A station network low-carbon interaction optimization method considering internal dynamic carbon potential of GCES

Abstract

The present application relates to the technical field of integrated energy system application, and relates to a station-network low-carbon interaction optimization method considering internal dynamic carbon potential of GCES, comprising: based on the correlation analysis of carbon-energy coupling relationship between each device in GCES, defining that the internal GCES is a complete electric-thermal coupling network; using Newton-Raphson method to iteratively solve the constructed power-hydropower-thermal equation, obtaining the power flow calculation result of the electric-thermal coupling network; comprehensively considering the influence of energy storage device on the internal node dynamic carbon potential of GCES, calculating the node dynamic carbon potential of the electric-thermal coupling network based on the power flow calculation result of the electric-thermal coupling network; taking the output of the device in GCES as the optimization variable, considering the influence of time-of-use electricity price and the node dynamic carbon potential of the electric-thermal coupling network, constructing a station-network low-carbon interaction optimization model to optimize the operation strategy of GCES. Compared with the prior art, the present application has the advantages of considering the influence of dynamic carbon potential on carbon emission, realizing the lowest carbon emission and the lowest economic cost, etc.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system application technology, and in particular to a method for optimizing low-carbon interaction between power stations and grids that takes into account the dynamic carbon potential within GCES. Background Technology

[0002] With the continuous development of new power systems, the power grid needs more flexible and adjustable load resources to participate in supply and demand regulation. Grid-connected Combined Cooling, Heating and Power Energy Stations (GCES) incorporate various energy storage resources, serving as comprehensive regional energy supply centers that integrate power generation, heating, and cooling, and are connected to the urban power grid. GCESs can achieve multi-energy complementarity and optimized scheduling in the coordinated supply of energy across multiple processes, including power generation, heating, and cooling. GCESs represent a potential high-quality, flexible, and adjustable load resource for the power grid, effectively promoting the development and utilization of clean energy and distributed energy, optimizing the spatial and temporal distribution of electricity production and consumption, facilitating industrial restructuring and changes in consumption concepts, and providing conditions for efficient energy conversion and energy conservation and carbon reduction in the power system.

[0003] As the proportion of integrated energy systems participating in grid supply and demand interaction gradually increases, and the demand for efficient energy utilization and low carbon emissions continues to rise, energy conservation and carbon reduction methods for integrated energy systems, as well as carbon flow tracing, have become research hotspots. For integrated energy systems containing energy storage devices, the energy consumption and production forms of each device are highly diverse, and the carbon flow within the system is complex and variable. The main purpose of studying the refined calculation of carbon energy flow distribution and dynamic carbon potential within integrated energy systems is to construct a carbon energy flow model through the dynamic coupling and correlation characteristics of energy flow and carbon flow within the integrated energy system, and then study the dynamic carbon potential measurement method of its internal energy supply network, so as to support the supply and demand balance of the regional power grid while achieving energy conservation and carbon reduction in the system.

[0004] Based on existing research, while significant progress has been made both domestically and internationally, the following issues remain: First, research on the dynamic coupling relationship between energy flow and carbon flow within a GCES is relatively scarce. Most studies tend to use the input-output relationship of equipment as the core of the modeling method, neglecting the feedback mechanism and dynamic coupling relationship between the two. Second, in low-carbon optimization methods within a GCES, the supporting role of the low-carbon interaction between the GCES and the power grid in system energy conservation and carbon reduction, as well as in the low-carbon operation of the regional power grid, is rarely considered.

[0005] A search revealed Chinese invention patent application publication number CN115329518A, which discloses a method, electronic device, and storage medium for measuring carbon emission flow in an electrothermal coupling network based on energy flow. The method includes the following steps: S1, constructing a carbon emission flow model for a power network; S2, constructing a carbon emission flow model for a thermal network; S3, constructing a carbon emission flow model for an energy conversion device; S4, calculating the energy flow of the integrated energy network based on the carbon emission flow model of the power network in step S1 and the carbon emission flow model of the thermal network in step S2; S5, collecting parameters of the electrothermal coupling network, and calculating the carbon emission flow of the electrothermal coupling network based on the calculation results of the carbon emission flow models of the power network in step S1, the thermal network in step S2, the energy conversion device in step S3, and the energy flow in step S4. This generates a node load matrix, an energy matrix of the energy supply nodes injecting into the network, an energy flow distribution matrix, and a node energy flux matrix in the electrothermal coupling network, and calculates the node carbon potential of the electrothermal coupling network; S6, calculating the carbon flow rate of the load nodes using the node carbon potential of the electrothermal coupling network obtained in step S5. The existing patent application fails to consider the impact of energy storage devices on the dynamic carbon potential of nodes within the GCES, resulting in inaccurate carbon emission calculations.

[0006] How to achieve dynamic modeling of carbon energy flow in GCES and low-carbon interactive optimization of station network has become a technical problem that needs to be solved. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a low-carbon interaction optimization method for the GCES that takes into account the dynamic carbon potential inside the GCES.

[0008] The objective of this invention can be achieved through the following technical solutions:

[0009] According to one aspect of the present invention, a method for optimizing low-carbon interaction in a gas station network considering the dynamic carbon potential within a gas station energy storage system (GCES) is provided, comprising the following steps:

[0010] Based on the internal equipment composition and system architecture of GCES, a correlation analysis of the carbon energy coupling relationship between the various devices within GCES is conducted. Based on the correlation analysis, GCES is defined as a complete electrothermal coupling network, and each device is regarded as an energy supply network node in the electrothermal coupling network.

[0011] The electro-hydraulic-thermal equations of the electro-thermal coupled network are constructed, and the Newton-Raphson method is used to iteratively solve the electro-hydraulic-thermal equations to obtain the power flow calculation results of the electro-thermal coupled network.

[0012] Taking into account the impact of energy storage devices on the dynamic carbon potential of nodes within the electrothermal coupling network, the dynamic carbon potential of nodes in the electrothermal coupling network is calculated based on the power flow calculation results of the electrothermal coupling network. The dynamic carbon potential of nodes includes the dynamic carbon potential of energy supply network nodes, the dynamic carbon potential of microgrid nodes, and the dynamic carbon potential of energy storage device nodes in the electrothermal coupling network.

[0013] Using the power output of equipment within the GCES as an optimization variable, and considering the impact of time-of-use electricity pricing and the dynamic carbon potential of nodes in the electrothermal coupling network, a low-carbon interactive optimization model for the station-network is constructed to optimize the operation strategy of the GCES. The low-carbon interactive optimization model for the station-network aims to minimize economic costs and carbon emissions.

[0014] Preferably, the dynamic carbon potential of the energy storage device node is the ratio of the remaining carbon flow of the energy storage device to the remaining energy of the energy storage device;

[0015] The energy storage device includes a cold storage tank and a heat storage tank.

[0016] More preferably, the remaining energy of the energy storage device includes the remaining cold energy of the cold storage tank and the remaining heat energy of the heat storage tank, and its calculation process includes:

[0017] ;

[0018] ;

[0019] In the formula, , express t The state of cold and hot energy at any given moment; , express t The cold and hot energy states at time 0; , These represent the cooling and heating efficiency coefficients, respectively. Indicates specific heat capacity; , These represent the rated capacities of the cold storage tank and the heat storage tank, respectively. This indicates the remaining cooling capacity of the cold storage tank; This indicates the remaining heat in the heat storage tank; Indicates the energy supply network node i mass flow rate; Indicates temperature difference.

[0020] More preferably, the remaining carbon flow of the energy storage device is dynamically determined by the charging carbon flow rate, the releasing carbon flow rate, and the loss carbon flow rate, wherein the charging carbon flow rate and the releasing carbon flow rate both take into account the changes in the stored energy in the energy storage tank and the dynamic carbon emission factors during charging and releasing.

[0021] Preferably, the calculation of the dynamic carbon potential of the energy supply network nodes includes:

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] In the formula, Indicates the energy supply network node i Dynamic carbon potential for heating or cooling; Indicates the energy supply network node i The heating or cooling power; Indicates the energy supply network node i Carbon emissions from heating or cooling; Indicates water supply network nodes n Dynamic carbon potential; Indicates the supply and return water pipeline network b carbon flux density; Indicates return water network node n Dynamic carbon potential; Represents the water supply temperature vector; Represents the return water temperature vector; Indicates the energy supply network node i The mass flow rate.

[0027] Preferably, the dynamic carbon potential of the nodes within the GCES is only affected by the power flow injected by the power supply network nodes.

[0028] Preferably, the station network low-carbon interactive optimization model includes cooling, heating and power balance constraints and energy supply network constraints, wherein the energy supply network constraints include node energy flow constraints, node temperature constraints, node power flow constraints and node voltage constraints.

[0029] Preferably, the GCES is a distributed structure, including multiple distributed energy stations, which share a set of energy storage equipment and complement each other's energy according to the load demand on the user side.

[0030] The electrothermal coupling network includes a microgrid consisting of multiple microgrid nodes and an internal power supply network of the GCES consisting of multiple power supply network nodes.

[0031] Preferably, the power flow calculation results of the electrothermal coupling network include voltage phase angle, voltage amplitude, mass flow rate, supply water temperature, and return water temperature.

[0032] Preferably, the electro-hydraulic-thermal equations of the electro-thermal coupled network are constructed using electro-thermal power flow calculation methods, including: node flow balance equations, loop pressure balance equations, supply water temperature equations, return water temperature equations, active power equations, and reactive power equations.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1) This invention is based on the correlation analysis of carbon energy coupling relationships among various devices within the GCES. It treats the GCES as a complete electrothermal coupling network and comprehensively considers the impact of energy storage devices on the dynamic carbon potential of nodes within the electrothermal coupling network. Based on the power flow calculation results of the electrothermal coupling network, it refines the calculation of the dynamic carbon potential of the nodes in the electrothermal coupling network. Considering the impact of time-of-use pricing and the dynamic carbon potential of the nodes in the electrothermal coupling network, it constructs a low-carbon interactive optimization model for the station network to optimize the operation strategy of the GCES, achieving the goal of minimizing economic costs and carbon emissions. Compared with strategies that do not consider dynamic carbon potential and time-of-use pricing, it more accurately reflects the economic and environmental benefits of the system after optimization, reducing energy waste while meeting load demand.

[0035] 2) This invention considers the impact of energy storage devices on the dynamic carbon potential of the system. It performs fine dynamic calculations on the dynamic carbon potential of the energy supply network nodes, the dynamic carbon potential of the micro nodes in the electrothermal coupling network, and the dynamic carbon potential of the energy storage device nodes. The dynamic carbon potential is applied to the station-network low-carbon interaction optimization model to optimize the operation strategy, so that the carbon emissions between the grid-connected combined cooling, heating and power energy station and the distribution network achieve optimal interaction. Attached Figure Description

[0036] Figure 1 This invention describes the architecture and carbon energy flow distribution of a grid-connected combined cooling, heating and power (CCHP) energy station.

[0037] Figure 2 This refers to the electrothermal coupling network of the grid-connected combined cooling, heating and power (CCHP) energy station in this invention.

[0038] Figure 3 This invention provides a method for calculating the dynamic carbon potential of nodes in the energy station's cooling, heating, and electrical coupled power supply network.

[0039] Figure 4 This is a diagram illustrating time-of-use electricity pricing.

[0040] Figure 5 To optimize the power balance of the power supply equipment before and after the power supply;

[0041] Figure 6 To optimize the heat and power balance of the power output of the preceding and following energy supply equipment;

[0042] Figure 7 To optimize the power balance between the power supply equipment before and after power supply;

[0043] Figure 8 To optimize the node dynamic carbon potential of IEEE33 before;

[0044] Figure 9 To optimize the node dynamic carbon potential of IEEE33;

[0045] Figure 10 This is a schematic diagram comparing the dynamic carbon emission factors at the grid connection point in this invention;

[0046] Figure 11 This is a schematic diagram of the dynamic carbon potential of the heat network nodes in this invention;

[0047] Figure 12 This is a schematic diagram of the dynamic carbon potential of a microgrid node in this invention;

[0048] Figure 13 This is a schematic diagram of the dynamic carbon emission factors for cooling and heating in the GCES system of this invention.

[0049] Figure 14 This is a schematic diagram of the dynamic carbon emission factor of GCES power supply in this invention;

[0050] Figure 15 This is a schematic diagram of the dynamic carbon emission factor during the filling and releasing of the cold storage tank in this invention;

[0051] Figure 16 This is a schematic diagram of the dynamic carbon emission factor of the thermal storage tank in this invention.

[0052] Figure 17 This is a flowchart illustrating the low-carbon interactive optimization method for the station network in this invention. Detailed Implementation

[0053] 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.

[0054] Example 1

[0055] This embodiment relates to a low-carbon interaction optimization method for power grid considering the dynamic carbon potential within the GCES. By refining the carbon energy flow model in the integrated energy system structure containing energy storage devices, the dynamic carbon potential of the nodes in the energy supply network within the GCES is calculated in detail. By comprehensively considering the dynamic carbon potential and time-of-use pricing, the low-carbon interaction between the GCES and the power grid is achieved.

[0056] like Figure 17 The method includes the following steps:

[0057] Step 1: Based on the internal equipment composition and system architecture of GCES, conduct a correlation analysis of the carbon energy coupling relationship between the various devices within the system. Treat the internal energy supply network of GCES as a complete electrothermal coupling network and design it as a star network. Analyze the operating characteristics and energy flow of each device in the system.

[0058] Step 2: Based on the relevant methods for calculating power and heat flow, the Newton-Raphson method is used to simultaneously solve the power-hydraulic-thermal equations of the internal electro-thermal coupled network of GCES, and obtain the voltage phase angle, voltage amplitude, mass flow rate, supply water temperature and return water temperature, thereby obtaining the power flow calculation results of the electro-thermal coupled network.

[0059] Step 3: Taking into account the impact of energy storage devices on the dynamic carbon potential of nodes inside the GCES, a method for calculating the dynamic carbon potential of nodes inside the GCES is proposed based on the power flow calculation results of the electrothermal coupled network. The concept of residual carbon flow is introduced, and the dynamic carbon potential of energy storage devices is defined in particular.

[0060] Step 4: Fully consider the dynamic carbon potential within the GCES and time-of-use pricing (e.g.) Figure 4 To mitigate the impact of energy consumption, carbon reduction, and economic operation of the system, the power output of equipment within the GCES is used as the optimization variable. The objective functions are set as minimizing economic cost and minimizing carbon emissions. An optimization model is constructed that satisfies the balance of cooling, heating, and electrical power, node energy flow constraints, node temperature constraints, node power flow constraints, and node voltage constraints. The CPLEX solver is used to solve the optimization model.

[0061] like Figure 1 The GCES (Gas-Generated Energy System) features a distributed structure with multiple subsystems and multi-level energy flow. The designed system mainly consists of two distributed energy stations. Energy station 1 primarily comprises a gas-fired internal combustion engine, a hot-water lithium bromide chiller, a gas-fired hot-water boiler, and an electric chiller unit. Energy station 2 primarily comprises a gas turbine, a flue gas heat exchanger, an electric boiler, and an air-source heat pump. The two energy stations share a single energy storage system, enabling mutual energy support and complementarity based on user load demands. The energy storage system mainly consists of cold storage tanks and heat storage tanks.

[0062] The above architecture allows for the analysis of carbon energy flow relationships between equipment. Gas-fired internal combustion engines and gas turbines both generate electricity and heat by consuming natural gas. Specifically, the gas-fired internal combustion engine and the lithium bromide generator unit constitute cogeneration unit 1 (CHP1), which utilizes the waste heat from the gas-fired internal combustion engine to generate cooling energy. The gas turbine and the flue gas heat exchanger constitute cogeneration unit 2 (CHP2), where the flue gas heat exchanger converts the waste heat from the gas turbine into heat energy. Gas-fired hot water boilers generate heat energy by consuming natural gas. Electric refrigeration units, air-source heat pumps, and electric boilers generate cooling energy by consuming electricity. During the generation of cooling, heat, and electricity within the energy station, the operation of each piece of equipment also generates certain carbon emissions. The complex energy supply network within the GCES and the energy conversion and storage processes of different equipment constitute its internal carbon energy flow distribution, such as... Figure 1 As shown, when GCES is connected to the grid, it exhibits significant potential for collaborative optimization and complex grid interaction characteristics:

[0063] 1) Cogeneration (combined heat and power unit, CHP) and energy storage devices have strong peak shaving and valley filling capabilities, which can effectively smooth out local power grid load fluctuations;

[0064] 2) The generation of electricity by the energy station and its connection to the grid will also have a certain impact on the carbon emissions of the upper-level power grid. At the same time, the upper-level power grid will also have an impact on the carbon emissions of the power-consuming equipment inside the energy station.

[0065] like Figure 2 The GCES is defined as a complete electrothermal coupling network. Each device is regarded as a power supply network node (n1~n15) in the electrothermal coupling network. The GCES contains a microgrid structure with 6 nodes (p1~p6) and the power supply network inside the GCES is designed as a star network with 15 nodes (n1~n15). The network nodes of the electrothermal coupling network are defined as shown in Table 1. The cold and hot water manifolds in the energy station are regarded as the intermediate nodes of the star network, i.e., n8~n11.

[0066] Table 1

[0067]

[0068] Step 2 specifically includes:

[0069] First, the dynamic carbon emission factor of the GCES grid connection point is calculated using the power carbon emission flow method. This method is then extended to the calculation method of the node dynamic carbon potential of the electrothermal coupled network, where the grid connection point is the 14th node of the upstream distribution network (IEEE 33 standard node system), and the energy station (GCES) is connected to the grid at this node. The electro-hydraulic-thermal systems of the electrothermal coupled network within the GCES are solved simultaneously using the Newton-Raphson method.

[0070] (21) Thermal: Nodal flow balance equation

[0071] (1)

[0072] In the formula, Represents the thermal network correlation matrix; Represents a node i The mass flow rate, kg / s; A vector representing the node mass flow rate, in kg / s.

[0073] (22) Thermodynamics: Loop pressure balance equation

[0074] (2)

[0075] In the formula, Represents the cyclic correlation matrix; Let m represent the head loss vector.

[0076] (3)

[0077] The head loss equation is mainly used to represent the relationship between head loss and mass flow rate in each pipe section. The head loss of the pipe section is calculated using the Darcy-Weisbach formula. This represents a coefficient vector, which generally depends on the pipe section diameter; Indicates the length of the pipe section, in meters (m). Indicates the inner diameter of the pipe section, in meters (m). Indicates the coefficient of friction; This indicates the density of water, in kg / m³. 3 ; Expresses the acceleration due to gravity, in m / s² 2 .

[0078] (23) Supply and return water temperature equations

[0079] (4)

[0080] (5)

[0081] In the formula, Represents the heat capacity coefficient matrix; Represents the water supply temperature vector; Represents the heat load vector; Represents the heat capacity coefficient matrix; Represents the return water temperature vector; This represents the heat load vector.

[0082] (24) Electricity: Active and Reactive Power Equations

[0083] (6)

[0084] (7)

[0085] In the formula, Represents a node i The active power, kW; Represents a node i The reactive power, kW; , The value represents the node voltage amplitude, in kV. , Represent the real and imaginary parts of the nodal admittance matrix; Indicates the total number of nodes in the system; This indicates the voltage phase angle.

[0086] (25) Iterative relation of the Newton-Raphson method (abbreviated as Newton-Raphson method)

[0087] (8)

[0088] (9)

[0089] In the formula, and Indicates the first i +1st time and the first i The solution vector of the second time x ; Represents the Jacobian matrix; Represents the deviation vector matrix; For voltage phase angle, For voltage amplitude, For mass flow rate, For water supply temperature, The return water temperature. In each iteration of the combined power-hydraulic-thermal solution, the thermal network correlation matrix A... h Correlation matrix B with loop h The update is based on the mass flow rate direction of each pipe segment. In the Newton-Lambert simultaneous solution, the main affected component is the deviation vector matrix. And Jacobi matrix ,as follows:

[0090] (10)

[0091] In the formula, Indicates thermal power deviation; Indicates the loop voltage drop deviation; Indicates deviation in water supply temperature; Indicates the deviation in return water temperature; Indicates active power deviation; Indicates reactive power deviation; This indicates the specific heat capacity of water; Indicates temperature difference; Indicates a given thermal power; Indicates the load water supply temperature; Indicates the return water temperature under load; Indicates a given active power; Indicates a given reactive power; Indicates the real part; Indicates the imaginary part; Indicates voltage amplitude; This represents the power grid admittance matrix.

[0092] (11)

[0093] In the formula, the overall Jacobian matrix is ​​obtained by solving the deviation vector and consists of four sub-matrices: the power sub-matrix J. e Electron-pair thermal matrix J eh thermal pair electron matrix J he Thermodynamic submatrix J h .

[0094] like Figure 3 Step 3 specifically includes:

[0095] 31) Solving the dynamic carbon potential of GCES power supply network nodes

[0096] The GCES includes three energy supply networks: cold, hot, and electric. The cold and hot networks are affected by the heat flow distribution of the supply and return water networks. Therefore, the carbon flow calculation for the cold and hot networks is as follows:

[0097] (12)

[0098] (13)

[0099] (14)

[0100] (15)

[0101] In the formula, Represents a nodei Heating / cooling carbon potential, gCO2 / kWh; Represents a node i Thermal / cooling power, kW; Represents a node i Carbon emissions from heating / cooling, kgCO2; Indicates water supply network nodes n Carbon potential, gCO2 / kWh; Indicates the supply and return water pipeline network b carbon flux density; Indicates return water network node n Carbon potential, gCO2 / kWh; Represents the water supply temperature vector; This represents the return water temperature vector, where the nodes are the internal power supply network nodes of GCES, and i ranges from 1 to 15.

[0102] Within the GCES, microgrid nodes are affected by the power flow at the grid connection point, and the node carbon potential is only affected by the power flow injected into that node. The carbon flow is calculated as follows:

[0103] (16)

[0104] (17)

[0105] In the formula, Represents microgrid nodes i Carbon flow rate at time t; Represents microgrid nodes i The electrical power, kW; Represents microgrid nodes i Carbon potential, gCO2 / kWh; This represents the active power of branch b at time t, in kW. Let represent the carbon flux density of branch b at time t.

[0106] Carbon flow is equivalent to adding a carbon emission label to the tidal current on each branch. It is a virtual network flow that exists in relation to the tidal current and is used to characterize the carbon emissions that maintain the tidal current on any branch in the system. Thus, the dynamic carbon potential of each node at each time period can be obtained from a spatial perspective.

[0107] Based on the carbon flow calculation formula above, the total carbon emissions from GCES are equal to the sum of carbon emissions from electrical energy, thermal energy, and cooling energy, as follows:

[0108] (18)

[0109] In the formula, E Indicates total carbon emissions from GCES; , , These represent carbon emissions from electrical energy, thermal energy, and cold energy, respectively, in kgCO2.

[0110] 32) Solving the dynamic carbon potential of energy storage device nodes

[0111] For carbon flow tracking calculations in electrothermal coupled networks, the energy storage device needs to be extended into a dynamic model, and the energy balance and carbon emission allocation logic of the heating network needs to be corrected simultaneously to obtain the dynamic power flow equations for thermal balance in GCES:

[0112] (19)

[0113] In the formula, Represents the thermal / cooling power of node i, in kW; The heat source power generation capacity of node i is expressed in kW. Represents the thermal load at node i, in kW; This represents the energy stored in the tank at time t, in kWh. This represents the set of heat sources that supply heat to node i.

[0114] The dynamic carbon potential of an energy storage device's nodes is determined by its historical charging and discharging processes and changes in carbon energy flow. During charging, the device absorbs energy from upstream nodes and simultaneously absorbs their carbon potential; during discharging, the stored energy is released to downstream nodes, and the carbon potential of the output energy is determined by the current remaining carbon flow rate within the storage tank. The instantaneous carbon flow rate dynamic equation and the remaining carbon flow rate equation for carbon storage and release in the energy storage device are as follows:

[0115] (20)

[0116] ;(twenty one)

[0117] In the formula, , Indicates the charge and discharge carbon flow rate; , Indicates the dynamic carbon emission factor during charging and discharging; This indicates the remaining carbon flow rate in the cold storage tank, in kgCO2. This indicates the remaining carbon flow rate in the thermal storage tank, in kgCO2. , Indicates the charge and discharge carbon flow rate; , Indicates the charge / release carbon flow rate; , This indicates the carbon flow rate for cold and hot losses.

[0118] The energy states in the cold storage tank and the heat storage tank in the GCES are defined as the state of cold (SOC) and the state of heat (SOH), respectively. The relevant calculations are as follows, and the remaining cold and heat in the tanks can be further calculated:

[0119] ;(twenty two)

[0120] ;(twenty three)

[0121] In the formula, , This represents the cold and hot energy states at time t; , This represents the cold and hot energy states at time t0; , Indicates the coefficient of performance for cooling and heating; Specific heat capacity, kJ·kg -1 ·℃ -1 ; , Indicates the rated capacity of the cold and heat storage tanks, in kW; This indicates the remaining cooling capacity of the cold storage tank, in kW; This indicates the remaining cooling capacity of the heat storage tank, in kW.

[0122] The nodal dynamic carbon potential of GCES energy storage devices (cold storage tanks, heat storage tanks) at the charging and discharging times can be calculated from the residual carbon flow and residual cold / heat. , as follows:

[0123] ;(twenty four)

[0124] Step 4 specifically includes:

[0125] The station-grid low-carbon interaction optimization model fully considers the impact of dynamic carbon potential and time-of-use pricing within the system. It uses the output of equipment within the GCES (Gas-Generated Energy System) as an optimization variable, and optimizes the GCES operation strategy based on time-of-use pricing and the dynamic carbon potential of nodes in the electrothermal coupling network. Through carbon-electricity synergy in station-grid low-carbon interaction, the GCES can adjust its energy use and power purchase strategies at different times, optimizing equipment output while meeting load demand, effectively reducing system operating costs and carbon emissions.

[0126] 41) Objective function

[0127] The objective function of the low-carbon interactive optimization model for the power station network is set to simultaneously satisfy the minimum economic cost and the minimum carbon emissions:

[0128] (25)

[0129] (26)

[0130] In the formula, The total economic cost is expressed in yuan. This represents the equipment energy consumption cost, expressed in yuan. This represents the energy purchase cost, expressed in yuan. E Indicates total carbon emissions from GCES; , , These represent carbon emissions from electrical energy, thermal energy, and cold energy, respectively, in kgCO2.

[0131] 42) Constraints

[0132] The low-carbon interactive optimization model of the power station network mainly has two types of constraints: cooling, heating and power balance constraints and energy supply network constraints.

[0133] Cold, heat, and electrical power balance constraints:

[0134] (27)

[0135] (28)

[0136] In the formula, , Indicates equipment n Upper and lower limits of output, kW; Indicates equipment n Output, kW; This indicates the generator output power of an internal combustion engine, expressed in kW. This indicates the power generation capacity of the gas turbine, expressed in kW. This indicates the amount of electricity purchased from the power grid, in kW. This indicates the thermal power of an internal combustion engine, expressed in kW. This indicates the thermal power of the gas turbine, in kW. This indicates the heating capacity of the gas-fired boiler, in kW. This indicates the heating capacity of the electric boiler, in kW. The remaining thermal power of the thermal storage tank is expressed in kW. This indicates the refrigeration power of the lithium bromide system, in kW; This indicates the refrigeration power of the electric refrigeration unit, in kW. This indicates the cooling capacity of the air source heat pump, in kW. This indicates the remaining cooling capacity of the cold storage tank, in kW; , , These represent electrical, heating, and cooling loads, respectively, in kW.

[0137] Power supply network constraints mainly include node energy flow constraints, node temperature constraints, node power flow constraints, and node voltage constraints.

[0138] (29)

[0139] (30)

[0140] (31)

[0141] (32)

[0142] In the formula, Represents the thermal / cooling power of node i, in kW; Represents a node i The mass flow rate, kg / s; Specific heat capacity, kJ·kg -1 ·℃ -1 ; Indicates temperature difference; Let represent the electrical power of node i, in kW; , Indicates the upper and lower limits of the temperature at node n, in °C; Represents the temperature at node n, in °C; This represents the power injected by the wind turbine into node i, in kW; The power injected into photovoltaic node i is expressed in kW. This represents the power injected into node i by the coal-fired unit, in kW; This represents the power injected into node i of the CHP (Combined Heat and Power) unit, in kW; Represents the active power of node i, in kW; , Indicates the upper and lower limits of the voltage at node i, in kV; This represents the voltage at node i, in kV.

[0143] Example 2

[0144] This embodiment also relates to a low-carbon interaction optimization method for the GCES network that considers the dynamic carbon potential within the GCES, setting up two scenarios to optimize the operation of the GCES:

[0145] Scenario 1: GCES operation optimization without considering dynamic carbon potential and time-of-use pricing.

[0146] Scenario 2: Consider the GCES operation optimization of the station network low-carbon interaction optimization model.

[0147] During periods of low electricity prices and carbon potential, the GCES (Generation-Generated Energy System) can increase the proportion of electricity purchased, reduce the output of internal generator units, or increase energy storage. This measure not only reduces electricity costs but also achieves energy conservation and carbon reduction in the system. During periods of high electricity prices and carbon potential, the GCES reduces the proportion of electricity purchased and increases the proportion of low-carbon power generation to achieve the system's goal of reducing energy purchase costs and meeting carbon reduction requirements.

[0148] The balance of cold, heat, and electrical power before and after the optimization of equipment output is as follows: Figure 5 , Figure 6 and Figure 7 Table 2 shows the comparison of the scenario optimization results. Scenario 2 shows a 4.4% reduction in carbon emissions and a 3.9% reduction in economic costs compared to Scenario 1.

[0149] Table 2

[0150]

[0151] Table 3 shows the parameters of photovoltaic, wind turbine, and coal-fired power units in the IEEE 33-node system. The dynamic carbon potential of each node at different times of the day before and after optimization is shown in Table 3. Figure 8 and Figure 9 As shown, the dynamic carbon potential at the grid connection point is compared to, for example... Figure 10 As shown in the figure, analysis reveals that nodes close to the main grid coal-fired power units but not connected to new energy power generation have higher carbon potential, while nodes close to new energy power generation such as photovoltaic and wind turbines have lower carbon potential.

[0152] After the upstream distribution network was connected to the GCES, the low-carbon characteristics of the GCES effectively reduced the dynamic carbon potential of node 14 at the grid connection point, and it was significantly lower than the average value (static carbon emission factor). This is because: the GCES contains cold and heat storage equipment and CHP units. The energy storage equipment stores energy during low loads and releases energy during high loads, which can optimize the energy supply and demand balance and improve energy utilization efficiency; the power generation of the CHP units within the GCES during high-carbon periods offsets part of the purchase of high-carbon electricity, thus affecting the system's dynamic carbon potential. The carbon potential is higher at night because photovoltaics do not generate electricity at night. At this time, node 14 is less affected by the low-carbon generating units of the upstream distribution network, and is also affected by the CHP units within the GCES during this period. During the day, the dynamic carbon potential of the node is reduced by the combined influence of photovoltaics and wind turbines.

[0153] Table 3

[0154]

[0155] The GCES (Generated Gas Storage and Energy Storage System) includes cold and hot energy storage devices and CHP (Consumer-Driven Power) units. The storage devices store energy during low-load periods and release it during high-load periods, optimizing energy supply and demand balance and improving energy efficiency. The CHP units within the GCES generate electricity during high-carbon periods, offsetting some of the purchase of high-carbon electricity, thus affecting the system's dynamic carbon potential. The dynamic carbon potential of nodes in a star network is as follows: Figure 11 , Figure 12 As shown, precise calculations yielded the dynamic carbon potential at each node, thus resolving the issue that calculating carbon emissions using average values ​​(static carbon emission factors) was too coarse and failed to consider the low-carbon characteristics of the energy station. Furthermore, based on equipment capacity, cooling, heating, and power loads, and pipe mass flow rates, the optimized dynamic carbon emission factors for cooling, heating, and power supply in the GCES can be solved, such as... Figure 13 and Figure 14 As shown. Among them, the rising and falling trends of dynamic carbon emission factors for cooling supply are related to... Figure 10 The carbon emission factors at node 14 of the grid connection point are basically consistent. This is because the cooling equipment within the GCES is all power-consuming equipment, and since the CHP units within the GCES generate relatively little electricity, the cooling equipment is mainly affected by the node's carbon emission factor. The heating network's carbon emission factor is lower at night because the CHP units in the GCES operate during periods of high-carbon electricity purchase, thereby reducing the carbon emissions of the GCES heating network. The reason why the heating network's carbon emission factor is higher than the cold network's carbon emission factor at certain times is that the heating equipment is mainly gas-fired heating equipment, and the carbon emission factor of gas is a fixed value, so the heating network is less affected by the carbon emission factor per kilowatt-hour.

[0156] Meanwhile, the rapid start-up and shutdown characteristics of the CHP units within the system can mitigate the impact of wind and solar power fluctuations. The utilization of waste heat from CHP can also reduce natural gas consumption and indirectly reduce electricity demand, thus having a complex impact on the carbon potential of the grid connection point. Therefore, it can be concluded that the self-powered electricity within the GCES belongs to low-carbon electricity.

[0157] The dynamic charging and discharging processes of cold and heat storage tanks can alter the power flow distribution at grid-connected nodes and the internal operating modes of the system, thereby changing network losses and the scheduling of GCES power supply equipment, and thus affecting the dynamic carbon potential of system nodes. The dynamic carbon potential of cold and heat storage tanks is as follows: Figure 15 and Figure 16 As shown, combined with Figures 5-7 Analysis shows that both the cold and heat storage tanks reach their peak capacity before 6 PM, and store only low-carbon cold and heat. Both tanks charge during relatively low-carbon periods and release energy during peak load demand periods.

[0158] Example 3

[0159] 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.

[0160] 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.

[0161] The processing unit performs the various methods and processes described above. For example, in some embodiments, the methods 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 the methods described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute the methods by any other suitable means (e.g., by means of firmware).

[0162] 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 Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0163] 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.

[0164] 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. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or 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, optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0165] 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 low-carbon interaction in a gas station network considering the dynamic carbon potential within a gas station energy storage system (GCES), characterized in that, Includes the following steps: Based on the internal equipment composition and system architecture of GCES, a correlation analysis of the carbon energy coupling relationship between the various devices within GCES is conducted. Based on the correlation analysis, GCES is defined as a complete electrothermal coupling network, and each device is regarded as an energy supply network node in the electrothermal coupling network. The electro-hydraulic-thermal equations of the electro-thermal coupled network are constructed, and the Newton-Raphson method is used to iteratively solve the electro-hydraulic-thermal equations to obtain the power flow calculation results of the electro-thermal coupled network. Taking into account the impact of energy storage devices on the dynamic carbon potential of nodes within the electrothermal coupling network, the dynamic carbon potential of nodes in the electrothermal coupling network is calculated based on the power flow calculation results of the electrothermal coupling network. The dynamic carbon potential of nodes includes the dynamic carbon potential of energy supply network nodes, the dynamic carbon potential of microgrid nodes, and the dynamic carbon potential of energy storage device nodes in the electrothermal coupling network. Using the power output of equipment within the GCES as an optimization variable, and considering the impact of time-of-use electricity pricing and the dynamic carbon potential of nodes in the electrothermal coupling network, a low-carbon interactive optimization model for the station network is constructed to optimize the operation strategy of the GCES. The low-carbon interactive optimization model for the station network aims to minimize economic costs and carbon emissions. The dynamic carbon potential of the energy storage device node is the ratio of the remaining carbon flow of the energy storage device to the remaining energy of the energy storage device; the energy storage device includes a cold storage tank and a heat storage tank; The remaining energy of the energy storage device includes the remaining cold energy of the cold storage tank and the remaining heat energy of the hot storage tank, and its calculation process includes: ； ； In the formula, , express t The state of cold and hot energy at any given moment is dimensionless; , express t The cold and hot energy states at time 0 are dimensionless; , These represent the cold and hot efficiency coefficients, respectively, and are dimensionless. Specific heat capacity, unit: kJ·kg -1 ·℃ -1 ; , These represent the rated capacities of the cold storage tank and the heat storage tank, respectively, in kWh. This indicates the remaining cooling capacity of the cold storage tank, in kW. This indicates the remaining heat capacity of the thermal storage tank, in kW. Indicates the energy supply network node i Mass flow rate, in kg / s; Temperature difference, unit: °C; The calculation of the dynamic carbon potential of the energy supply network nodes includes: ； ； ； ； In the formula, Indicates the energy supply network node i Dynamic carbon potential for heating or cooling; Indicates the energy supply network node i The heating or cooling power; Indicates the energy supply network node i Carbon emissions from heating or cooling; Indicates water supply network nodes n Dynamic carbon potential; Indicates the supply and return water pipeline network b carbon flux density; Indicates return water network node n Dynamic carbon potential; Represents the water supply temperature vector; Represents the return water temperature vector; Indicates the energy supply network node i The mass flow rate.

2. The method for optimizing low-carbon interaction in a station network considering the dynamic carbon potential within a GCES, as described in claim 1, is characterized in that... The remaining carbon flow rate of the energy storage device is dynamically determined by the charging carbon flow rate, the releasing carbon flow rate, and the loss carbon flow rate. The charging carbon flow rate and the releasing carbon flow rate both take into account the changes in the stored energy in the energy storage tank and the dynamic carbon emission factors during charging and releasing.

3. The method for optimizing low-carbon interaction in a station network considering the dynamic carbon potential within a GCES, as described in claim 1, is characterized in that... The dynamic carbon potential of nodes within the GCES is only affected by the power flow injected into the power supply network nodes.

4. The method for optimizing low-carbon interaction in a station network considering the dynamic carbon potential within a GCES, as described in claim 1, is characterized in that... The low-carbon interactive optimization model of the station network includes cooling, heating and power balance constraints and energy supply network constraints, wherein the energy supply network constraints include node energy flow constraints, node temperature constraints, node power flow constraints and node voltage constraints.

5. The method for optimizing low-carbon interaction in a station network considering the dynamic carbon potential within a GCES, as described in claim 1, is characterized in that... The GCES is a distributed structure, including multiple distributed energy stations. These multiple distributed energy stations share a set of energy storage equipment and complement each other's energy according to the load demand on the user side. The electrothermal coupling network includes a microgrid consisting of multiple microgrid nodes and an internal power supply network of the GCES consisting of multiple power supply network nodes.

6. The method for optimizing low-carbon interaction in a station network considering the dynamic carbon potential within a GCES, as described in claim 1, is characterized in that... The power flow calculation results of the electrothermal coupling network include voltage phase angle, voltage amplitude, mass flow rate, supply water temperature, and return water temperature.

7. The method for optimizing low-carbon interaction in a station network considering the dynamic carbon potential within a GCES, as described in claim 1, is characterized in that... The electro-hydraulic-thermal equations of the electro-thermal coupled network are constructed using the electro-thermal power flow calculation method, including: nodal flow balance equations, loop pressure balance equations, supply water temperature equations, return water temperature equations, active power equations, and reactive power equations.

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