet the demand for cleaner energy consumption.

[0043] The integrated energy dispatching method provided in this application establishes a multi-energy unit parameter model covering energy production efficiency, operating costs, and carbon emission intensity. It combines node carbon potential to quantify the impact of carbon emissions, transforming the dual objectives of minimizing operating costs and carbon emissions into constraints. Furthermore, it utilizes a multi-objective optimization algorithm based on fuzzy decision-making to generate dispatching strategies. This approach ensures energy utilization efficiency while balancing system economy and low-carbon emission reduction requirements, thus addressing the problem of traditional dispatching methods struggling to balance energy efficiency, cost, and carbon emissions.

[0044] Figure 1 A schematic diagram illustrating the application scenario of the integrated energy dispatching method provided in this application, such as... Figure 1 As shown, the specific application scenario of this application is as follows: In integrated energy dispatching, the server 100 determines the energy dispatching strategy based on the system data input by the integrated energy system 110, and outputs the corresponding dispatching strategy to the integrated energy system 110, thereby regulating its working status.

[0045] It should be noted that, Figure 1 The scenario shown includes servers and integrated energy systems, which are only used as examples of one or a specific number of such systems. However, this disclosure is not limited to this, meaning that the number of servers and integrated energy systems can be arbitrary.

[0046] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0047] Figure 2 Flowchart of the integrated energy dispatching method provided in this application Figure 1 ,like Figure 2 As shown, the method includes:

[0048] S201. Determine the parameter models corresponding to various energy units in the power network.

[0049] The parametric model is used to determine the energy production efficiency, operating cost, and carbon emission intensity of corresponding types of energy units, including fossil fuel generator units and renewable energy generator units.

[0050] Specifically, this embodiment is used to provide a general description of the main steps of integrated energy dispatch.

[0051] In this embodiment of the disclosure, the executing entity is a module, control system, or server used for data detection and operation status scheduling of the integrated energy system in the power grid. For ease of description, it will be referred to as the server below.

[0052] Before conducting specific scheduling, the server first needs to determine the parameter models of various energy units in the power network, including energy production efficiency, operating costs, and carbon emission intensity.

[0053] Since different types of energy units have different operating modes, energy generation efficiencies, operating costs, and carbon emissions, it is necessary to establish corresponding parameter models for each type of energy unit in order to optimize scheduling in the future.

[0054] For example, the output model of a wind turbine is based on the wind speed-power curve, and its output power is affected by the cut-in wind speed, cut-out wind speed, and rated wind speed; while the output model of a photovoltaic unit needs to consider the light intensity and temperature correction coefficient to reflect the power generation efficiency under actual environmental conditions.

[0055] For fossil fuel-related generator sets, such as gas-fired boilers, the model needs to be linked to natural gas consumption rate and calorific value, and ramp-up power constraints need to be introduced to ensure that they operate within a safe range.

[0056] Models for hydrogen fuel cells need to distinguish between electrical and thermal efficiency, and calculate their carbon emission intensity based on hydrogen consumption rate. Models for combined heat and power (CHP) units need to consider both electrical and thermal output efficiencies, and correlate them with natural gas input power.

[0057] In addition to the energy production units mentioned above, it also includes energy storage equipment, carbon capture and storage equipment, etc.

[0058] The models for energy storage devices need to describe the relationship between their charging / discharging power and capacity. P2G (Power-to-Gas, primarily used to convert renewable energy sources such as wind and solar power into gaseous fuels for hydrogen fuel cell operation and to facilitate the storage of excess renewable energy) devices need to be modeled in stages for the electro-hydrogen and hydrogen-to-methane processes. The models for carbon capture and storage (CCS, used to reduce carbon emissions) devices need to calculate the energy consumption per unit of CO2 captured and correlate it with the carbon emission intensity of the gas turbine and boiler.

[0059] In practical applications, servers can incorporate real-time data (such as weather forecasts and fuel price fluctuations) to dynamically update model parameters. For example, they can adjust the output curve of wind turbines based on short-term wind speed forecasts or optimize the operating costs of gas-fired boilers based on natural gas market prices.

[0060] In addition, the parametric model can also incorporate multi-energy coupling relationships, such as considering the synergistic effect of P2G equipment and renewable energy, and converting surplus wind power into hydrogen or methane to improve system flexibility.

[0061] S202. Based on the parameter model, determine the carbon potential of various energy units at corresponding nodes in the power network.

[0062] Among them, carbon potential is determined based on carbon emission intensity.

[0063] Specifically, after obtaining the parametric model, the carbon potential of each node in the power network can be calculated based on the parametric model. The core of this is to quantify the carbon emissions per unit of electricity at each node.

[0064] The calculation of carbon potential relies on the "proportional sharing principle," which means that the carbon emissions of a node are determined by the carbon emission intensity of the units connected to that node and the carbon flow flowing in from other nodes.

[0065] In practice, it is necessary to construct input-output models of energy and carbon emissions related to nodes and units in order to solve for the node carbon potential vector. For example, the carbon potential of a node may be obtained by weighting the high carbon emissions of the local gas turbine and the low carbon emissions of the wind power of the adjacent node.

[0066] The key to model calculation is to ensure power flow conservation, that is, the active power injected into the node (i.e. the part of the power actually used for load consumption and energy conversion) is equal to the outflow power, thereby ensuring the physical rationality of carbon potential calculation.

[0067] In addition, the dynamic nature of carbon potential needs to be reflected in the scheduling model. For example, during peak periods of wind and solar power generation, the node carbon potential may decrease significantly, thereby guiding the load to shift to low-carbon periods.

[0068] In practical calculations, time-segmented carbon potential calculations can be introduced in the time dimension, such as distinguishing carbon emission differences during peak and off-peak electricity pricing periods to more accurately guide demand response. Alternatively, in the spatial dimension, the nodal carbon potential of different regions can be distinguished based on the characteristics of the regional power grid (e.g., high carbon potential in industrial areas and low carbon potential in residential areas).

[0069] S203. Based on the predetermined scheduling optimization objective and carbon potential, determine the corresponding carbon emission constraints.

[0070] The scheduling optimization objectives include minimizing the operating costs of the power network and minimizing carbon emissions in the power network, with carbon emissions determined based on carbon potential.

[0071] Specifically, servers need to translate the dual objectives of "lowest operating costs" and "lowest carbon emissions" into mathematical constraints, such as setting a carbon emission cap of 80% of the historical average, or ensuring that operating costs do not exceed budget thresholds.

[0072] The setting of constraints must take into account the compatibility of the algorithm. For example, multi-objective optimization algorithms may require constraints to be in the form of inequalities, while fuzzy decision-making requires constraints to be mapped to membership functions.

[0073] In addition, the constraints need to reflect actual operational limitations, such as the minimum start-up and shutdown time of the gas turbine and the depth of charge and discharge of the energy storage equipment.

[0074] By embedding these constraints into a multi-objective optimization framework, it can be ensured that the generated scheduling strategy is both economically feasible and environmentally friendly.

[0075] In some embodiments, multi-objective weight optimization can incorporate user preference models, such as industrial users prioritizing cost while public utilities prioritize emissions reduction, thereby dynamically adjusting the stringency of constraints.

[0076] S204. Output the energy production efficiency, operating cost, carbon potential and carbon emission constraints to the multi-objective optimization algorithm based on fuzzy decision-making, and output the corresponding energy dispatch strategy.

[0077] Among them, the energy dispatch strategy is used to schedule the working status of energy units.

[0078] Specifically, the server can generate the final scheduling strategy by inputting the data and conditions obtained in the aforementioned steps into the multi-objective optimization algorithm.

[0079] Multi-objective optimization algorithms can screen the optimal solution set based on non-dominated sorting and crowding calculation, and then obtain multiple non-dominated solution sets through crossover and mutation iterative optimization. Fuzzy decision-making comprehensively evaluates the non-dominated solution set, for example, by defining membership functions for economic efficiency and low carbon emissions, and combining them with weight vectors to select the final solution. By setting algorithm parameters (such as population size and mutation probability) and designing fuzzy rules (such as index weights), the rationality and operability of the scheduling strategy can be ensured. OCR guarantees that the obtained energy scheduling strategy can meet the aforementioned scheduling optimization objectives.

[0080] In practical calculations, a distributed optimization framework can be combined to decompose the multi-objective optimization in large-scale IES into multiple sub-problems that can be solved in parallel, thereby improving computational efficiency.

[0081] The integrated energy dispatching method provided in this application provides a precise data foundation for optimized dispatching by constructing a refined parameter model for multi-energy units; it incorporates the spatial distribution characteristics of grid carbon flow into dispatching considerations by determining node carbon potential; it then balances traditional economic objectives with emerging low-carbon demands through dual-objective constraint transformation; finally, it adopts a multi-objective optimization algorithm combined with fuzzy decision-making to output a dispatching strategy that takes into account both cost optimization and low-carbon benefits while ensuring solution efficiency, thus solving the industry problem that traditional methods struggle to coordinate the optimization of energy efficiency, cost, and carbon emissions.

[0082] Figure 3 The integrated energy dispatch process provided in this application Figure 2 ,like Figure 3 As shown, in this embodiment... Figure 2 Based on the examples, the specific implementation process of the integrated energy dispatching method is described in detail. The method includes:

[0083] S301. Determine the parameter models corresponding to various energy units in the power network.

[0084] The parametric model is used to determine the energy production efficiency, operating cost, and carbon emission intensity of corresponding types of energy units, including fossil fuel generator units and renewable energy generator units.

[0085] Specifically, in this embodiment, the parameters in the parameter model corresponding to the fossil fuel generator set include the corresponding fuel type, carbon emission intensity, output power, and operating cost; the parameters in the parameter model corresponding to the renewable energy generator set include output power.

[0086] The following provides illustrative examples of the structures of various parametric models:

[0087] Renewable energy units such as wind turbines and photovoltaic generators.

[0088] The parameter model of the wind turbine is as follows: The working principle of a wind turbine is that the wind drives the rotor to rotate, and the speed is increased through the transmission system to reach the generator's rotational speed, thereby driving the generator to generate electricity. The main factor affecting the hourly power generation of a wind turbine is the average wind speed at the turbine shaft, and its output power can be expressed as follows:

[0089] ,

[0090] In the formula, P WT P represents the actual output power of the fan, measured in kW. WT std WT represents the rated power output of the fan (WT and std are not aligned here, but the part where WT and std are aligned in the above formula represents the same parameter; the alignment situation is similar thereafter and will not be explained separately), in kW; v represents the actual wind speed at the fan shaft, in m / s; v in The wind speed at which the fan cuts in, measured in m / s; v out To cut off the fan's wind speed, the unit is m / s, v τ This is the rated wind speed of the fan, expressed in m / s.

[0091] Parametric model of a photovoltaic (PV) generator: Sunlight shines on the PN junction of a semiconductor, generating new electron-hole pairs. These photogenerated carriers move to opposite regions under the drive of the PN junction's electric field, forming current loops. Its output power can be represented as follows:

[0092] ,

[0093] In the formula: P PV std The rated output power of the photovoltaic panel under standard operating conditions is expressed in kW; G AC This represents the actual light intensity, measured in W / m². 2 G std Light intensity under standard conditions, measured in W / m². 2 k is the temperature power correction factor; T C T represents the operating temperature of the photovoltaic panel, in °C; T represents the reference operating temperature of the photovoltaic panel, in °C. amd The ambient temperature is expressed in °C.

[0094] Fossil fuel power generation units, such as gas-fired boiler units and hydrogen fuel cell units.

[0095] The parameter model of a gas-fired boiler unit: A gas-fired boiler unit is a thermal energy device that uses gas as fuel to generate high-temperature gas through combustion, which heats water into steam or heat. Its thermal power mathematical model can be represented by the following:

[0096] ,

[0097] In the formula, P th_GB (t) represents the output thermal power of the gas-fired boiler unit during time period t, in kW; G ng_GB (t) represents the natural gas consumption rate of the gas-fired boiler unit during time period t, in m³ / s. 3 / h;X ng The lower heating value of natural gas is expressed in kJ / m³. 3 η th_GB η is the energy conversion coefficient of the gas-fired boiler unit. th_GB std The rated power of the gas-fired boiler unit is expressed in kW; β GB For the state parameters of the gas-fired boiler unit; P ng_GB min P ng_GB max These represent the minimum and maximum input natural gas power of the gas-fired boiler unit, respectively, in kW; ΔP ng_GB min ΔP ng_GB max These represent the minimum and maximum ramp-up natural gas power of the gas-fired boiler unit, respectively.

[0098] Parametric model of hydrogen fuel cell unit: A hydrogen fuel cell unit is a device that generates electricity through the chemical reaction of hydrogen and oxygen. It is one of the key technologies for hydrogen energy utilization. Taking proton exchange membrane fuel cell as an example, the electrolyte is usually a proton exchange membrane, which is an ion-conducting material that selectively permeates protons. The proton exchange membrane fuel cell catalytically reacts hydrogen and oxygen to produce electricity, heat, and water, with a conversion efficiency of about 60%, and is characterized by high efficiency and environmental friendliness. Its specific working principle is common knowledge in this field and will not be elaborated here.

[0099] Its power generation parameter model is shown below:

[0100] P ele_HFC (t) = η ele_HFC X hg G ng_HFC (t),

[0101] In the formula, P ele_HFC (t) represents the electrical power generated by the hydrogen fuel cell (HFC) during time period t, in kW; G ng_HFC(t) represents the rate at which the HFC consumes hydrogen during time interval t, in meters per second (m). 3 / h;X hg η is the lower heating value of hydrogen, expressed in kJ / h; ele_HFC The power generation efficiency of HFC.

[0102] The parameter model for its heating is shown below:

[0103] P th_HFC (t) = η th_HFC X hg G ng_HFC (t),

[0104] In the formula, P th_HFC (t) represents the thermal output power of the HFC, η th_HFC The heat conversion efficiency of HFC.

[0105] Therefore, the energy generation efficiency of hydrogen fuel cells in heating and power generation can be evaluated separately.

[0106] In addition to the two types of units mentioned above, IES may also include combined heat and power (CHP) units, which are devices that can simultaneously produce electricity and heat. They can drive a generator to output electrical energy through the input fuel and output heat energy through a heat recovery unit. At the same time, the heat generated can also be used in conjunction with the built-in waste heat power generation device to output some electrical energy, thereby improving energy output efficiency.

[0107] The parametric model for the power and heat supply of a combined heat and power (CHP) unit can be:

[0108] ,

[0109] In the formula: P ele_GT (t), P th_GT (t) represents the power generation and heat generation of the combined heat and power unit during time period t, respectively; P ng_GT (t) represents the natural gas input power of the combined heat and power unit during time period t, in kW; η ele_GT η th_GT These refer to the power generation and heat generation efficiencies of a combined heat and power (CHP) unit, respectively.

[0110] In addition to the aforementioned units that directly generate electricity and heat, the equipment in IES also includes energy storage equipment, power-to-gas conversion equipment, carbon capture and storage equipment, etc.

[0111] The parameter model for the relationship between the energy storage device's capacity and charging / discharging power at different time periods is as follows:

[0112] S cap (t + 1) = (1 - α ES )S cap (t) + ηES cha P cha (t)Δt - P dis (t) Δt / η ES dis ,

[0113] In the formula: S cap (t), S cap (t+1) represents the capacity of the energy storage device before the start of the current time period and before the start of the next time period, respectively; P cha (t), P dis (t) represents the charging and discharging power during time period t; η ES cha η ES dis This is represented as a charge / discharge constraint.

[0114] Electricity-to-gas conversion equipment can use electricity generated from wind and solar power to produce hydrogen. The resulting hydrogen can be stored in a hydrogen storage tank in three ways: first, it can be combined with captured carbon dioxide to produce methane, which can then be used for heating; and third, it can be used to generate electricity in a hydrogen fuel cell unit. Therefore, the parameter model for the electro-hydrogen production process can be expressed as follows:

[0115] ,

[0116] P hg_EL (t) represents the electrical power consumed by the power-to-gas conversion equipment during time period t, μ EL (t) is the efficiency function of the power-to-gas conversion equipment for generating hydrogen, P ele_EL Hydrogen power generated by the power-to-gas conversion equipment. Ψ EL (i) represents the polynomial coefficients of the hydrogen production efficiency function of the power-to-gas conversion equipment, n represents the number of pieces in the piecewise function of the hydrogen production efficiency, and P ele_EL std This refers to the rated electrical power consumption of the power-to-gas conversion equipment.

[0117] The parametric model for the hydrogen-to-methane process can be expressed as:

[0118] ,

[0119] In the formula, V CO2_MR (t), V ng_MR (t) represents the volume of carbon dioxide utilized and the volume of methane produced in the methane reactor of the power-to-gas conversion equipment during time period t, respectively; E CO2_MR m represents the mass of carbon dioxide utilized by the methane reactor during time period t; CO2 This represents the mass of carbon dioxide per unit volume.

[0120] Carbon capture and storage equipment includes two stages: carbon capture and carbon sequestration. It absorbs flue gas containing carbon dioxide through an absorbent, and then stores the carbon dioxide in the absorbent in a carbon dioxide storage tank through a de-absorber for sequestration or participation in hydrogen methanation. The absorbent after being treated by the de-absorber can then be used for carbon dioxide absorption.

[0121] Therefore, its total operating energy consumption can be expressed as a parametric model:

[0122] ,

[0123] In the formula: P CCS (t) represents the power consumption of the carbon capture and storage equipment during time period t; P CCS_op (t) represents the power consumption of the carbon capture and storage equipment during carbon capture operation in time period t; P CCS_fixed Fixed power consumption for carbon capture in carbon capture and storage (CFS) equipment; P ele_GT (t), P th_GB (t) represents the electrical power generated by the gas turbine and the thermal power generated by the gas boiler during time period t, respectively; e GT e GB These represent the carbon emission intensity per unit of electricity generated by a gas turbine and the carbon emission intensity per unit of heat generated by a gas-fired boiler, respectively; m CO2 η is the mass of CO2 per unit volume. CCS E represents the capture efficiency of carbon capture and storage (CCS) equipment. CCS (t) represents the amount of carbon dioxide captured by the carbon capture and storage (CFS) equipment during time period t, in kg; λ CCS_op The electrical power consumed by a carbon capture device to capture a unit mass of carbon dioxide, in kWh / kg; This refers to the upper limit of power consumption for the carbon capture process in carbon capture and storage equipment.

[0124] The above parameter model can be used to easily calculate data such as carbon emission intensity of different types of energy units, which facilitates the calculation of subsequent energy dispatch strategies.

[0125] S302. Based on the predetermined corresponding nodes of each energy unit in the power network, determine the output power and carbon emission intensity of each node.

[0126] Specifically, each node in the power network (which can be a physical node, such as a substation, a region, such as a factory load node, or a virtual node, such as a multi-energy hub node containing multiple energy units) can have its corresponding output power and carbon emission intensity calculated based on the energy units it contains, combined with the aforementioned parameter model. That is, the sum of the output power of all energy units in the same node is taken as the output power of that node, and the sum of the carbon emission intensities of all energy units (at which point the amount of carbon capture and storage equipment can absorb can be subtracted from the sum) is used to determine the carbon emission intensity of that node.

[0127] S303. Based on the output power of each node, determine the power distribution and carbon emission flow in the power network where each node is located.

[0128] The carbon emission flow is derived from the carbon emission intensity and the corresponding output power.

[0129] Specifically, since there are topological and coupling relationships between different nodes, the line to which each node belongs in the power network can be obtained. Based on the line, the power distribution in the same line can be determined, and the carbon emission intensity of the nodes on the line can be multiplied by the power flow distribution of the output power (i.e., the distribution of output power) to obtain the carbon emission intensity corresponding to the line.

[0130] S304. Based on the principle of proportional sharing, the carbon emission flow corresponding to each node is allocated to the power network to obtain the carbon potential corresponding to each node.

[0131] The carbon potential is calculated based on the carbon emission streams received by the node and the carbon emission streams generated by the node itself.

[0132] Specifically, the grid carbon emission factor can be represented by the carbon potential of grid nodes, which is determined by the combined effect of the carbon emission flow generated by the energy units connected to the node and the carbon emission flow flowing into the node from other nodes, and can be calculated by the "proportional sharing principle" (a common method for allocating carbon emission responsibility in complex systems).

[0133] Furthermore, the specific calculation method for the carbon potential corresponding to a node may include:

[0134] Step A1: Determine the output power and received carbon emission stream of each node.

[0135] Specifically, based on the topology, the output power of each node and the received carbon emission stream can be obtained.

[0136] Step A2: Determine the carbon emission flow allocation ratio for each node based on the output power distribution ratio of each node.

[0137] Among them, the carbon emission flow distribution ratio is positively correlated with the output power distribution ratio.

[0138] Specifically, since the output power of different nodes on the line varies, the allocation ratio of carbon emission streams needs to be determined according to the principle of proportional sharing, that is, based on the distribution ratio of output power. This allocation method can be expressed as:

[0139] ,

[0140] In the formula: I + e represents the set of nodes that have been detected flowing into this node. G,i P represents the carbon emission intensity of the generating unit connected to this node. G,i This refers to the active power flow injected into this node (i.e., the active power along the line).

[0141] Step A3: Determine the carbon potential of each node based on the total carbon emission flow and carbon emission flow allocation ratio of the line where each node is located.

[0142] Specifically, assuming there are no isolated nodes in the power grid, meaning that any node has at least one connection edge with every other node, the nodal carbon potential can be represented in matrix form as follows:

[0143] ,

[0144] In the formula, η N,i P is an N-dimensional unit row vector, with the i-th dimension being 1 and the other dimensions being 0; B Let P be the branch power flow distribution matrix, which is an N-order square matrix. If there is a positive active power flow P passing through node i to node j, then P B [i, j] = P, P B [j, i] = 0; P G Inject a matrix into the generator set, a K×N matrix, where K is the number of generator sets and P is the number of generator sets. G [k, i] = P represents the active power flow P injected from generator set k to node i; E G [k] represents the carbon emission intensity of generator set k; E N E is the nodal carbon potential vector. N [i] represents the carbon potential of node i, which can be characterized as the node's carbon emission factor.

[0145] Since the active power flow of injected node i is conserved, we can obtain:

[0146] ,

[0147] In the formula, P N = diag(ζ N+K [P B , PG ] T ), diag denotes constructing a diagonal matrix, P N (η N,i ) represents the active power flow into node i, including the power injection from connected nodes and generator sets.

[0148] From the above, we can conclude that:

[0149] η N,i P N (η N,i ) T e i = η N,i (P B T E N + P G T E G ),

[0150] Extending to all dimensions yields:

[0151] P N E N = P B T E N + P G T E G ,

[0152] The rearranged nodal carbon potential vector E N The calculation formula is:

[0153] E N =(P N - P B T ) -1 P G T E G ,

[0154] Therefore, the carbon potential of each node can be calculated.

[0155] S305. Based on the operating costs of each node, determine the cost constraints corresponding to the lowest operating cost.

[0156] Specifically, in practical energy dispatch, the primary consideration must be economic efficiency, i.e., minimizing operating costs. In this case, the constraints can be expressed as:

[0157] F IES = min(f op + f buy + f cost_CO2 + fDR + f cut ),

[0158] The overall operating cost of the power grid is as follows:

[0159] ,

[0160] In the formula, i represents the energy unit equipment, and c op (i) represents the operating cost per unit power of device i, and P(i, t) represents the operating power of device i during time period t.

[0161] Energy purchase cost consists of electricity purchase cost and gas purchase cost, and is calculated as follows:

[0162] ,

[0163] In the formula, P ng_buy P ele_buy These represent the gas purchase and natural gas purchase power during time period t; c ele_price (t) represents the electricity price during time period t, and c ng_price The gas price is calculated based on power consumption (which is usually unaffected by time of day).

[0164] Carbon emission cost f cost_CO2 Including carbon trading costs f cost_CO2_trade And carbon sequestration costs f cost_CO2_save The calculation method is as follows:

[0165] f cost_CO2 = f cost_CO2_trade + f cost_CO2_save ,

[0166] The calculation method for low-carbon incentive response compensation costs is as follows:

[0167] f DR = f shift + f tran + f cut ,

[0168] Among them, f shift To compensate for the cost of changing the load through scheduling, f tran To compensate for the costs of shifting load during scheduling periods, f cut This refers to the compensation cost when reducing load through scheduling.

[0169] The cost of abandoning wind and solar power is calculated as follows:

[0170] ,

[0171] In the formula, P cur_WT (t), P cur_PV(t) represents the curtailment power of wind turbines and photovoltaic units during time period t, in kW.

[0172] S306. Based on the various types of energy units in the power grid, determine the constraints on carbon emissions generated by the power grid.

[0173] Specifically, in integrated energy dispatching, the objective of carbon emission dispatching optimization is to minimize it, and the calculation formula is as follows:

[0174] ,

[0175] In the formula: E IES * E ng_machine * E ng_equ * These are the actual carbon dioxide emissions generated during the dispatch cycle by the equivalent gas load after considering the integrated energy system, gas turbine units (including gas turbines and gas boilers), and demand response (DR, which refers to the proactive adjustment of one's electricity consumption behavior based on price signals or incentives, such as reducing, shifting, or increasing electricity consumption to respond to the needs of grid operation).

[0176] β th * β ng_load * These represent the actual carbon dioxide emissions generated per unit of heat power produced and per unit of gas load consumed; E ele_buy * β represents the indirect carbon emissions from purchasing electricity from the grid, calculated using the grid carbon emission factor. ele_buy * E is the carbon emission factor of the power grid. carbon_save_CCS This refers to the amount of carbon dioxide captured and stored by the carbon capture and storage (CCS) equipment during the scheduling cycle.

[0177] S307. Based on the costs incurred when adjusting the electrical load, determine the corresponding load adjustment constraints.

[0178] Specifically, the aforementioned steps involve the costs of adjusting the electricity load, namely the low-carbon incentive response compensation costs, which will be explained in detail here.

[0179] Electricity load can be divided into adjustable (e.g., enterprise electricity consumption) and non-adjustable (e.g., power supply for various essential living facilities). The adjustable portion includes loads that can be shifted, meaning the power supply time can be changed according to a predetermined plan, allowing the load to be shifted across multiple scheduling periods. Since load shifting affects normal electricity consumption on the demand side, the compensation costs incurred after load shifting need to be considered during the scheduling process.

[0180] ,

[0181] In the formula: f cost_shift The compensation cost for changing loads through scheduling is the load shifting compensation coefficient, expressed in units of cost per kW; P shift (t) represents the power shifted to time period t; T represents the total number of time periods within a scheduling cycle.

[0182] Transferable load refers to the electricity consumption that can be flexibly adjusted across different time periods, but it is necessary to ensure that the total load remains consistent before and after the transfer throughout the entire cycle. Load transfer also incurs compensation costs, which can be expressed as:

[0183] ,

[0184] In the formula: f cost_tran This is the load transfer compensation factor, expressed as the cost per kW.

[0185] Reduceable loads refer to loads that can tolerate a certain degree of interruption, power reduction, or time shortening. Depending on the supply and demand of the power grid system, these loads can be partially or completely reduced. To avoid frequent load reduction responses that could affect users' electricity comfort, constraints need to be placed on the number of reductions and the duration of consecutive reductions.

[0186] The constraint on the number of reductions is as follows:

[0187] In the formula, N cut max This represents the maximum number of reductions that can be made.

[0188] The minimum and maximum consecutive reduction time constraints are:

[0189] ,

[0190] In the formula, T cut min T is the minimum number of consecutive reduction periods. cut max This represents the maximum number of consecutive reduction periods.

[0191] Based on this, the compensation cost for load reduction can be expressed as:

[0192] ,

[0193] In the formula, f cost_cut The power compensation factor is the unit of the reduced load, and the unit is the cost per kW.

[0194] S308. Determine the carbon emission constraints based on cost constraints, carbon emission constraints, and load adjustment constraints.

[0195] Specifically, the combination of various constraints obtained through the aforementioned steps can serve as carbon emission constraints when conducting energy dispatch strategy analysis.

[0196] S309. Output the energy production efficiency, operating cost, carbon potential and carbon emission constraints to the multi-objective optimization algorithm based on the genetic algorithm, and output the corresponding non-dominated solution set.

[0197] Among them, the multi-objective optimization algorithm based on genetic algorithms is the non-dominated sorting genetic algorithm.

[0198] Specifically, common non-dominated sorting genetic algorithms include the NSGA-II algorithm (which stands for Non-dominated Sorting Genetic Algorithm II, or the second-generation non-dominated sorting genetic algorithm).

[0199] The specific processing procedure is as follows:

[0200] Step B1: Based on the input energy production efficiency, operating costs, carbon potential, and carbon emission constraints, create a randomly generated parent population P0 of size N. Evaluate and rank all individuals in the parent population based on the non-dominance principle and assign fitness values. Then, perform selection, crossover, and mutation operations to generate a new generation population Q. Set the population iteration count k to 0.

[0201] Step B2: Develop the population Q generated from k iterations. k and P k By splicing it with its parent generation, a population R of size 2N is obtained. k =Q k ∪P k For population R k Performing an undominated sort yields multiple undominated solution sets L. k .

[0202] Step B3: For all solution sets L k The population is sorted using a crowding comparison method, and the best N individuals are selected to form a new generation population P. k+1 .

[0203] Step B4, for population P k+1 Perform population replication, crossover, and mutation operations to generate a new generation population Q. k+1 .

[0204] Step B5: Check if the termination condition is met. If it is, stop the algorithm; otherwise, increment the iteration count k by 1 and proceed to step B2 to continue execution.

[0205] Therefore, a set of non-dominated solutions containing multiple possible scheduling strategies can be obtained.

[0206] S310. Input the non-dominated solution set into the fuzzy decision algorithm and output the energy scheduling strategy.

[0207] Specifically, based on the received sets of non-dominated solutions, the server can determine the final energy scheduling strategy using a fuzzy decision-making algorithm. The steps include:

[0208] Step C1: Determine the evaluation indexes used to evaluate each non-dominated solution set. The number of evaluation indexes is m.

[0209] Step C2: Determine the evaluation index levels. The number of levels for each index is 0.

[0210] Step C3: Determine the weight vector A=(a1,a2,…,a…) for the evaluation indicators. m ), where ∑a m =1, a m >0.

[0211] Step C4: Perform single-factor fuzzy evaluation. For each non-dominated solution, calculate the membership degree of each indicator level to obtain the fuzzy relation matrix R. R is an m-row, o-column matrix. The membership functions are selected as follows:

[0212] ,

[0213] Among them, f ij This is represented as the membership degree of the index in the i-th row and j-th column.

[0214] Step C5: Multi-index comprehensive evaluation. The comprehensive membership evaluation result matrix B is obtained by performing a dot product operation between the weight vector A and the fuzzy relation matrix R. The optimal solution is the one with the highest membership value in option B.

[0215] Therefore, by combining fuzzy decision-making algorithms and multi-objective optimization functions, the optimal determination of energy dispatch strategies can be achieved.

[0216] After determining the corresponding energy dispatch strategy, the server can adjust the various energy units in the power grid based on the energy dispatch strategy, thereby achieving comprehensive energy dispatch and optimizing carbon emissions and operating costs.

[0217] The integrated energy dispatching method provided in this application constructs a parameter model covering both fossil fuel and renewable energy units, quantifies the energy efficiency, operating costs, and carbon emission intensity of various units, and combines dynamic calculation of node carbon potential with multi-constraint optimization to achieve efficient and low-carbon dispatching of integrated energy systems. It tracks carbon flow distribution by adopting a proportional sharing principle to quantify node carbon emissions. By establishing a multi-objective optimization model that includes economic efficiency, environmental protection, and load adjustment, and then combining genetic algorithms and fuzzy decision-making for collaborative solution, it ultimately outputs a dispatching strategy that balances optimal cost and minimum carbon emissions. This effectively solves the problem of balancing economic efficiency and environmental protection in traditional energy dispatching, significantly improves system operating efficiency, reduces carbon emission intensity, and provides reliable technical support for the optimized dispatching of multi-energy complementary systems.

[0218] Figure 4 A schematic diagram of the integrated energy dispatching device provided in this application is shown below. Figure 4 As shown, the integrated energy dispatching device 400 provided in this embodiment includes:

[0219] The acquisition module 410 is used to determine the parameter models corresponding to various energy units in the power grid. The parameter models are used to determine the energy production efficiency, operating cost and carbon emission intensity of the corresponding type of energy unit. The energy units include fossil fuel generator units and renewable energy generator units.

[0220] Processing module 420 is used to determine the carbon potential of various energy units at corresponding nodes in the power grid based on a parametric model. The carbon potential is determined based on carbon emission intensity.

[0221] Analysis module 430 is used to determine the corresponding carbon emission constraints based on the predetermined scheduling optimization objectives and carbon potential. The scheduling optimization objectives include the lowest operating cost of the power network and the lowest carbon emissions in the power network. The carbon emissions are determined based on the carbon potential.

[0222] The output module 440 is used to output energy production efficiency, operating cost, carbon potential and carbon emission constraints to a multi-objective optimization algorithm based on fuzzy decision-making, and output the corresponding energy dispatch strategy. The energy dispatch strategy is used to schedule the working status of energy units.

[0223] In one possible implementation, the acquisition module 410 specifically includes parameters in the parameter model corresponding to the fossil fuel generator set, including the corresponding fuel type, carbon emission intensity, output power and operating cost; and parameters in the parameter model corresponding to the renewable energy generator set, including output power.

[0224] In one possible implementation, the processing module 420 is specifically used to: determine the output power and carbon emission intensity of each node based on the pre-determined corresponding node of each energy unit in the power network; determine the power distribution and carbon emission flow in the power network line where each node is located based on the output power of each node, wherein the carbon emission flow is obtained based on the carbon emission intensity and the corresponding output power; and allocate the carbon emission flow corresponding to each node to the power network based on the proportional sharing principle to obtain the carbon potential corresponding to each node, wherein the carbon potential is calculated based on the carbon emission flow received by the node and the carbon emission flow generated by the node itself.

[0225] In one possible implementation, the processing module 420 is specifically used to: determine the output power and received carbon emission flow of each node; determine the carbon emission flow allocation ratio of each node based on the output power distribution ratio of each node, wherein the carbon emission flow allocation ratio is positively correlated with the output power distribution ratio; and determine the carbon potential corresponding to each node based on the total carbon emission flow and carbon emission flow allocation ratio of the line to which each node is located.

[0226] In one possible implementation, the analysis module 430 is specifically used to: determine the cost constraint corresponding to the lowest operating cost based on the operating cost of each node; determine the carbon emission constraint generated by the power network based on the various energy unit types in the power network; determine the corresponding load adjustment constraint based on the cost incurred when adjusting the electricity load; and determine the carbon emission constraint based on the cost constraint, carbon emission constraint, and load adjustment constraint.

[0227] In one possible implementation, the output module 440 is specifically used to output energy production efficiency, operating cost, carbon potential and carbon emission constraints to a multi-objective optimization algorithm based on genetic algorithm, and output the corresponding non-dominated solution set; input the non-dominated solution set into a fuzzy decision algorithm, and output an energy scheduling strategy.

[0228] In one possible implementation, the output module 440 specifically includes a non-dominated sorting genetic algorithm as the multi-objective optimization algorithm based on the genetic algorithm.

[0229] The integrated energy dispatching device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0230] Figure 5 A schematic diagram of the control device provided in this application. Figure 5 As shown, the control device 500 provided in this embodiment includes at least one processor 520 and a memory 510. Optionally, the control device 500 further includes a communication component. The processor 520, memory 510, and communication component are connected via a bus 530.

[0231] In a specific implementation, at least one processor 520 executes computer execution instructions stored in memory 510, causing at least one processor 520 to perform the above-described method.

[0232] The specific implementation process of processor 520 can be found in the above method embodiments, and its implementation principle and technical effect are similar, so it will not be repeated here.

[0233] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0234] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0235] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0236] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0237] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0238] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0239] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0240] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0241] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0242] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0243] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0244] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0245] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A comprehensive energy dispatching method, characterized in that, include: Determine the parameter models corresponding to various energy units in the power grid, wherein the parameter models are used to determine the energy production efficiency, operating cost and carbon emission intensity of the corresponding type of energy unit, and the energy units include fossil fuel power generation units and renewable energy units; Based on the parameter model, the carbon potential of various energy units at corresponding nodes in the power grid is determined, and the carbon potential is determined based on the carbon emission intensity. Based on the predetermined scheduling optimization objective and the carbon potential, the corresponding carbon emission constraints are determined. The scheduling optimization objective includes minimizing the operating cost of the power network and minimizing the carbon emissions in the power network. The carbon emissions are determined based on the carbon potential. The energy production efficiency, operating cost, carbon potential, and carbon emission constraints are output to a multi-objective optimization algorithm based on fuzzy decision-making, which outputs a corresponding energy scheduling strategy. The energy scheduling strategy is used to schedule the operating status of the energy units.

2. The method according to claim 1, characterized in that, The parameters in the parameter model corresponding to the fossil fuel generator set include the corresponding fuel type, carbon emission intensity, output power, and operating cost; The parameters in the parameter model corresponding to the renewable energy unit include output power.

3. The method according to claim 1, characterized in that, The determination of the carbon potential of various energy units at corresponding nodes in the power grid based on the parameter model includes: Based on the pre-determined corresponding nodes of each energy unit in the power grid, determine the output power and carbon emission intensity of each node; Based on the output power of each node, the power distribution and carbon emission flow in the power network where each node is located are determined, wherein the carbon emission flow is obtained based on the carbon emission intensity and the corresponding output power; Based on the principle of proportional sharing, the carbon emission flow corresponding to each node is allocated to the power network to obtain the carbon potential corresponding to each node. The carbon potential is calculated based on the carbon emission flow received by the node and the carbon emission flow generated by the node itself.

4. The method according to claim 3, characterized in that, The principle of proportional sharing is used to allocate the carbon emission flow corresponding to each node to the power grid, thereby obtaining the carbon potential corresponding to each node, including: Determine the output power and received carbon emission streams of each node; Based on the output power distribution ratio of each node, the carbon emission flow allocation ratio of each node is determined, and the carbon emission flow allocation ratio is positively correlated with the output power distribution ratio. The carbon potential corresponding to each node is determined based on the total carbon emission flow of the line where each node is located and the carbon emission flow allocation ratio.

5. The method according to claim 4, characterized in that, The process of determining the corresponding carbon emission constraints based on the predetermined scheduling optimization objective and the carbon potential includes: Based on the operating costs of each node, determine the cost constraints corresponding to the lowest operating cost; Based on the various types of energy units in the power grid, determine the carbon emission constraints generated by the power grid. Based on the costs incurred in adjusting the electricity load, the corresponding load adjustment constraints are determined. The carbon emission constraints are determined based on the cost constraints, the carbon emission constraints, and the load adjustment constraints.

6. The method according to any one of claims 1 to 5, characterized in that, The step of outputting the energy production efficiency, operating cost, carbon potential, and carbon emission constraints to a multi-objective optimization algorithm based on fuzzy decision-making, and outputting the corresponding energy dispatch strategy, includes the following steps: The energy production efficiency, operating cost, carbon potential, and carbon emission constraints are output to a multi-objective optimization algorithm based on a genetic algorithm, and the corresponding non-dominated solution set is output. The non-dominated solution set is input into the fuzzy decision algorithm to output the energy scheduling strategy.

7. The method according to claim 6, characterized in that, The multi-objective optimization algorithm based on genetic algorithm is a non-dominated sorting genetic algorithm.

8. A comprehensive energy dispatching device, characterized in that, include: The acquisition module is used to determine the parameter models corresponding to various energy units in the power grid. The parameter models are used to determine the energy production efficiency, operating cost and carbon emission intensity of the corresponding type of energy unit. The energy units include fossil fuel generator units and renewable energy generator units. The processing module is used to determine the carbon potential of various energy units at corresponding nodes in the power grid based on the parameter model, wherein the carbon potential is determined based on the carbon emission intensity. An analysis module is used to determine the corresponding carbon emission constraints based on a predetermined scheduling optimization objective and the carbon potential. The scheduling optimization objective includes minimizing the operating cost of the power network and minimizing the carbon emissions in the power network. The carbon emissions are determined based on the carbon potential. The output module is used to output the energy production efficiency, operating cost, carbon potential and carbon emission constraints to a multi-objective optimization algorithm based on fuzzy decision-making, and output the corresponding energy scheduling strategy, which is used to schedule the working status of the energy unit.

9. A control device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 7.

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