Low-carbon economic operation optimization method for electro-pneumatic coupled distributed interconnected energy systems

Abstract

This invention relates to a method for optimizing the low-carbon economic operation of an electricity-gas coupled distributed interconnected energy system. The method includes: Step S1, constructing a dynamic characteristic model of energy conversion for each energy station based on the dynamic characteristics of each energy station using a data-driven subspace identification method; Step S2, constructing a coupling relationship model between interconnected energy stations based on the dynamic characteristic models of energy station energy conversion using the power balance equations of electricity and gas transmission nodes; Step S3, constructing a carbon emission and carbon trading mechanism for the energy system, and establishing a low-carbon economic operation optimization scheduling model for the electricity-gas coupled distributed interconnected integrated energy system, with the goal of minimizing energy system cost, based on the coupling relationship model between interconnected energy stations, and optimizing the operating parameters of the energy system. Compared with existing technologies, this invention can reduce carbon emissions while improving the overall economic efficiency of the system.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system optimization, and in particular to a method for optimizing the low-carbon and economical operation of an electric-gas coupled distributed interconnected energy system. Background Technology

[0002] How to deeply promote the low-carbon transformation of the energy system and achieve a balance between economic and environmental goals has become an important issue for the development of the energy industry. In terms of carbon dioxide emission reduction measures, using market-regulated economic means is an effective method. Low-carbon integrated energy systems can fully mobilize the flexible resources of each energy link, improving energy conversion efficiency while reducing system carbon emissions, and have become an important strategic direction in the international energy field.

[0003] Within the same region, to meet the cooling, heating, and electrical load demands of different spaces and types of users, and to achieve cascaded energy utilization, multiple distributed combined heat and power (CHP) integrated energy stations are typically planned and designed. As the number of distributed CHP integrated energy stations increases within a region, energy exchange between terminal energy stations becomes crucial for improving the overall stability and operational flexibility of the regional energy supply. Electricity and natural gas, as traditional energy transmission media, offer advantages such as long transmission distances and minimal losses. Therefore, electricity-gas coupled distributed interconnected integrated energy systems that achieve energy exchange between energy stations within the system through electricity and natural gas transmission represent an important future development direction for regional integrated energy systems.

[0004] However, the operation optimization of the electro-gas coupled distributed interconnected integrated energy system is not only affected by the dynamic characteristics of energy conversion of each terminal energy station, but also by the topology and characteristic parameters of the energy transmission medium between stations. Overall, the energy coupling relationship of the system is complex and variable, making operation optimization difficult. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a low-carbon and economical operation optimization method for an electricity-gas coupled distributed interconnected energy system. In terms of describing the system's energy coupling relationship model, compared to previous static relationship models, this method uses a data-driven approach to identify and obtain the dynamic energy conversion characteristics model of each terminal energy station within the system. It also constructs an inter-station coupling relationship model through the power balance equations of the electricity and gas transmission nodes, providing a model foundation for optimizing the system's economical and low-carbon operation. Furthermore, by introducing carbon trading costs into the system operation optimization objective function, the optimization problem can improve the economic efficiency of system operation while reducing carbon dioxide emissions.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] According to a first aspect of the present invention, a method for optimizing the low-carbon and economical operation of an electro-pneumatic coupled distributed interconnected energy system is provided, the method comprising the following steps:

[0008] Step S1: Based on the dynamic characteristics of each energy station in the energy system, construct the energy conversion dynamic characteristic model of each energy station using a data-driven subspace identification method;

[0009] Step S2: Based on the energy conversion dynamic characteristic model of the energy station, construct the coupling relationship model between interconnected energy stations using the power balance equations of the electricity and gas transmission nodes;

[0010] Step S3: Construct a carbon emission and carbon trading mechanism for the energy system, and based on the coupling relationship model between interconnected energy stations, establish a low-carbon economic operation optimization scheduling model for the electricity-gas coupled distributed interconnected integrated energy system with the goal of minimizing energy system cost, and optimize and solve the operating parameters of the energy system.

[0011] Preferably, step S1 specifically involves: based on the dynamic characteristics of each energy station in the energy system, constructing a dynamic characteristic model of energy conversion for each energy station using a data-driven subspace identification method, the mathematical expression of which is:

[0012] X(k+1) = AX(k) + BU(k)

[0013] Y(k)=CX(k)+DU(k)

[0014] In the formula, U is the energy station input vector, including net electrical input power. and net natural gas input power Y is the output vector of the energy station, including the electrical load L. e,i and heat load L h,i X is the system state vector; A, B, C, and D are the parameter matrices of the system model, obtained by the subspace identification method.

[0015] Preferably, the coupling relationship model between interconnected energy stations in step S2 includes an inter-station electrical power and natural gas flow transmission model based on power balance of transmission nodes, specifically:

[0016]

[0017]

[0018] In the formula, S m (k) represents the complex power injected at node m; S mn (k) represents the power flow of all neighboring nodes n in node m, which is summarized into a set N. m ;F m(k) represents the volumetric flow rate injected into node m; F mn (k) represents the linear flow between nodes m and n; R m Let m be the set of neighboring nodes of node m, that is, the nodes connected to node m through pipes.

[0019] Preferably, the power flow S of all adjacent nodes n in node m mn The expression for calculating (k) is:

[0020]

[0021] In the formula, V(k) and θ(k) represent the magnitude and angle of the line node voltage, respectively; y mn b mn These are the series admittance and parallel resistance of the line, respectively. The superscript * indicates the conjugate of the current value, and the superscript sh indicates parallel connection.

[0022] The linear flow F between nodes m and n mn The expression for calculating (k) is:

[0023]

[0024] The pipeline is equipped with a compressor, which is driven by a gas turbine. The gas turbine is modeled with an additional airflow F. com (k):

[0025]

[0026] In the formula, p m p n These represent the pressures upstream and downstream of the pipeline, respectively; k mn k represents the pipeline constant. com p is the compressibility constant. inc The pressure of the compressor;

[0027] Volumetric flow rate corresponds to power flow rate, and the relationship is expressed as follows:

[0028] P g,mn (k)=c GHV F mn (k)

[0029] In the formula, P g,mn (k) represents the power flow of the gas between nodes m and n, F mn (k) represents the linear flow between nodes m and n, c GHV This represents the total calorific value of the fluid.

[0030] Preferably, step S3 specifically involves: establishing a low-carbon economic operation optimization scheduling model for an electricity-gas coupled distributed interconnected integrated energy system, with the goal of minimizing the overall daily operating cost of the system; and solving for the optimal values ​​of electricity and gas purchases, as well as electricity and gas consumption at each energy station.

[0031] Objective function:

[0032]

[0033] In the formula, These represent the unit prices at which energy station i purchases energy from the external power grid and the external natural gas grid, respectively. These represent the power obtained by energy station i from the external power grid and the power obtained by energy station i from external natural gas, respectively. This represents the total carbon trading cost corresponding to the carbon trading mechanism.

[0034] Constraints include load power balance constraints, transmission coupling constraints, node and pressure constraints, external power grid output power, and external gas grid output flow limit constraints.

[0035] Preferably, the total carbon trading cost corresponding to the carbon trading mechanism in step S3 is... The expression is:

[0036]

[0037] In the formula, The price is the unit price for carbon emissions trading. Carbon emissions; The initial quota allocated for the free carbon emission allowance;

[0038] carbon emissions Including carbon emissions from natural gas combustion and carbon emissions from purchased electricity, the expressions are as follows:

[0039]

[0040]

[0041] In the formula, Here, N represents the carbon emissions from natural gas combustion, and N is the number of energy stations. t Let N be the total number of running hours. t =24; This is the amount of natural gas power that energy station i purchases from the gas network at time t; Carbon emission factor of natural gas; Carbon emissions from purchased electricity The baseline emission factor for the power grid; It is the electrical power that energy station i purchases from the grid at time t;

[0042] The initial quota for the allocation of the free carbon emission allowance for:

[0043]

[0044] In the formula, ε e ε is the emission allowance coefficient per unit of electricity; h The emission quota coefficient per unit of heat; L e,i (t),L h,i (t) represents the electrical load and thermal load of energy station i at time t, respectively.

[0045] Preferably, the constraints include load power balance constraints, transmission coupling constraints, node and pressure constraints, external power grid output power constraints, and external gas grid output flow limit constraints, specifically:

[0046] 1) Load power balance constraints:

[0047]

[0048] In the formula, P Le,i P Lh,i These represent the electrical and thermal load power of each energy station, respectively. These represent the electrical and thermal power converted by CHP at each energy station, respectively. These represent the electrical and thermal power losses at each energy station, respectively; P gh,i This indicates the thermal power generated by the combustion of natural gas at each energy station;

[0049] 2) Transmission coupling constraints:

[0050]

[0051]

[0052] In the formula, P mn F represents the power between electrical node m and its adjacent node n; mn F represents the power between natural gas node m and its neighboring node n. com,mn This represents the power of the compressor between natural gas node m and its adjacent node n;

[0053] 3) Node and pressure constraints:

[0054] 0≤V i ≤V i max

[0055]

[0056]

[0057] In the formula, V i V i max These represent the voltage amplitude and upper limit of the voltage amplitude at the electrical node, respectively; p i , These represent the pressure and upper pressure limit of the gas node, respectively; p inc,i This represents the compression variable of the two compressors. Indicates the lower and upper limits of the compression variables of the two compressors;

[0058] 4) External gas network output and external gas network flow restriction constraints:

[0059]

[0060]

[0061] In the formula, The power and power limit that energy station i receives from the external power grid; Let represent the flow rate and upper limit of the external gas network obtained by the i-th energy station.

[0062] Preferably, the simplified expression of the low-carbon economic operation optimization scheduling model of the electro-pneumatic coupled distributed interconnected integrated energy system is:

[0063]

[0064] st

[0065] x(k+1)=f(x(k),u(k))

[0066] g(x(k),u(k))=0

[0067] h(x(k),u(k))≤0

[0068] In the formula, x(k+1) represents the dynamic process of the system, g(·) represents the static and instantaneous relationship of transmission in the system, and the h(·) inequality represents the constraint on the parameters.

[0069] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement any of the methods described above.

[0070] According to a third aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the methods described herein.

[0071] Compared with the prior art, the present invention has the following advantages:

[0072] 1) This invention can consider unified planning for multiple distributed integrated energy stations in a region. By connecting energy transmission pipelines such as electricity and gas, it can optimize the conversion, storage and cascade utilization of energy on a larger scale, realize mutual supply and assistance between stations, and improve overall economic efficiency.

[0073] 2) This invention introduces the carbon emission trading model into the optimized operation model of the electrically coupled distributed interconnected integrated energy system, and constructs a dynamic coupling optimization problem for the low-carbon economic operation of the system. This can improve the economic efficiency of the system operation while effectively reducing the carbon emissions of the system. Attached Figure Description

[0074] Figure 1 This is a schematic diagram of the process of the present invention;

[0075] Figure 2 This is a schematic diagram of the system of the present invention;

[0076] Figure 3 This is a schematic diagram of the time-of-use electricity price and natural gas price for each energy station within the system of this invention;

[0077] Figure 4 This is a schematic diagram illustrating the daily electricity and heat load demand of all distributed energy stations within the system of this invention.

[0078] Figure 5 This is a schematic diagram of the hourly electricity and gas purchase variation curves of the system of the present invention;

[0079] Figure 6 This is a schematic diagram of the power variation curve of the energy station EH1 in the system of the present invention;

[0080] Figure 7 This is a schematic diagram of the natural gas interaction curves between energy stations EH3, EH1, and EH2 in the system of this invention;

[0081] Figure 8 This is a schematic diagram showing the power and gas consumption of the system of the present invention in both interconnected and decoupled modes;

[0082] Figure 9 This is a flowchart of the method of the present invention. Detailed Implementation

[0083] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0084] Example

[0085] like Figure 9As shown in the figure, this embodiment presents a method for optimizing the low-carbon and economical operation of an electro-pneumatic coupled distributed interconnected energy system. The method includes the following steps:

[0086] Step S1: Based on the dynamic characteristics of each energy station in the energy system, a data-driven subspace identification method is used to construct a dynamic characteristic model of energy conversion for each energy station. The mathematical expression is:

[0087] X(k+1) = AX(k) + BU(k)

[0088] Y(k)=CX(k)+DU(k)

[0089] In the formula, U is the energy station input vector, including net electrical input power. and net natural gas input power The unit is MW; Y is the energy station output vector, including the electrical load L. e,i and heat load L h,i The unit is MW; X is the system state vector, where the actual meaning of each state variable is not considered; A, B, C, and D are the parameter matrices of the system model, obtained by the subspace identification method.

[0090] Step S2: Based on the energy conversion dynamic characteristic model of the energy station, a coupling relationship model between interconnected energy stations is constructed using the power balance equations of the electricity and gas transmission nodes. This includes the inter-station electrical power and natural gas flow transmission model based on the power balance of the transmission nodes, specifically:

[0091]

[0092]

[0093] In the formula, S m (k) represents the complex power injected at node m, in MW; S mn (k) represents the power flow of all neighboring nodes n in node m, which is summarized into a set N. m ;F m (k) represents the volumetric flow rate injected into node m, in cubic meters per second (m³). 3 / h;F mn (k) represents the linear flow between nodes m and n, in units of m. 3 / h;R m Let m be the set of neighboring nodes of node m, that is, the nodes connected to node m through pipes;

[0094] The power flow S of all adjacent nodes n in node m mn The expression for calculating (k) is:

[0095]

[0096] In the formula, V(k) and θ(k) represent the voltage magnitude at the line node (kV) and the angle (rad), respectively; y mn b mn These are the series admittance S and the parallel resistance, respectively, in Ω. The superscript * indicates the conjugate of the current value, and the superscript sh indicates parallel connection.

[0097] Wherein, the linear flow F between nodes m and n mn The expression for calculating (k) is:

[0098]

[0099] To maintain a certain pressure, a compressor needs to be added to the pipeline. The compressor is driven by a gas turbine, which is modeled with an additional airflow F. com (k):

[0100]

[0101] In the formula, p m p n These represent the pressures upstream and downstream of the pipeline, respectively, in kPa; k mn k represents the pipeline constant. com p is the compressibility constant. inc The compressor pressure is expressed in kPa; the volumetric flow rate corresponds to the power flow rate, and the relationship is as follows:

[0102] P g,mn (k)=c GHV F mn (k)

[0103] In the formula, P g,mn (k) represents the power flow of the gas between nodes m and n, F mn (k) represents the linear flow between nodes m and n, c GHV This represents the total calorific value of the fluid.

[0104] Step S3: Construct a carbon emission and carbon trading mechanism for the energy system. Based on the coupling relationship model between interconnected energy stations, and with the goal of minimizing energy system costs, establish a low-carbon economic operation optimization scheduling model for an electricity-gas coupled distributed interconnected integrated energy system. Optimize and solve the operating parameters of the energy system to obtain the optimal values ​​for electricity and gas purchases, as well as electricity and gas consumption for each energy station. Specifically:

[0105] Objective function:

[0106]

[0107] In the formula, These represent the unit price at which energy station i purchases energy from the external power grid and the external natural gas grid, respectively, in yuan; These represent the power obtained by energy station i from the external power grid and the power obtained by energy station i from external natural gas, respectively. This represents the total carbon trading cost corresponding to the carbon trading mechanism.

[0108] Constraints include load power balance constraints, transmission coupling constraints, node and pressure constraints, external power grid output power, and external gas grid output flow limit constraints.

[0109] Among them, the total carbon trading cost corresponding to the carbon trading mechanism The expression is:

[0110]

[0111] In the formula, The price is per unit of carbon emissions trading, expressed in yuan. Carbon emissions; Initial allowances allocated for free carbon emission credits; carbon emissions Including carbon emissions from natural gas combustion and carbon emissions from purchased electricity, the expressions are as follows:

[0112]

[0113]

[0114] In the formula, Here, N represents the carbon emissions from natural gas combustion, and N is the number of energy stations. t Let N be the total number of running hours. t =24; This is the natural gas power that energy station i purchases from the gas network at time t, expressed in MW; The carbon emission factor for natural gas is taken as 16.2 × 10⁻⁶ in this embodiment. -6 kg / kJ; Carbon emissions from purchased electricity The baseline emission factor for the power grid is set at 0.8244 kg / kWh in this embodiment. It is the electrical power that energy station i purchases from the grid at time t;

[0115] Initial quotas for the allocation of free carbon emission credits for:

[0116]

[0117] In the formula, ε e ε is the emission allowance coefficient per unit of electricity, which is taken as 0.7567 kg / kWh in this embodiment; h The emission quota coefficient per unit of heat is taken as 0.385 kg / kWh in this embodiment; L e,i (t),Lh,i (t) represents the electrical load and thermal load of energy station i at time t, respectively, in MW.

[0118] Specifically, the constraints include load power balance constraints, transmission coupling constraints, node and pressure constraints, and external grid output power and external gas grid output flow limit constraints, as follows:

[0119] 1) Load power balance constraints:

[0120]

[0121] In the formula, P Le,i P Lh,i These represent the electrical and thermal load power of each energy station, in MW. These represent the electrical and thermal power converted by CHP at each energy station, in MW. These represent the electrical and thermal power losses at each energy station, in MW; P gh,i This indicates the thermal power generated by the combustion of natural gas at each energy station, expressed in MW.

[0122] 2) Transmission coupling constraints:

[0123]

[0124]

[0125] In the formula, P mn F represents the power between electrical node m and its adjacent node n, in MW; mn F represents the power between natural gas node m and its neighboring node n, in MW; com,mn This represents the power of the compressor between natural gas node m and its adjacent node n, expressed in MW.

[0126] 3) Node and pressure constraints:

[0127] 0≤V i ≤V i max

[0128]

[0129]

[0130] In the formula, V i V i max These represent the voltage amplitude and upper limit of the voltage amplitude at the electrical node, respectively, in kV; p i , These represent the pressure and upper pressure limit of the gas node, respectively, in kPa; p inc,i This represents the compression variable of the two compressors. Indicates the lower and upper limits of the compression variables of the two compressors;

[0131] 4) External gas network output and external gas network flow restriction constraints:

[0132]

[0133]

[0134] In the formula, The power and upper limit of the power obtained by energy station i from the external power grid, in MW; Let $\frac{i}{i}$ be the flow rate and upper limit of the flow rate obtained by the i-th energy station from the external gas network, in units of kg / s.

[0135] The simplified expression for the low-carbon economic operation and optimal scheduling model of the electro-gas coupled distributed interconnected integrated energy system is as follows:

[0136]

[0137] st

[0138] x(k+1)=f(x(k),u(k))

[0139] g(x(k),u(k))=0

[0140] h(x(k),u(k))≤0

[0141] In the formula, x(k+1) represents the dynamic process of the system, g(·) represents the static and instantaneous relationship of transmission in the system, and the h(·) inequality represents the constraint on the parameters.

[0142] Next, the method of the present invention will be described in detail with reference to specific application examples.

[0143] like Figure 1 The diagram shows a flowchart of a low-carbon economic operation optimization strategy for an electricity-gas coupled distributed interconnected integrated energy system. Specifically, in system modeling, a data-driven approach is used to construct a dynamic mathematical model of the energy stations, and nodal power balance equations are used to characterize the coupling between interconnected energy stations within the system. In designing the system optimization strategy, carbon emission and carbon trading cost targets are introduced on top of traditional operating cost targets, ultimately constructing a low-carbon economic operation optimization scheduling model for the electricity-gas coupled distributed interconnected integrated energy system.

[0144] like Figure 2As shown in the system diagram of a low-carbon economic operation optimization strategy for an electricity-gas coupled distributed interconnected integrated energy system, energy station EH1 provides integrated energy services such as electricity and heat to residential areas. The electricity and gas purchased from the external power grid G1 and natural gas network N1 are denoted as follows: and MW; Energy station EH2, similar to EH1, provides integrated energy services including electricity and heat to residential areas. The electricity and gas purchased from the external power grid G2 and natural gas network N2 are respectively denoted as MW. and MW; Energy station EH3 provides integrated energy services including electricity and heat to the commercial area, and purchases electricity from the external power grid G1. MW.

[0145] like Figure 3 As shown, the energy prices at each energy station on a typical day are known, with the natural gas price at 2.2 yuan / m³. 3 The price per unit of calorific value is 0.22 yuan / kW·h.

[0146] like Figure 4 As shown, on a typical winter day, the electricity and heat loads of the two residential areas are the same, while the electricity and heat load demands of the commercial area are different from those of the residential areas.

[0147] like Figure 5 As shown, through intraday operational optimization of the system, the optimal results of the hourly power and gas purchase processes for each energy station were obtained while meeting the dynamic demands of each station. The overall power purchase volume of each energy station is relatively low and fluctuates significantly, while the natural gas purchase volume is relatively large compared to the power purchase volume. This is because natural gas prices generally remain at a low level without fluctuation, while electricity prices are high and fluctuate during different time periods. Therefore, during periods of high electricity prices and high electricity load demand, the system primarily meets the electricity load demand by increasing the use of natural gas to generate electricity from CHP.

[0148] like Figure 6 As shown, since energy can be exchanged between energy stations, when an energy station experiences a shortage or surplus of electricity or natural gas, it can seek energy support from other energy stations through transmission lines or pipelines. Figure 5 This refers to the energy consumption, power generation, and surplus electricity available for exchange with other energy stations at energy station EH1. During peak electricity load and price periods from 6:00 AM to 8:00 PM, energy station EH1's consumption of purchased electricity is less than that of cheaper natural gas. Natural gas supply must meet both heat and electricity loads, so natural gas purchases increase significantly during this period, leaving little surplus electricity. Surplus electricity during peak periods can be stored in batteries to prepare for overload conditions or for coordination with other energy stations.

[0149] like Figure 7 As shown, since the system is configured so that energy station EH3 has no external natural gas network connection, its heat load can only be met through interaction with energy stations EH1 and EH2. Energy stations EH1 and EH2 not only need to meet their own electricity and heat load requirements, but any excess energy is also transmitted through natural gas pipelines. The portion of the heat load slightly larger than that of energy station EH3 in the diagram is because the efficiency of the energy conversion equipment is less than 1.

[0150] like Figure 8 As shown, in decoupled mode (i.e., each energy station operates independently), the electricity and gas consumption of each energy station significantly exceeds that in interconnected mode. This is because in interconnected mode, energy can be exchanged between energy stations. If there is excess energy available during operation, it can provide support to other energy stations through interconnection channels, ensuring the full cascade utilization of this energy and preventing significant energy waste. Therefore, the overall electricity and gas purchases are significantly reduced compared to independent operation. In interconnected mode, the system's carbon emissions and costs are greatly reduced compared to independent operation. Specifically, the carbon emissions of the electrically coupled distributed interconnected integrated energy system decrease by 15.29%, carbon trading costs decrease by 15.29%, and energy costs decrease by 38.4%. In summary, the electric-gas coupled distributed interconnected integrated energy system has certain advantages in reducing total carbon emissions. It not only enables reasonable system operation and scheduling but also effectively reduces the system's operating energy costs, realizing the concept of low-carbon economy.

[0151] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0152] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0153] The processing unit executes the various methods and processes described above, such as methods S1-S2. For example, in some embodiments, methods S1-S2 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1-S2 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1-S2 by any other suitable means (e.g., by means of firmware).

[0154] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0155] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0156] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0157] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the low-carbon and economical operation of an electro-pneumatic coupled distributed interconnected energy system, characterized in that, The method includes the following steps: Step S1: Based on the dynamic characteristics of each energy station in the energy system, construct the energy conversion dynamic characteristic model of each energy station using a data-driven subspace identification method; Step S2: Based on the energy conversion dynamic characteristic model of the energy station, construct the coupling relationship model between interconnected energy stations using the power balance equations of the electricity and gas transmission nodes; The coupling relationship model between the interconnected energy stations includes an inter-station electrical power and natural gas flow transmission model based on the power balance of transmission nodes, specifically: In the formula, For nodes m The complex power injected at the point; For nodes m All adjacent nodes n The power flow is summarized into a set. ; To inject into the node m Volumetric flow rate; For nodes m and n Linear flow between; For nodes m The set of adjacent nodes, i.e., connected to the node via a pipe. m Nodes; The node m All adjacent nodes n power flow The calculation expression is: In the formula, These represent the voltage magnitude and angle at the line node, respectively. , These represent the series admittance and parallel resistance of the line, respectively. The superscript * indicates the conjugate of the current value. Indicates parallel connection; The node m and n Linear flow between The calculation expression is: The pipeline is equipped with a compressor, which is driven by a gas turbine. The gas turbine is modeled with an additional airflow. : In the formula, , These represent the pressures upstream and downstream of the pipeline, respectively. Represents pipeline constant; It is the compressibility constant; The pressure of the compressor; Volumetric flow rate corresponds to power flow rate, and the relationship is expressed as follows: In the formula, For nodes m and n The power flow of the gas between them For nodes m and n Line flow between The total calorific value of the fluid; Step S3: Construct a carbon emission and carbon trading mechanism for the energy system, and based on the coupling relationship model between interconnected energy stations, establish a low-carbon economic operation optimization scheduling model for the electricity-gas coupled distributed interconnected integrated energy system with the goal of minimizing energy system cost, and optimize and solve the operating parameters of the energy system.

2. The method for optimizing the low-carbon and economical operation of an electro-pneumatic coupled distributed interconnected energy system according to claim 1, characterized in that, Step S1 specifically involves: based on the dynamic characteristics of each energy station in the energy system, constructing a dynamic energy conversion characteristic model for each energy station using a data-driven subspace identification method. The mathematical expression is: In the formula, The input vector for the energy station includes net electrical input power. and net natural gas input power ; The output vector of the energy station includes electrical load. and heat load ; This is the system state vector; The parameter matrix of the system model is obtained by the subspace identification method.

3. The method for optimizing the low-carbon and economical operation of an electro-pneumatic coupled distributed interconnected energy system according to claim 1, characterized in that, Step S3 specifically involves: establishing a low-carbon economic operation optimization scheduling model for an electricity-gas coupled distributed interconnected integrated energy system with the goal of minimizing the overall daily operating cost of the system; and solving for the optimal values ​​of electricity and gas purchases, as well as electricity and gas consumption at each energy station. Objective function: In the formula, They represent energy stations Unit price when purchasing from external power grids and external natural gas networks; Respectively representing energy stations Power obtained from external power grid, energy station Power obtained from external natural gas This represents the total carbon trading cost corresponding to the carbon trading mechanism. Constraints include load power balance constraints, transmission coupling constraints, node and pressure constraints, external power grid output power, and external gas grid output flow limit constraints.

4. The method for optimizing the low-carbon and economical operation of an electro-pneumatic coupled distributed interconnected energy system according to claim 3, characterized in that, The total carbon trading cost corresponding to the carbon trading mechanism in step S3 The expression is: In the formula, The price is the unit price for carbon emissions trading. Carbon emissions; The initial quota allocated for the free carbon emission allowance; carbon emissions Including carbon emissions from natural gas combustion and carbon emissions from purchased electricity, the expressions are as follows: In the formula, Carbon emissions from natural gas combustion Number of energy stations; For the total running hours, take ; It is an energy station exist t The power of natural gas purchased from the gas network at that time; Carbon emission factor of natural gas; Carbon emissions from purchased electricity The baseline emission factor for the power grid; It is an energy station i exist t The electrical power purchased from the power grid at that time; The initial quota for the allocation of the free carbon emission allowance for: In the formula, Emission quota coefficient per unit of electricity; The emission quota coefficient per unit of heat; Energy stations exist t The electrical and thermal loads at that time.

5. The method for optimizing the low-carbon and economical operation of an electro-pneumatic coupled distributed interconnected energy system according to claim 3, characterized in that, The constraints include load power balance constraints, transmission coupling constraints, node and pressure constraints, external grid output power constraints, and external gas grid output flow limit constraints, specifically: 1) Load power balance constraints: In the formula, These represent the electrical and thermal load power of each energy station, respectively. These represent the electrical and thermal power converted by CHP at each energy station, respectively. These represent the electrical and thermal power losses at each energy station, respectively. This indicates the thermal power generated by the combustion of natural gas at each energy station; 2) Transmission coupling constraints: In the formula, Indicates electrical node m With adjacent nodes n The power between; Indicates natural gas node m With neighboring nodes n The power between; Indicates natural gas node m With neighboring nodes n The power of the compressor between; 3) Node and pressure constraints: In the formula, , These represent the voltage amplitude and upper limit of the voltage amplitude of the electrical node, respectively. , These represent the pressure and upper pressure limit of the gas node, respectively. This represents the compression variable of the two compressors. Indicates the lower and upper limits of the compression variables of the two compressors; 4) External gas network output and external gas network flow restriction constraints: In the formula, For energy station Power obtained from the external power grid, and power limit; For the first i The flow rate and flow limit that an energy station receives from the external gas network.

6. The method for optimizing the low-carbon and economical operation of an electro-pneumatic coupled distributed interconnected energy system according to claim 3, characterized in that, The simplified expression of the low-carbon economic operation optimization scheduling model of the electro-pneumatic coupled distributed interconnected integrated energy system is as follows: In the formula, Represents the dynamic process of the system. Represents the static and instantaneous relationships transmitted in the system. Inequality expressions include restrictions on parameters.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 6.

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