Electrical coupling system optimization scheduling method and system based on carbon emission flow theory

Abstract

The invention relates to an electrical coupling system optimization scheduling method and system based on a carbon emission flow theory, and the method comprises the steps: building an electrical integrated energy system structure model according to the structure parameters of an electrical coupling system; respectively constructing carbon potential calculation models of the electrical system and the natural gas system by combining branch carbon flow density parameters and carbon emission factors on the basis of power distribution and gas flow distribution; constructing an upper-layer system scheduling model by taking the minimum cost sum as a target and considering natural gas system flow constraints; constructing a lower-layer system scheduling model by taking economy and low-carbon optimization of the comprehensive energy park as a target; the natural gas system flow constraint is processed by adopting a mathematical relaxation and linearization method to solve an upper-layer system scheduling model, so that the carbon potential value of each energy supply node is calculated to be used for solving a lower-layer system scheduling model. Compared with the prior art, space tracking and responsibility division of carbon emission are achieved, and the low-carbon operation target of the whole system is achieved while energy supply is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of energy system dispatching technology, and in particular to an optimized dispatching method and system for electrically coupled systems based on carbon emission flow theory. Background Technology

[0002] The low-carbon transformation of energy systems has become a core direction for current research and engineering applications. As a major source of greenhouse gas emissions, the optimization of the power system's operation is crucial for achieving carbon reduction. Meanwhile, natural gas, as a fossil fuel with lower carbon intensity, plays a key role in promoting the construction of integrated energy systems. With the development of new energy technologies such as wind power and energy storage, integrated energy systems coupled with electricity are gradually becoming an important form of future low-carbon energy dispatch.

[0003] Current research has made progress in carbon trading mechanisms, power system optimal dispatch, and integrated energy system modeling. However, most existing integrated energy system optimal dispatch methods treat carbon emissions as a post-processing analysis result, failing to effectively allocate and track carbon emission responsibilities in the integrated energy system network in real time, thus limiting the fine-grained control of carbon emissions. Furthermore, some studies neglect the responsiveness of demand-side users in the carbon reduction process, making it difficult to effectively leverage users' carbon reduction potential.

[0004] Carbon emission flow theory offers a solution to the aforementioned problems. This theory can track the transmission paths of carbon emissions in energy networks and effectively allocate carbon emission responsibility to the loads of each node, facilitating quantitative assessment and dynamic control of carbon emissions. However, a comprehensive model that combines carbon emission flow theory, electricity-gas system coupling, and low-carbon economic coordinated optimal scheduling is currently lacking. Most existing low-carbon economic optimal scheduling schemes neglect the system's carbon potential linkage and demand response mechanisms, limiting the system's carbon reduction potential and economic improvement space.

[0005] Therefore, there is an urgent need for a two-tiered optimization scheduling method that comprehensively considers carbon emission allocation, synergy of the integrated electricity-gas energy system, and optimization of the low-carbon economy at the park level, in order to promote the low-carbon and efficient operation of the integrated energy system. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technologies, which mostly neglect the carbon potential linkage and demand response mechanism of the system, thus limiting the carbon emission reduction potential and economic improvement space of the system, and to provide an optimized scheduling method and system for electrically coupled systems based on carbon emission flow theory.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] An optimized scheduling method for an electrical coupling system based on carbon emission flow theory includes: establishing a structural model of an integrated electrical energy system according to the structural parameters of the electrical coupling system. The upper-level system of this model consists of an electrical system and a natural gas system, and power flow is associated at each node, introducing branch carbon flow density parameters; the lower-level system is a single integrated energy park, which is connected to the upper-level system through electricity and gas purchase.

[0009] Based on power distribution and airflow distribution, carbon potential calculation models for the electrical system and the natural gas system are constructed by combining the branch carbon flow density parameters and carbon emission factors, respectively.

[0010] The upper-level system scheduling model is constructed with the goal of minimizing the total cost and taking into account the flow constraints of the natural gas system. The lower-level system scheduling model is constructed with the goal of optimizing the economy and low carbon emissions of the integrated energy park. Mathematical relaxation and linearization methods are used to process the flow constraints of the natural gas system in order to solve the upper-level system scheduling model, thereby calculating the carbon potential value of each energy supply node, which is used to solve the lower-level system scheduling model to prioritize the use of renewable energy.

[0011] Furthermore, the carbon potential calculation model expression for the electrical system is as follows:

[0012]

[0013] In the formula, θ i P represents the carbon potential of node i, which receives power from multiple branches. l Inject power into branch l, ρ l For branch carbon flux density, P g For the output of the node generator set, λ g Let G be the carbon emission factor of the generator set, in(i) be the set of branches injected into node i, and G be the carbon emission factor of the generator set. i This is the set of thermal power units connected to node i.

[0014] Furthermore, the carbon potential calculation model expression for the natural gas system is as follows:

[0015]

[0016] In the formula, θ j Let Q be the carbon potential at node j. m ρ represents the gas flow rate injected into this node. m Q represents the carbon flux density of the corresponding pipeline; w λ represents the local gas well production. w Let W be the carbon emission factor per unit gas volume of the gas well, in(j) be the set of pipes for injection node j, and W be the carbon emission factor per unit gas volume of the gas well. j This is the set of gas wells connected to node j.

[0017] Furthermore, the objective function expression of the upper-level system scheduling model is:

[0018]

[0019] In the formula, P i Let a represent the output of the i-th thermal power unit. i b i and c i Q is the fuel cost coefficient for this unit; j p represents the gas production of the j-th gas well. j The unit gas price.

[0020] Furthermore, the constraints of the upper-layer system scheduling model include:

[0021] Pipeline flow equation constraints are used to describe the nonlinear functional relationship between gas flow rate and pressure at both ends of a pipeline.

[0022] Node pressure range constraints are used to ensure the safe operation of the gas network within the specified upper and lower pressure limits;

[0023] Gas well production upper and lower limit constraints are used to impose boundary constraints on the production capacity of each gas well.

[0024] Furthermore, after optimizing the energy supply strategy of the upper-level system scheduling model, the method calculates the carbon potential value of each energy supply node in the electrical system and natural gas system based on the carbon potential calculation model, and then transmits it to the lower-level system scheduling model.

[0025] Furthermore, the objective function expression of the lower-level system scheduling model is:

[0026] minC total =C purchase +C OM +C carbon

[0027]

[0028] In the formula, C total For comprehensive operating costs, C purchase For the cost of purchasing electricity and gas, C OM For the operation and maintenance costs of equipment within the park, C carbon The carbon trading cost corresponding to the carbon emissions of the park. and P represents the electricity and gas prices at time t; t grid and This indicates the amount of electricity and gas purchased; P represents the maintenance cost coefficient of device i; i,tθ represents the operating power of device i at time t; t Indicates the carbon trading price; The total carbon emissions at time t are determined by the carbon potential feedback from the upper-level system scheduling model and the local energy consumption. T represents the set of devices participating in the scheduling; T represents the set of scheduling cycles.

[0029] Furthermore, the constraints of the lower-level system scheduling model include:

[0030] Wind turbine operating constraints are used to ensure that the wind turbine output does not exceed its maximum predicted output.

[0031] Gas turbine operation and energy conversion constraints are used to constrain the relationship between gas turbine output, gas consumption and calorific value in combination with the characteristics of combined heat and power.

[0032] Energy storage system charging and discharging and energy capacity constraints are used to constrain the charging and discharging power, energy state and efficiency of the energy storage system.

[0033] Power balance constraints are used to ensure that the electricity and heat load requirements of the park are met at each scheduling moment.

[0034] Furthermore, the solution process for the upper-level system scheduling model and the lower-level system scheduling model is as follows:

[0035] The Lagrangian function of the lower-level system scheduling model is constructed. Based on the KKT complementary relaxation conditions of the lower-level system scheduling model, the lower-level system scheduling model is transformed into the constraint conditions of the upper-level system scheduling model. Then, the Big-M method is used to linearize the nonlinear terms in the transformed single-level nonlinear model, forming a single-level mixed integer linear programming problem, which is then solved by a solver.

[0036] The present invention also relates to an optimized scheduling system for an electrical coupling system based on carbon emission flow theory, comprising a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the method described above.

[0037] Compared with the prior art, the present invention has the following advantages:

[0038] (1) This invention proposes to dynamically track the carbon emission flow of each node based on the carbon emission flow theory, using node power injection and equipment carbon emission factor, and calculate carbon potential to measure carbon emission intensity, thereby realizing spatial tracking and responsibility division of carbon emissions. This provides a spatial reference for carbon emission intensity for the two-layer optimization scheduling model, enabling the lower-level park to formulate the optimal energy purchase strategy based on the carbon potential signal transmitted from the upper level, thereby achieving the overall low-carbon operation goal of the system while ensuring energy supply.

[0039] (2) In order to achieve economic and efficient operation of the power-gas system on the energy supply side, this invention constructs an upper-level scheduling model for joint optimization of the power system and the natural gas system, based on the carbon emission flow theory. According to the node carbon potential information fed back by the upper-level scheduling model, the lower-level integrated energy park constructs an optimized scheduling model with the goal of optimal economy and low carbon performance. Overall, it can effectively improve the operational economy and carbon emission reduction capability of the integrated energy system and promote the low-carbon and efficient operation of the integrated energy system. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating an optimized scheduling method for an electrical coupling system based on carbon emission flow theory, provided in an embodiment of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0042] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0043] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0044] Example 1

[0045] like Figure 1 As shown, this embodiment provides an optimized scheduling method for an electrically coupled system based on carbon emission flow theory, including the following steps:

[0046] S1: Based on the structural parameters of the electrical coupling system, establish a structural model of the electrical integrated energy system. The upper system of this model consists of an electrical system and a natural gas system, and the lower system consists of a single integrated energy park.

[0047] S2: Based on the carbon emission flow theory, a carbon potential calculation model for electrical and natural gas systems is constructed. This carbon potential calculation model uses nodal power injection and equipment carbon emission factors to dynamically track the carbon emission flow of each node and calculate the carbon potential to measure the carbon emission intensity.

[0048] S3: Construct an upper-level system scheduling model with the goal of minimizing the sum of fuel costs and natural gas costs for thermal power units;

[0049] S4: To optimize the economy and low carbon emissions of the integrated energy park, a lower-level system scheduling model is constructed. Based on the solution results of the upper-level system scheduling model, the carbon potential value of each energy supply node is calculated using the carbon potential calculation model, which is then used for the optimization solution of the lower-level system scheduling model.

[0050] S5: By using the KKT conditions and complementary relaxation relations of the lower-level model, the two-level model is equivalently transformed into a single-level mixed integer nonlinear programming problem. After linearization using the Big-M method, the optimal system scheduling scheme is solved using the CPLEX optimizer.

[0051] Specifically, step S1 includes the following sub-steps:

[0052] S11: Establishment of the Electrical Upper-Level System Structure Model: The upper-level system consists of the power system and the natural gas system, forming the core network for energy supply and carbon emission control. In this invention, the power system mainly includes the distribution network, thermal power units, and transmission lines, while the natural gas system includes natural gas wells, pipeline networks, and gas user nodes. The establishment of this system model aims to describe the paths and characteristics of energy and carbon emission transmission from the supply side to downstream.

[0053] In this system, distribution network nodes are numbered according to topological relationships, connecting generator units and user loads to form a node-branch structure. Thermal power units, as the main power source, have their output constrained by fuel costs, upper and lower output limits, and unit carbon emission intensity. The natural gas system supplies gas downstream from gas wells, with gas flow transmitted through a pipeline network. Its flow is controlled by gas pressure differentials and must satisfy the gas flow equation and node pressure constraints.

[0054] To accommodate carbon emission flow modeling, each node in the upper-level system needs to be associated with either a power flow or a gas flow, and branch carbon flow density parameters need to be introduced as the basis for calculating the carbon potential. During system operation, the flow of electricity and gas not only affects the energy supply path but also directly determines the distribution of carbon emissions within the network.

[0055] S12: Establishment of the Integrated Energy Park Structural Model: The lower-level system is a single integrated energy park, which is the main energy consumption side and also has local power generation and regulation capabilities. In this invention, the park is equipped with various energy devices, specifically including wind turbines, gas turbines, electric energy storage devices, electric heat pumps, and thermal energy storage systems. The park also has independent electrical and thermal load demands, making it an important target for optimizing load response and carbon emission reduction.

[0056] Wind turbines provide renewable electricity, but their output is uncertain; gas turbines can provide electricity and heat by burning natural gas, with relatively low carbon emission intensity; electric energy storage devices are used to store electricity during off-peak hours and release it during peak hours, achieving peak shaving and valley filling and system regulation; electric heat pumps and thermal storage devices work together to improve energy utilization efficiency while meeting heat load requirements.

[0057] Integrated energy parks connect to the upper-level system through electricity and gas purchases, and possess self-consumption and dispatch response capabilities. Park dispatch will be affected by electricity prices, gas prices, equipment operation and maintenance costs, and carbon potential signals. Therefore, its model needs to comprehensively describe the operating constraints, power balance, and carbon emission response mechanisms of multiple types of equipment to provide accurate support for lower-level optimized dispatch and carbon emission reduction strategies.

[0058] Step S2 specifically includes the following sub-steps:

[0059] S21: Power System Carbon Emission Flow Modeling and Nodal Carbon Potential Calculation

[0060] To achieve accurate quantification and transmission path tracking of carbon emissions in the power system, a carbon flow model in the power grid is first established based on carbon emission flow theory. Carbon emissions in the power system mainly originate from thermal power units, and the carbon emissions per unit output are called carbon emission intensity. These carbon emissions propagate along the power flow path in the distribution network and are transmitted to each load node through branch sharing.

[0061] This step uses a proportional allocation method to establish the carbon flux density model of the power grid branches, that is, to reasonably distribute the carbon emissions of the generating units to all paths in the network according to the proportion of power injected into the branches. Suppose a node i receives power from multiple branches, its carbon potential can be calculated by the following formula:

[0062]

[0063] In the formula, θ i P represents the carbon potential of node i, which receives power from multiple branches. l Inject power into branch l, ρ l For branch carbon flux density, P g For the output of the node generator set, λ g Let G be the carbon emission factor of the generator set, in(i) be the set of branches injected into node i, and G be the carbon emission factor of the generator set. i This is the set of thermal power units connected to node i.

[0064] This method can reflect the impact of power grid structure, power supply structure and power distribution on the carbon emission intensity of each node in real time, and can be dynamically updated according to scheduling strategies or load changes to ensure that the carbon potential of the nodes has timeliness and spatial resolution.

[0065] S22: Carbon Emission Flow Modeling and Nodal Carbon Potential Calculation for Natural Gas Systems

[0066] As a crucial component of the energy supply network, the carbon emissions from natural gas systems primarily originate from the gas production process at gas wells and the combustion process at terminal gas-fired equipment. To accurately measure the carbon emission intensity from natural gas flow, carbon emission flow theory is employed to model the gas network. First, a gas flow distribution and carbon potential transmission model for the natural gas pipeline is established. The Weymouth equation is used to describe the gas flow process within the pipeline, and the flow direction is determined based on the pressure difference. A pipeline carbon flow model is then constructed based on this. Carbon emissions are transferred from the source end (gas well) to the load end nodes according to the pipeline flow rate. Combining the pipeline network topology and node flow distribution, the carbon potential level of each gas node is calculated. Let the carbon potential of a gas node j be θ. j Then its calculation expression is:

[0067]

[0068] In the formula, θ j Let Q be the carbon potential at node j. m ρ represents the gas flow rate injected into this node. m Q represents the carbon flux density of the corresponding pipeline; w λ represents the local gas well production. w Let W be the carbon emission factor per unit gas volume of the gas well, in(j) be the set of pipes for injection node j, and W be the carbon emission factor per unit gas volume of the gas well. j This represents the set of gas wells connected to node j. The carbon potential calculation of the natural gas system also has dynamic update capabilities in the time dimension, and can be adjusted in real time by combining the gas network operation status, pipeline flow fluctuations and gas well extraction strategies, so as to achieve fine modeling and response optimization of carbon emission intensity.

[0069] In summary, the nodal carbon potential calculation model for the power system and the natural gas system provides a spatial reference for carbon emission intensity for the two-level optimal scheduling model, enabling the lower-level parks to formulate the optimal energy purchase strategy based on the carbon potential signal transmitted from the upper level, thereby achieving the overall low-carbon operation goal of the system while ensuring energy supply.

[0070] Step S3 specifically includes the following steps:

[0071] S31: Constructing the upper-level system scheduling model:

[0072] To achieve economical and efficient operation of the power-gas system on the energy supply side, a high-level dispatch model for joint optimization of the power and natural gas systems is constructed based on carbon emission flow theory. This model aims to minimize the sum of fuel costs for thermal power units and natural gas costs. By optimizing generator output and gas well production, it forms an overall energy supply strategy for the power-gas system and calculates carbon potential information, which is then transmitted as an input signal to the lower-level integrated energy park to achieve coordinated optimization dispatch between the upper and lower levels. The model uses minimizing system-level energy supply costs as its objective function and specifically includes two parts:

[0073] The fuel cost of thermal power units is calculated by considering the quadratic relationship between their output and fuel cost; the gas production cost of natural gas wells is calculated by multiplying the unit gas production price by the gas production volume.

[0074] The objective function expression is as follows:

[0075]

[0076] In the formula, P i Let a represent the output of the i-th thermal power unit. i b i and c i Q is the fuel cost coefficient for this unit; j p represents the gas production of the j-th gas well. j The unit gas price.

[0077] S32: Constructing flow constraints for the natural gas system:

[0078] The gas flow process in the natural gas system is modeled using a simplified Weymouth model, with the following constraints: pipeline flow equation constraint: describing the nonlinear functional relationship between gas flow rate and pressure at both ends of the pipeline; node pressure range constraint: ensuring the safe operation of the gas network within specified pressure limits; gas well production limits constraint: imposing boundary constraints on the production capacity of each gas well. Since the above gas flow model contains multiple nonlinear constraint terms, to improve the solution efficiency of the model, the second-order cone relaxation method or Big-M method is used to linearize it, thereby transforming it into a solvable optimization subproblem.

[0079] After optimizing the energy supply strategy of the upper-level system, the carbon potential value of each energy supply node in the power grid and gas network is calculated based on carbon emission flow theory, i.e., the carbon emission intensity per unit of energy. This carbon potential will be transmitted as an important input parameter to the lower-level integrated energy park for its carbon-sensing energy purchase decision-making, realizing regional-level low-carbon load response control. The upper-level scheduling model contains continuous decision variables (such as unit output and gas well production) and nonlinear constraints (such as gas flow equations), belonging to a non-convex optimization problem with high solution complexity. To this end, this invention proposes the following solution strategy: using mathematical relaxation and linearization methods to process the nonlinear constraint terms, and using a commercial solver (CPLEX) in conjunction with mathematical modeling tools (such as YALMIP) to solve the problem, ensuring convergence stability between the global optimum and feasible solutions.

[0080] By constructing and optimizing the above-mentioned upper-level scheduling model, the optimal energy supply strategy of the electric-gas system can be formed under the premise of satisfying the energy supply and demand balance and safety constraints, providing basic support for the low-carbon response and economic operation of the lower-level system.

[0081] Step S4 specifically includes the following sub-steps:

[0082] S41: Construct the optimization objective function for the lower-level integrated energy park scheduling model:

[0083] Based on the node carbon potential information fed back from the upper-level scheduling model, the lower-level integrated energy park constructs an optimal scheduling model with the goal of maximizing both economic efficiency and low carbon emissions. This model aims to minimize the park's overall operating costs within the scheduling cycle. Operating costs include: electricity purchase costs from the grid, gas purchase costs from the gas grid, operation and maintenance costs of equipment within the park (such as wind turbines, gas turbines, and energy storage devices), and carbon trading costs corresponding to the park's carbon emissions (when a carbon trading mechanism is introduced). The objective function is as follows:

[0084] minC total =C purchase +C OM +C carbon (4)

[0085]

[0086] In the formula, C total For comprehensive operating costs, C purchase For the cost of purchasing electricity and gas, C OM For the operation and maintenance costs of equipment within the park, C carbon The carbon trading cost corresponding to the carbon emissions of the park. and P represents the electricity and gas prices at time t; t grid and This indicates the amount of electricity and gas purchased; P represents the maintenance cost coefficient of device i; i,t θ represents the operating power of device i at time t; t Indicates the carbon trading price; The total carbon emissions at time t are determined by the carbon potential feedback from the upper-level system scheduling model and the local energy consumption. T represents the set of devices participating in the scheduling; T represents the set of scheduling cycles.

[0087] S42: Establish constraints for the operation and energy balance of integrated energy equipment.

[0088] Based on the characteristics of various energy devices within the park, the operational constraints of the scheduling model are constructed, mainly including:

[0089] 1. Wind turbine operating constraints

[0090] To ensure that the wind turbine output does not exceed its maximum predicted output, the fluctuations caused by wind speed variations must be taken into account.

[0091]

[0092] In the formula, P w,t and These represent the wind turbine's output and maximum output at time t, respectively.

[0093] 2. Constraints on Gas Turbine Operation and Energy Conversion

[0094] Considering the characteristics of combined heat and power (CHP), the relationship between gas turbine output, gas consumption, and calorific value is constrained:

[0095]

[0096] In the formula, and Let η be the minimum and maximum heat output of the gas turbine at time t, respectively; h,t V represents the heat production efficiency of the gas turbine. GT,t q represents the gas consumption of the gas turbine. g This refers to the calorific value of natural gas.

[0097] 3. Energy storage system charging and discharging and energy capacity constraints

[0098] The charge / discharge power, state of energy, and efficiency of the confined electric energy storage system are shown below:

[0099]

[0100] In the formula, P t ch and P t dis Let be the power of energy storage charging and discharging at time t, respectively. and These represent the 0-1 variables of the energy storage device's charge and discharge states, E t Let E be the remaining energy of the energy storage device at time t. max and E min These represent the maximum and minimum storage capacities of the energy storage device, η. ch and η dis These refer to the energy conversion efficiency of energy storage charging and discharging.

[0101] 4. Power balance constraints

[0102] At every scheduling moment, the park's electricity and heat load requirements are met:

[0103]

[0104] In the formula, P load,t H represents the total load demand of the park. load,t V represents the user's heat load demand. GT,t V represents the amount of gas consumed by the gas turbine at time t. gas,t This indicates the amount of gas supplied.

[0105] Through the above constraints, the model ensures the feasibility and operational safety of the scheduling solution, and, combined with the upper-level carbon potential, guides the park to prioritize the use of renewable energy to reduce carbon emissions and transaction costs.

[0106] In this embodiment, the specific implementation process of step S5 is as follows:

[0107] In the aforementioned two-level model, the upper-level model includes integer and continuous variables and contains nonlinear constraints, while the lower-level model is a mixed-integer linear programming problem. The upper and lower-level models are coupled, making direct solution difficult. By constructing the Lagrangian function of the lower-level model and applying the KKT complementary relaxation conditions, the lower-level model is transformed into constraints for the upper-level model. Then, the Big-M method is used to linearize the nonlinear terms in the transformed single-level nonlinear model, resulting in a single-level mixed-integer linear programming problem.

[0108] Solve mixed-integer linear programming problems in Matlab using the commercial solver CPLEX 12.8 and the YALMIP toolbox.

[0109] Example 2

[0110] This embodiment provides an electrical coupling system optimization scheduling system based on carbon emission flow theory, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the electrical coupling system optimization scheduling method based on carbon emission flow theory as described in Embodiment 1.

[0111] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0112] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0116] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimal scheduling of an electrically coupled system based on carbon emission flow theory, characterized in that, The method comprises the following steps: According to the structural parameters of the electrical coupling system, a structural model of the electrical integrated energy system is established, the upper system of the model is composed of an electrical system and a natural gas system, and a power flow is associated at each node, and a branch carbon flow density parameter is introduced; The lower system is a single integrated energy park, which is connected to the upper system through power purchase and gas purchase; Based on power distribution and gas flow distribution, the carbon potential calculation model of the electrical system and the natural gas system is respectively constructed by combining the branch carbon flow density parameter and the carbon emission factor; The upper system scheduling model is constructed with the minimum sum of costs as the target and considering the flow constraints of the natural gas system; the lower system scheduling model is constructed with the optimal economy and low carbon of the integrated energy park as the target; the flow constraints of the natural gas system are processed by using the mathematical relaxation and linearization method to solve the upper system scheduling model, so as to calculate the carbon potential values of each energy supply node, which are used to solve the lower system scheduling model to preferentially use renewable energy.

2. The method of claim 1, wherein, The expression of the carbon potential calculation model of the electrical system is: where θ i represents the carbon potential of node i, which receives power from multiple branches, P l represents the power injected into branch l, p l represents the carbon flow density of branch, p g represents the output of the generator set at node, l g represents the carbon emission factor of the generator set, in(i) represents the set of branches injected into node i, G i represents the set of thermal power generator sets connected to node i.

3. The method of claim 1, wherein, The expression of the carbon potential calculation model of the natural gas system is: where θ j is the carbon potential of node j, Q m represents the pipeline gas flow injected into this node, ρ m is the carbon flow density of the corresponding pipeline; Q w is the local gas well gas production, λ w is the carbon emission factor per unit of gas production, in(j) is the set of pipelines injected into node j, W j is the set of gas wells connected to node j.

4. The method of claim 1, wherein, The expression of the objective function of the upper system scheduling model is: where P i represents the output of the i th thermal power unit, a i , b i and c i are the fuel cost coefficients of the unit. Q j represents the gas production of the jth gas well, p j is the unit gas price thereof.

5. The method of claim 1, wherein, The constraint conditions of the upper system scheduling model comprise: The pipeline flow equation constraint is used to describe the nonlinear function relationship between the gas flow in the pipeline and the pressure at both ends; The node pressure range constraint is used to ensure the safe operation of the gas network within the specified pressure upper and lower limits; The gas well gas production upper and lower limit constraint is used to implement boundary constraints on the production capacity of each gas well.

6. The method of claim 1, wherein, After the optimization solution of the energy supply strategy of the upper system scheduling model is completed, the carbon potential values of each energy supply node in the electrical system and the natural gas system are calculated based on the carbon potential calculation model and are transmitted to the lower system scheduling model.

7. The method of claim 1, wherein, The expression of the objective function of the lower system scheduling model is: minC total = C purchase + C OM + C carbon In the formula, C total C is the comprehensive operation cost purchase C is the electricity and gas purchase cost OM C is the operation and maintenance cost of equipment in the park carbon C is the carbon trading cost corresponding to the carbon emissions of the park And P represents the electricity price and gas price at time t; P t grid And P represents the electricity purchase quantity and gas purchase quantity; P represents the operation and maintenance cost coefficient of equipment i; P i,t P represents the operation power of equipment i at time t; θ t P represents the carbon trading price; P represents the total carbon emission at time t, which is determined by the carbon potential feedback of the upper system scheduling model and the local energy consumption; P represents the equipment set participating in scheduling; T represents the scheduling period set.

8. The method of claim 1, wherein, The constraint conditions of the lower system scheduling model comprise: The wind turbine operation constraint is used to constrain the wind turbine output not to exceed the maximum predicted output; The gas turbine operation and energy conversion constraint is used to constrain the relationship among the gas turbine output, gas consumption and heat value in combination with the combined heat and power characteristics; The energy storage system charging and discharging and energy capacity constraint is used to constrain the charging and discharging power, energy state and efficiency of the electric energy storage system; The power balance constraint is used to constrain the satisfaction of the electrical and thermal load demand of the park at each scheduling time.

9. The method of claim 1, wherein The solving process of the upper system scheduling model and the lower system scheduling model is as follows: The Lagrange function of the lower system scheduling model is constructed, the lower system scheduling model is converted into the constraint conditions of the upper system scheduling model based on the KKT complementary relaxation condition of the lower system scheduling model, the nonlinear terms in the converted single-layer nonlinear model are linearized by using the Big-M method to form a single-layer mixed integer linear programming problem, and then the problem is solved by using a solver.

10. An electrical coupling system optimal scheduling system based on carbon emission flow theory, characterized in that, The method comprises a memory and a processor, the memory stores a computer program, and the processor calls the computer program to execute the steps of the method according to any one of claims 1-9.

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