Station network low-carbon interaction optimization method considering GCES internal dynamic carbon potential

Abstract

The invention relates to the technical field of comprehensive energy system application, and relates to a station network low-carbon interaction optimization method considering dynamic carbon potential in a GCES, comprising the following steps: defining the interior of the GCES as a complete electrothermal coupling network based on correlation analysis of a carbon energy coupling relationship among devices in the GCES; adopting a Newton-Raphson method to carry out iterative solution on the constructed electric power hydraulic thermal equation to obtain a load flow calculation result of the electrothermal coupling network; the influence of energy storage equipment on the dynamic carbon potential of the internal nodes of the GCES is comprehensively considered, and the dynamic carbon potential of the nodes of the electrothermal coupling network is calculated based on the load flow calculation result of the electrothermal coupling network; and taking the equipment output condition in the GCES as an optimization variable, considering the influence of the time-of-use electricity price and the node dynamic carbon potential of the electrothermal coupling network, and constructing a station network low-carbon interaction optimization model to optimize the operation strategy of the GCES. Compared with the prior art, the method has the advantages that the influence of the dynamic carbon potential on carbon emission is considered, and the lowest carbon emission and the lowest economic cost are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of integrated energy system application, and particularly relates to a station-network low-carbon interaction optimization method considering internal dynamic carbon potential of GCES. BACKGROUND

[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. The grid-connected combined cooling, heating and power energy station (GCES) contains various energy storage resources, and is an integrated regional energy supply center that integrates power generation, heat supply, and refrigeration, and is connected to the urban power grid. GCES can realize multi-energy complementation and optimal scheduling in the multi-process energy collaborative supply of power generation, heat supply, and refrigeration. GCES is a potential high-quality flexible and adjustable load resource for the power grid, which can effectively promote the development and utilization of clean energy and distributed energy, optimize the spatial pattern and time distribution of power production and consumption, promote industrial structure adjustment and consumption concept change, and provide conditions for energy efficient conversion and energy saving and carbon reduction of the power system.

[0003] With the increasing proportion of integrated energy systems participating in power supply and demand interaction, and the increasing demand for energy efficient utilization and low carbon, the energy saving and carbon reduction method and carbon flow tracing of integrated energy systems have become a research hotspot. For integrated energy systems containing energy storage devices, the energy consumption and production forms of each device are very diverse, and the internal carbon flow of the system is complex and variable. The main purpose of the fine calculation of the internal carbon and energy flow distribution and dynamic carbon potential of the integrated energy system is to construct a carbon and energy flow model through the dynamic coupling and correlation characteristics of the internal energy flow and carbon flow of the integrated energy system, and then to study the dynamic carbon potential calculation method of the internal energy supply network, so as to realize energy saving and carbon reduction of the system and support the supply and demand balance of the regional power grid.

[0004] From the existing research, it can be seen that domestic and foreign research has made certain research results, but there are still the following problems. First, the research on the dynamic coupling and correlation relationship between the internal energy flow and carbon flow of GCES is relatively rare, and most of them are biased towards using the input and output relationship of the device as the core of the modeling method, ignoring the feedback mechanism and dynamic coupling relationship between them. Second, in the low-carbon optimization method of GCES, the low-carbon interaction between GCES and the power grid is rarely considered to support the energy saving and carbon reduction of the system and the low-carbon operation of the regional power grid.

[0005] After retrieval, Chinese invention patent application publication No. CN115329518A discloses an energy flow-based carbon emission flow metering method for an electric-thermal coupling network, an electronic device and a storage medium, comprising the following steps: S1, constructing a carbon emission flow model of a power network; S2, constructing a carbon emission flow model of a thermal network; S3, constructing a carbon emission flow model of an energy conversion device; S4, calculating the energy flow of a comprehensive energy network according to the carbon emission flow model of the power network of step S1, the carbon emission flow model of the thermal network of step S2; S5, collecting parameters of an electric-thermal coupling network, and calculating the carbon emission flow of the electric-thermal coupling network according to the carbon emission flow model of the power network of step S1, the carbon emission flow model of the thermal network of step S2, the carbon emission flow model of the energy conversion device of step S3 and the calculation result of the energy flow of step S4, to generate a node load matrix in the electric-thermal coupling network, an energy matrix injected into the network by a power supply node, an energy flow distribution matrix and a node energy flux matrix, and calculate the node carbon potential of the electric-thermal coupling network; S6, calculating the carbon flow rate of a load node according to the node carbon potential of the electric-thermal coupling network obtained in step S5. The existing patent application has the problem that the influence of energy storage devices on the internal dynamic carbon potential of GCES is not considered, resulting in inaccurate carbon emission calculation.

[0006] How to realize GCES carbon energy flow dynamic modeling and station network low-carbon interactive optimization has become a technical problem to be solved. SUMMARY

[0007] The purpose of the present application is to overcome the defects of the prior art and provide a station network low-carbon interactive optimization method considering the internal dynamic carbon potential of GCES.

[0008] The purpose of the present application can be realized by the following technical solutions: According to one aspect of the present application, a station network low-carbon interactive optimization method considering the internal dynamic carbon potential of GCES is provided, comprising the following steps: According to the device composition and system architecture inside GCES, the correlation analysis of the carbon energy coupling relationship between each device inside GCES is performed; based on the correlation analysis, GCES inside is defined as a complete electric-thermal coupling network, and each device is regarded as a power supply network node in the electric-thermal coupling network; The electric power, hydraulic and thermal equations of the electric-thermal coupling network are constructed, and the Newton-Raphson method is used to iteratively solve the electric power, hydraulic and thermal equations to obtain the power flow calculation result of the electric-thermal coupling network; The influence of energy storage devices on the internal dynamic carbon potential of the electric-thermal coupling network is considered, and the node dynamic carbon potential of the electric-thermal coupling network is calculated based on the power flow calculation result of the electric-thermal coupling network; the node dynamic carbon potential includes the dynamic carbon potential of the power supply network node in the electric-thermal coupling network, the dynamic carbon potential of the microgrid node in the electric-thermal coupling network and the dynamic carbon potential of the energy storage device node; The output of the equipment in the GCES is taken as an optimization variable, the time-of-use electricity price and the influence of the node dynamic carbon potential of the electric-thermal coupled network are considered, and a station-network low-carbon interaction optimization model is constructed to optimize the operation strategy of the GCES, wherein the station-network low-carbon interaction optimization model takes the minimum economic cost and the minimum carbon emission as the target.

[0009] Preferably, the node dynamic carbon potential of the energy storage device is the ratio of the residual carbon flow of the energy storage device to the residual energy of the energy storage device. The energy storage device comprises a cold storage tank and a heat storage tank.

[0010] More preferably, the residual energy of the energy storage device comprises residual cold energy of the cold storage tank and residual heat energy of the heat storage tank, and the calculation process comprises: ; ; In the formula, , denote the cold and heat energy states at the moment t; t , denote the cold and heat energy states at the moment 0; t , denote the cold and heat efficiency coefficients, respectively; denote the specific heat capacity; , denote the rated capacities of the cold storage tank and the heat storage tank, respectively; denote the residual cold energy of the cold storage tank; denote the residual heat energy of the heat storage tank; denote the mass flow rate of the energy supply network node ; i denote the temperature difference.

[0011] More preferably, the residual carbon flow of the energy storage device is dynamically determined by the charging carbon flow rate, the discharging carbon flow rate and the loss carbon flow rate, wherein the charging carbon flow rate and the discharging carbon flow rate both consider the change of the stored energy in the energy storage tank and the charging and discharging dynamic carbon emission factor.

[0012] Preferably, the calculation of the node dynamic carbon potential of the energy supply network comprises: ; ; ; ; In the formula, denote the heat supply or cold supply dynamic carbon potential of the energy supply network node i ; denote the heat supply or cold supply dynamic carbon potential of the energy supply network node i ​​a heating or cooling power; representing a heating or cooling carbon emission of the energy supply network node i representing a dynamic carbon potential of the energy supply network node representing a carbon flow density of the energy supply network pipe n representing a dynamic carbon potential of the energy supply network node representing a carbon flow density of the energy supply network pipe b representing a dynamic carbon potential of the energy supply network node representing a dynamic carbon potential of the energy supply network node n representing a water supply temperature vector representing a return water temperature vector representing a water supply temperature vector representing a mass flow rate of the energy supply network node. i

[0013] Preferably, the GCES internal node dynamic carbon potential is only affected by the energy supply network node injection tidal flow.

[0014] Preferably, the station network low-carbon interaction optimization model includes a cold and heat power balance constraint and an energy supply network constraint, wherein the energy supply network constraint includes a node energy flow constraint, a node temperature constraint, a node tidal flow constraint and a node voltage constraint.

[0015] Preferably, the GCES is a distributed structure, including a plurality of distributed energy stations, and a plurality of distributed energy stations share a set of energy storage devices, and form energy mutual aid and complement each other according to user side load demand. The electric-thermal coupling network includes a microgrid composed of a plurality of microgrid nodes, and a GCES internal energy supply pipe network composed of a plurality of energy supply network nodes.

[0016] Preferably, the tidal flow calculation result of the electric-thermal coupling network includes voltage phase angle, voltage amplitude, mass flow rate, water supply temperature and return water temperature.

[0017] Preferably, the electric-thermal coupling network power water thermal power equation is constructed by using power thermal tidal flow calculation related method, including: node flow balance equation, loop pressure balance equation, water supply temperature equation, return water temperature equation, active power equation, reactive power equation.

[0018] Compared with the prior art, the present application has the following beneficial effects: ​1) The application is based on the correlation analysis of the carbon energy coupling relationship between the devices inside the GCES, and the inside of the GCES is regarded as a complete electro-thermal coupling network. The influence of the energy storage device on the dynamic carbon potential of the internal node of the electro-thermal coupling network is considered. Based on the flow calculation result of the electro-thermal coupling network, the node dynamic carbon potential of the electro-thermal coupling network is calculated in detail. Considering the influence of time-of-use electricity price and node dynamic carbon potential of the electro-thermal coupling network, a station-network low-carbon interaction optimization model is constructed to optimize the operation strategy of the GCES, so as to realize the goal of minimum economic cost and carbon emission. Compared with the strategy without considering dynamic carbon potential and time-of-use electricity price, the economic efficiency and environmental protection after the system optimization solution are more accurately reflected, and the energy waste is reduced while meeting the load demand.

[0019] 2) The application considers the influence of the energy storage device on the system dynamic carbon potential, and respectively calculates the dynamic carbon potential of the energy supply network node in the electro-thermal coupling network, the dynamic carbon potential of the micro node in the electro-thermal coupling network and the dynamic carbon potential of the energy storage device node in detail. The dynamic carbon potential is applied to the station-network low-carbon interaction optimization model to optimize the operation strategy, so that the carbon emission between the grid-connected combined heat and power energy station and the distribution network realizes interactive optimization. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The architecture and carbon energy flow distribution of the grid-connected combined heat and power energy station in the application; Figure 2 The electro-thermal coupling network of the grid-connected combined heat and power energy station in the application; Figure 3 The dynamic carbon potential calculation method of the energy supply network node of the energy station in the application; Figure 4 The time-of-use electricity price schematic diagram; Figure 5 The cold power balance of the energy supply device output before and after optimization; Figure 6 The heat power balance of the energy supply device output before and after optimization; Figure 7 The electric power balance of the energy supply device output before and after optimization; Figure 8 The node dynamic carbon potential of the optimization before IEEE33; Figure 9 The node dynamic carbon potential of the optimization after IEEE33; Figure 10 The grid point dynamic carbon emission factor comparison schematic diagram in the application; Figure 11 The dynamic carbon potential schematic diagram of the heat network node in the application; Figure 12 The dynamic carbon potential schematic diagram of the micro grid node in the application; Figure 13 Fig. 1 is a schematic diagram of a dynamic carbon emission factor for GCES cooling in the present application; Figure 14 Fig. 2 is a schematic diagram of a dynamic carbon emission factor for GCES power in the present application; Figure 15 Fig. 3 is a schematic diagram of a dynamic carbon emission factor for charging and discharging of a cold storage tank in the present application; Figure 16 Fig. 4 is a schematic diagram of a dynamic carbon emission factor for charging and discharging of a heat storage tank in the present application; Figure 17 Fig. 5 is a schematic diagram of a process of a station-network low-carbon interaction optimization method in the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0022] Embodiment 1 The present embodiment relates to a station-network low-carbon interaction optimization method considering the internal dynamic carbon potential of GCES. Through fine modeling of carbon energy flow in the structure of a comprehensive energy system containing energy storage devices, fine calculation of the node dynamic carbon potential of the energy supply network in GCES is realized. Through comprehensive consideration of the dynamic carbon potential and time-of-use electricity price, low-carbon interaction of GCES and the power grid is realized.

[0023] As Figure 17 , the method comprises the following steps: Step 1: According to the device composition and system architecture inside GCES, the relevance analysis of the carbon energy coupling relationship between each device inside the system is performed. The energy supply network inside GCES is regarded as a complete electric-thermal coupling network and designed as a star network. The operating characteristics and energy flow of each device in the system are analyzed. Step 2: According to the related method of power and thermal power flow calculation, the Newton-Raphson method is used to simultaneously solve the power and thermal equations of the electric-thermal coupling network inside GCES. The voltage phase angle, voltage amplitude, mass flow rate, water supply temperature and return water temperature are obtained, so as to obtain the power flow calculation result of the electric-thermal coupling network.

[0024] Step 3: The influence of energy storage devices on the node dynamic carbon potential inside GCES is comprehensively considered. Based on the power flow calculation result of the electric-thermal coupling network, a calculation method of the node dynamic carbon potential inside GCES is proposed. The concept of residual carbon flow is introduced, and the dynamic carbon potential of energy storage devices is defined.

[0025] Step 4: Taking into full account of the internal dynamic carbon potential and time-of-use electricity price (e.g. Figure 4 ), the system energy-saving and carbon-reducing and economic operation goals are considered, the equipment output of GCES is taken as the optimization variable, the minimum economic cost and the minimum carbon emission are set as the objective function, and the optimization model satisfying the cold and heat power balance, node energy flow constraint, node temperature constraint, node power flow constraint, and node voltage constraint is constructed, and the CPLEX solver is used to solve the optimization model.

[0026] As shown in Figure 1 , the GCES is a distributed structure with multiple subsystems and multi-level energy flow. The designed system mainly consists of two distributed energy stations. The equipment of energy station 1 mainly includes a gas internal combustion engine, a hot water type lithium bromide refrigerator, a gas hot water boiler, and an electric refrigerator unit. The equipment of energy station 2 mainly includes a gas turbine, a flue gas heat exchanger, an electric boiler, and an air source heat pump. The two energy stations share a set of energy storage equipment, and can form energy mutual aid and complement each other according to the load demand of the user side. The energy storage equipment mainly consists of a cold storage tank and a heat storage tank.

[0027] From the above architecture, the carbon energy flow correlation between the devices can be analyzed. The gas internal combustion engine and the gas turbine consume natural gas to generate electric energy and heat energy. The gas internal combustion engine and the lithium bromide unit constitute a heat and power unit 1 (CHP1), and the lithium bromide unit generates cold energy by utilizing the waste heat of the gas internal combustion engine. The gas turbine and the flue gas heat exchanger constitute a heat and power unit 2 (CHP2), and the flue gas heat exchanger converts the waste heat of the gas turbine into heat energy. The gas hot water boiler generates heat energy by consuming natural gas. The electric refrigerator unit, the air source heat pump, and the electric boiler generate cold energy by consuming electric energy. In the process of generating cold energy, heat energy, and electric energy inside the energy station, the operation process of each device will also produce a certain amount of carbon emissions. The complex energy supply network inside the GCES and the energy conversion and storage process of different devices constitute the internal carbon energy flow distribution, as shown in Figure 1 When the GCES is connected to the grid, it exhibits significant collaborative optimization potential and complex grid interaction characteristics: 1) The cogeneration unit (CHP) and the energy storage equipment have strong peak shaving and valley filling ability, and can effectively smooth the local grid load fluctuation. 2) The power generation of the energy station to the grid will also have a certain impact on the carbon emissions of the upper grid, and the upper grid will also have an impact on the carbon emissions of the internal power consumption devices of the energy station.

[0028] As shown in Figure 2, define GCES internal as a complete electro-thermal coupling network, each device is regarded as a power supply network node (n1~n15) in the electro-thermal coupling network, GCES internal contains a 6-node (p1~p6) microgrid structure, and the GCES internal power supply network is designed as a 15-node (n1~n15) star network, the definition of the network nodes of the electro-thermal coupling network is shown in Table 1, wherein the cold and hot water distribution reservoir in the energy station is regarded as the intermediate node of the star network, i.e. n8~n11.

[0029] Table 1

[0030] Step 2 specifically includes: First, the power dynamic carbon emission factor of the GCES grid-connected point is calculated by the power carbon emission flow method, and the node dynamic carbon potential calculation method of the electro-thermal coupling network is extended according to this method, wherein the grid-connected point is the 14th node of the upper distribution network (IEEE33 standard node system), and the energy station (GCES) is connected to the grid at this node. The power, water and heat of the electro-thermal coupling network in the GCES internal are solved simultaneously, and the Newton-Raphson method is used for solving.

[0031] (21) Heat: node flow balance equation ; (1) In the formula, represents the heat network correlation matrix; represents the mass flow rate of node i , kg / s; represents the vector of node mass flow rate, kg / s.

[0032] (22) Heat: loop pressure balance equation ; (2) In the formula, represents the loop correlation matrix; represents the head loss vector, m.

[0033] ; (3) The head loss equation is mainly used to represent the relationship between the head loss of each pipe section and the mass flow rate, and the head loss of the pipe section is calculated by using the Darcy-Weisbach formula. represents the coefficient vector, which is generally determined by the pipe diameter; represents the pipe length, m; represents the pipe inner diameter, m; represents the friction coefficient; represents the water density, kg / m 3 ; represents the acceleration due to gravity, m / s 2 .

[0034] (23) Supply and return water temperature equations ; (4) ; (5) Where, 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; represents the heat load vector.

[0035] (24) Electricity: Active and reactive power equations ; (6) ; (7) Where, Representation node i Active power, kW; Representation node i Reactive power, kW; 、 represents the node voltage amplitude, kV; 、 represents the real and imaginary parts of the node admittance matrix; Indicates the total number of nodes in the system; Indicates the voltage phase angle.

[0036] (25) Newton-Raphson method (abbreviated as Newton-Raphson method) iteration relationship ; (8) ; (9) Where, and Indicates the i +1 and i The solution vector x ; represents the Jacobian matrix; represents the bias vector matrix; is the voltage phase angle, is the voltage amplitude, is the mass flow rate, is the water supply temperature, is the return water temperature. In each iteration of the electric-hydraulic-thermal joint solution, the thermal network correlation matrix A h and loop association matrix Bh The update is made according to the mass flow rate direction of each pipe segment. In the Newton-Raphson simultaneous solution, the main affected are the bias vector matrix and the Jacobian matrix , as follows: ; (10) In the formula, represents the thermal power bias; represents the loop pressure drop bias; represents the supply water temperature bias; represents the return water temperature bias; represents the active power bias; represents the reactive power bias; represents the specific heat capacity of water; represents the temperature difference; represents the given thermal power; represents the load supply water temperature; represents the load return water temperature; represents the given active power; represents the given reactive power; represents the real part; represents the imaginary part; represents the voltage amplitude; represents the grid admittance matrix.

[0037] ; (11) In the formula, the overall Jacobian matrix is solved by the bias vector, and is composed of 4 sub-matrices: the power sub-matrix J e , the electrical-to-thermal sub-matrix J eh , the thermal-to-electrical sub-matrix J he , and the thermal sub-matrix J h .

[0038] As Figure 3 , step 3 specifically includes: 31) GCES energy supply network node dynamic carbon potential solution The GCES includes cold, heat, and electricity energy supply networks. The cold network and the heat network are affected by the heat flow distribution of the supply and return water pipe network, so the carbon flow calculation for the cold network and the heat network is as follows: ; (12) ; (13) ; (14) ; (15) In the formula, represents the node ithe heating / cooling carbon potential of the supply node, gCO2 / kWh; representing the node i the heating / cooling power of the node, kW; representing the node i the heating / cooling carbon emission of the node, kgCO2; representing the carbon potential of the supply node n , gCO2 / kWh; representing the carbon flow density of the supply and return pipe network pipe b ; representing the carbon potential of the return node n , gCO2 / kWh; representing the supply water temperature vector; representing the return water temperature vector, wherein the node here is the GCES internal energy supply network node, i is from 1 to 15.

[0039] The GCES internal micro-grid node is affected by the grid-connected point power flow, and the node carbon potential is only affected by the injection flow of the node. The carbon flow is calculated as follows: ; (16) ; (17) In the formula, representing the carbon flow rate at time t at the micro-grid node i ; representing the electrical power of the micro-grid node i , kW; representing the carbon potential of the micro-grid node i , gCO2 / kWh; representing the active power of branch b at time t, kW; representing the carbon flow density of branch b at time t.

[0040] The carbon flow is equivalent to adding a carbon emission label to the flow on each branch. It is a virtual network flow formed by attaching to the flow and used to represent the carbon emission of maintaining the flow of any branch in the system. Thus, the dynamic carbon potential of each node at each time period can be obtained from the spatial angle.

[0041] From the above carbon flow calculation formula, the total carbon emission of the GCES is equal to the sum of the carbon emissions of electrical energy, thermal energy and cold energy, as follows: ; (18) In the formula, E representing the total carbon emission of the GCES; , , representing the carbon emissions of electrical energy, thermal energy and cold energy, respectively, kgCO2.

[0042] 32) Dynamic carbon potential solving of energy storage device node For carbon flow tracking calculation of the electric-thermal coupled network, the energy storage device needs to be expanded into a dynamic model, and the energy balance and carbon emission allocation logic of the heat network are also modified to obtain the dynamic power flow equation of the GCES: ; (19) In the formula, Pi represents the heating / cooling power of node i, kW; Pi represents the heat source power of node i, kW; Pi represents the heat load of node i, kW; Et represents the energy stored in the tank at time t, kWh; Qi represents the set of heat sources supplying heat to node i.

[0043] The node dynamic carbon potential of the energy storage device is determined by its historical charging and discharging process and carbon energy flow change. When the energy storage device is charging, it absorbs energy from the upstream node and absorbs the carbon potential at the same time. When discharging, the stored energy is released to the downstream node, and the carbon potential of the output energy is determined by the remaining carbon flow in the tank. The instantaneous carbon flow rate dynamic equation of the energy storage device carbon flow storage and release and the remaining carbon flow equation are as follows: ; v20) ; (21) In the formula, , Qi represents the charging and discharging carbon flow rate; , Qi represents the charging and discharging dynamic carbon emission factor; Qi represents the remaining carbon flow in the cold storage tank, kgCO2; Qi represents the remaining carbon flow in the heat storage tank, kgCO2; , Qi represents the charging and discharging carbon flow rate; , Qi represents the charging and discharging carbon flow rate; , Qi represents the charging and discharging carbon flow rate.

[0044] The energy state in the cold storage tank and the heat storage tank in the GCES is 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 tank can be further calculated: ; (22) ; (23) In the formula, , represents the cold, heat energy state at time t; , represents the cold, heat energy state at time t0; , represents the cold, heat efficiency coefficient; represents the specific heat capacity, kJ·kg -1 ·℃ -1 ; , represents the rated capacity of the cold storage tank, kW; represents the remaining cold energy of the cold storage tank, kW; represents the remaining cold energy of the cold storage tank, kW.

[0045] The node dynamic carbon potential of the GCES energy storage device (cold storage tank, heat storage tank) at the charging and discharging time can be calculated from the remaining carbon flow and the remaining cold / heat , As follows: ; (24) Step 4 specifically includes: The station network low-carbon interaction optimization model fully considers the influence of internal dynamic carbon potential and time-of-use electricity price, and takes the output of the GCES equipment as an optimization variable. According to the time-of-use electricity price and the node dynamic carbon potential of the electric-thermal coupled network, the operation strategy of the GCES is optimized. By participating in the station network low-carbon interaction through carbon-electricity cooperation, the GCES can adjust the energy use and power purchase strategy in different time periods, optimize the output of the equipment under the premise of meeting the load demand, and effectively reduce the system operation cost and carbon emissions.

[0046] 41) Objective function The objective function of the station network low-carbon interaction optimization model is set to simultaneously satisfy the minimum economic cost and the minimum carbon emissions: ; (25) ; (26) In the formula, represents the total economic cost, yuan; represents the equipment energy consumption cost, yuan; represents the energy purchase cost, yuan; E represents the total carbon emissions of the GCES; , , represents the carbon emissions of electric energy, heat energy, and cold energy, respectively, kgCO2.

[0047] 42) Constraint conditions There are mainly two kinds of constraints in the station network low-carbon interaction optimization model: cold-heat-electric power balance constraint and energy supply network constraint; Cold-heat-electric power balance constraint: ; (27) ; (28) where, , denotes the equipment n output upper limit, kW; denotes the equipment n output, kW; denotes the internal combustion engine power generation, kW; denotes the gas turbine power generation, kW; denotes the grid power purchase, kW; denotes the internal combustion engine heating power, kW; denotes the gas turbine heating power, kW; denotes the gas boiler heating power, kW; denotes the electric boiler heating power, kW; denotes the heat storage tank residual heating power, kW; denotes the lithium bromide machine refrigeration power, kW; denotes the electric refrigerator refrigeration power, kW; denotes the air source heat pump refrigeration power, kW; denotes the cold storage tank residual cold power, kW; , , denote the electric, heating, and cooling loads, respectively, kW.

[0048] Energy supply network constraints: mainly include node energy flow constraints, node temperature constraints, node power flow constraints, and node voltage constraints: ; (29) ; (30) ; (31) ; (32) where, denotes the heating / cooling power of node i, kW; denotes the mass flow rate of node i , kg / s; denotes the specific heat capacity, kJ·kg -1 ·℃ -1 ; denotes the temperature difference; denotes the electric power of node i, kW; , denotes the node n temperature upper and lower limits, ℃; denotes the node n temperature, ℃; Pwind,i represents the power of the wind turbine injection node i, kW; Ppv,i represents the power of the photovoltaic injection node i, kW; Pcoal,i represents the power of the coal-fired unit injection node i, kW; PCHP,i represents the power of the CHP (Combined Heat and Power) injection node i, kW; Pi represents the active power of node i, kW; , Vi represents the upper and lower limits of the voltage of node i, kV; Vi represents the voltage of node i, kV.

[0049] Embodiment 2 This embodiment also relates to a station network low-carbon interaction optimization method considering the internal dynamic carbon potential of GCES, and two scenarios are set to optimize the operation of GCES: Scenario 1: GCES operation optimization without considering dynamic carbon potential and time-of-use electricity price.

[0050] Scenario 2: GCES operation optimization considering the station network low-carbon interaction optimization model.

[0051] Since the station network low-carbon interaction optimization model can increase the proportion of power purchase, reduce the output of internal generator units or increase energy storage in periods with low electricity price and carbon potential, this measure not only reduces the electricity cost, but also achieves energy saving and carbon reduction of the system; in periods with high electricity price and carbon potential, GCES reduces the proportion of power purchase and increases the proportion of low-carbon power generation, so as to achieve the demand of reducing the energy cost and carbon emission of the system.

[0052] The cold, heat and electric power balance before and after solving the equipment output optimization is as shown in Figure 5 , Figure 6 and Figure 7 The comparison of scenario optimization results is shown in Table 2, and the carbon emission of scenario 2 is reduced by 4.4% and the economic cost is reduced by 3.9% compared with scenario 1.

[0053] Table 2

[0054] In the IEEE33 node, the parameters of photovoltaic, wind turbine and coal-fired unit are shown in Table 3, and the dynamic carbon potential (Carbon Potanial) of each node in each period within a day before and after optimization is shown in Figure 8 and Figure 9 The comparison of grid-connected node dynamic carbon potential is shown in Figure 10 Through analysis, it can be found that the carbon potential of the node close to the main network coal-fired unit and not connected to new energy power generation is high, and the carbon potential of the node close to photovoltaic and wind turbine new energy power generation is low.

[0055] The low-carbon characteristics of the GCES effectively reduce the dynamic carbon potential of the 14th node of the grid-connected point, and are significantly lower than the average value (static carbon emission factor), because: the GCES contains cold and heat storage devices and CHP units, the storage devices store energy at low load and release energy at high load, which can optimize the energy supply and demand balance and improve energy utilization efficiency; the power generation of the CHP units in the GCES offsets the purchase of part of the high-carbon power during the high-carbon period, thereby affecting the dynamic carbon potential of the system. The carbon potential is higher at night because photovoltaic power is not generated at night, at which time the 14th node is less affected by the low-carbon power generation units of the upper distribution network, and is also affected by the CHP units in the GCES, and the dynamic carbon potential of the node is reduced during the day by photovoltaic and wind power.

[0056] Table 3

[0057] The GCES contains cold and heat storage devices and CHP units, the storage devices store energy at low load and release energy at high load, which can optimize the energy supply and demand balance and improve energy utilization efficiency; the power generation of the CHP units in the GCES offsets the purchase of part of the high-carbon power during the high-carbon period, thereby affecting the dynamic carbon potential of the system. The dynamic carbon potential of the nodes of the star-shaped network is shown in Figure 11 、 Figure 12 , the dynamic carbon potential of the nodes at each time is accurately calculated, thereby solving the problem that the average value (static carbon emission factor) is used to calculate the carbon emission, which is relatively rough and does not consider the low-carbon characteristics of the energy station. Further, according to the device capacity, cold and heat load, and pipe segment mass flow rate, the dynamic carbon emission factors of the optimized GCES for cooling, heating, and power supply can be solved, as shown in Figure 13 and Figure 14 . Among them, the rising and falling trend of the dynamic carbon emission factor for cooling is basically the same as the carbon emission factor of the 14th node of the grid-connected point in Figure 10 , because the cooling devices in the GCES are all power-consuming devices, and the power generation of the CHP units in the GCES is small, so the cooling devices are mainly affected by the carbon emission factor of the node. The heating carbon emission factor of the heat network is lower at night, because the CHP units of the GCES operate during the purchase period of high-carbon power, thereby reducing the carbon emission of the GCES heat pipe network, and the reason why the carbon emission factor of the heat network is higher than that of the cold network in part of the period is that the heating device is mainly a gas heating device, and the carbon emission factor of the gas is a fixed value, and the heat network is less affected by the carbon emission factor of the power.

[0058] At the same time, the rapid start-stop characteristics of the CHP units in the system can smooth the influence of wind and light fluctuations, and the waste heat utilization of the CHP can reduce the consumption of natural gas and indirectly reduce the demand for electricity, thereby having a complex influence on the carbon potential of the grid-connected point, and it is concluded that the self-power supply of the GCES is low-carbon power.

[0059] The dynamic charging and discharging process of cold storage tanks and thermal storage tanks can change the power distribution of grid-connected nodes and the operation mode within the system, thereby changing the network loss and GCES energy supply equipment scheduling, thereby affecting the dynamic carbon potential of system nodes. Figure 15 and Figure 16 As shown, combined Figures 5-7 Analysis shows that both cold and thermal storage tanks reach peak capacity before 6 p.m., storing low-carbon cooling and heat. Both tanks are charged during relatively low-carbon times and released during peak load demand periods.

[0060] Example 3 The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0061] Many components in a device are connected to the I / O interface, including: input units, such as a keyboard and mouse; output units, such as various types of displays and speakers; storage units, such as magnetic disks and optical disks; and communication units, such as network cards, modems, and wireless communication transceivers. The communication unit allows the device to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks.

[0062] The processing unit performs the various methods and processes described above. For example, in some embodiments, the method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via a 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 method described above can be performed. Alternatively, in other embodiments, the CPU can be configured to execute the method in any other appropriate manner (e.g., by means of firmware).

[0063] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0064] Program code for carrying out methods of the present application 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 apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / operations specified in the flowchart diagrams and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, or entirely on a remote machine or server.

[0065] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable storage medium can include, without limitation, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories, optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0066] The above description is only specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A station network low-carbon interaction optimization method considering internal dynamic carbon potential of GCES, characterized in that, The method comprises the following steps: According to the equipment configuration and system architecture inside the GCES, the correlation of the carbon energy coupling relationship between each device inside the GCES is analyzed; based on the correlation analysis, the GCES inside is defined as a complete electro-thermal coupling network, and each device is regarded as a power supply network node in the electro-thermal coupling network; The power-hydraulic-thermal equation of the electro-thermal coupling network is constructed, and the Newton-Raphson method is used to iteratively solve the power-hydraulic-thermal equation to obtain the flow calculation result of the electro-thermal coupling network; The influence of the energy storage device on the dynamic carbon potential of the internal node of the electro-thermal coupling network is comprehensively considered, and the dynamic carbon potential of the node of the electro-thermal coupling network is calculated based on the flow calculation result of the electro-thermal coupling network; the dynamic carbon potential of the node includes the dynamic carbon potential of the power supply network node in the electro-thermal coupling network, the dynamic carbon potential of the micro-grid node in the electro-thermal coupling network, and the dynamic carbon potential of the energy storage device node; The output of the device inside the GCES is taken as an optimization variable, the influence of the time-of-use electricity price and the dynamic carbon potential of the node of the electro-thermal coupling network is considered, and a station-network low-carbon interaction optimization model is constructed to optimize the operation strategy of the GCES, and the station-network low-carbon interaction optimization model takes the minimum economic cost and the minimum carbon emission as the target.

2. The method of claim 1, wherein the method further comprises: The dynamic carbon potential of the energy storage device node is the ratio of the residual carbon flow rate of the energy storage device to the residual energy of the energy storage device; The energy storage device includes a cold storage tank and a heat storage tank.

3. The method of claim 2, wherein the method further comprises: The residual energy of the energy storage device includes residual cold energy of the cold storage tank and residual heat energy of the heat storage tank, and the calculation process includes: ； ； wherein, , denotes t the cold, hot energy state at time t; , denotes t the cold, hot energy state at time t = 0; , denotes the cold, hot efficiency coefficient, respectively; denotes the specific heat capacity; , denotes the rated capacity of the cold storage tank, hot storage tank, respectively; denotes the remaining cold energy of the cold storage tank; denotes the remaining hot energy of the hot storage tank; denotes the mass flow rate of the energy supply network node i ; denotes the temperature difference.

4. The method of claim 2, wherein the method further comprises: The residual carbon flow rate of the energy storage device is dynamically determined by the charging carbon flow rate, the discharging carbon flow rate and the loss carbon flow rate, wherein the charging carbon flow rate and the discharging carbon flow rate both consider the change of the stored energy in the energy storage tank and the dynamic carbon emission factor of charging and discharging.

5. The method of claim 1, wherein the method further comprises: The calculation of the dynamic carbon potential of the power supply network node includes: ； ； ； ； wherein represents the heating or cooling dynamic carbon potential of a heating or cooling network node i ; represents the heating or cooling power of a heating or cooling network node i ; represents the heating or cooling carbon emission of a heating or cooling network node i ; represents the dynamic carbon potential of a water supply network node n ; represents the carbon flow density of a water supply or return water network pipe b ; represents the dynamic carbon potential of a return water network node n ; represents the water supply temperature vector ; represents the return water temperature vector represents the mass flow rate of a heating or cooling network node i .

6. The method of claim 1, wherein the method further comprises: The dynamic carbon potential of the node inside the GCES is only affected by the injection flow of the power supply network node.

7. The method of claim 1, wherein the method further comprises: The station-network low-carbon interaction optimization model includes cold-heat-power balance constraints and power supply network constraints, wherein the power supply network constraints include node energy flow constraints, node temperature constraints, node flow constraints and node voltage constraints. 8.The method of claim 1, wherein, The GCES is of a distributed structure, including a plurality of distributed energy stations, a plurality of the distributed energy stations share a set of energy storage devices, and form energy mutual aid and complementation according to the load demand of the user side. The electro-thermal coupling network includes a micro-grid composed of a plurality of micro-grid nodes and a GCES internal power supply network composed of a plurality of power supply network nodes.

9. The method of claim 1, wherein the method further comprises: The flow calculation result of the electro-thermal coupling network includes voltage phase angle, voltage amplitude, mass flow rate, water supply temperature and return water temperature.

10. The method of claim 1, wherein the method further comprises: The power-hydraulic-thermal equation of the electro-thermal coupling network is constructed by using the related method of power-thermal flow calculation, including: node flow balance equation, loop pressure balance equation, water supply temperature equation, return water temperature equation, active power equation and reactive power equation.

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