Integrated energy system scheduling method considering dynamic characteristics of heat supply network under green certificate-carbon transaction interaction mechanism

Abstract

The invention relates to an integrated energy system scheduling method considering dynamic characteristics of a heat supply network under a green certificate-carbon transaction interaction mechanism. The method comprises the following steps: constructing an integrated energy system framework; constructing a green certificate transaction model and a stepped carbon transaction model based on the integrated energy system framework, further constructing a green certificate-carbon transaction interaction model by mining carbon emission reduction attributes contained in green certificates, and linking a green certificate market and a carbon market by taking a green certificate transaction volume and a carbon emission right transaction volume as interaction media; according to the heat delay and heat loss characteristics of the heat supply network in the integrated energy system framework, a heat supply network dynamic model is constructed; and on the basis of the dynamic models of the green evidence market, the carbon market and the heat supply network after interaction, constructing an optimal scheduling model by taking the minimum cost of the integrated energy system as a target, and solving and outputting a scheduling result to complete a scheduling process. Compared with the prior art, the method has the advantages of low carbon, economical operation and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system operation, and in particular to an integrated energy system scheduling method taking into account the dynamic characteristics of a heat network under a green certificate-carbon trading interaction mechanism. Background Art

[0002] By integrating multiple energy sources, integrated energy systems can coordinate and optimize the relationships between different energy sources, thereby achieving cascaded energy utilization and the coupled complementarity of various energy sources. In response to the "dual carbon" goals, integrated energy systems have gradually shifted from traditional economic scheduling to scheduling that balances low-carbon and economic efficiency. Green certificate trading mechanisms and tiered carbon trading mechanisms are important market-based institutional safeguards for the low-carbon economic operation of integrated energy systems. Currently, the consideration of green certificate trading and carbon trading in the scheduling of integrated energy systems is becoming increasingly common. For example, patent CN116780535B introduces carbon trading and green certificate trading in a virtual power plant system, providing a useful reference for the low-carbon economic operation of power systems. However, it fails to consider the carbon emission reduction properties of green certificates and lacks interaction between green certificates and carbon trading. Patent application CN115689736A, based on carbon trading and green certificate trading mechanisms, discloses a joint carbon green certificate trading market framework and establishes an optimized scheduling model for integrated energy systems that considers conditional value at risk. However, these solutions fail to address the interaction between the carbon trading market and the green certificate trading market, failing to effectively stimulate the vitality of the green certificate and carbon trading markets.

[0003] Integrated energy systems involve multiple coupled energy flows, but electricity and heat have different transmission characteristics and timescales. Ignoring this transmission process can lead to inaccurate scheduling results. Current research leverages the transmission characteristics of heating networks to improve system economics and renewable energy consumption, but it fails to consider the impact of the dynamic characteristics of heating networks on system carbon emissions. Furthermore, current research fails to consider the impact of the interaction between these dynamic characteristics and market trading mechanisms on system operation and scheduling.

[0004] The existing technologies mentioned above for low-carbon economic scheduling of integrated energy systems have achieved some remarkable results, but there are still certain shortcomings: First, since the green certificate trading market is in its infancy, some optimization scheduling methods only focus on the research of carbon trading, green certificate trading, or a single combination of green certificate and carbon trading. The linkage and interaction between the carbon trading market and the green certificate trading market needs further exploration; Second, most of the current technologies only utilize the transmission characteristics of the heating network to improve the system's economy and renewable energy absorption, and lack full consideration of the potential of the dynamic characteristics of the heating network in carbon emission reduction. Summary of the Invention

[0005] The purpose of the present invention is to provide a comprehensive energy system scheduling method that takes into account the dynamic characteristics of the heat network under the green certificate-carbon trading interaction mechanism to achieve low-carbon economic operation.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for scheduling an integrated energy system under a green certificate-carbon trading interaction mechanism taking into account the dynamic characteristics of the heat network comprises the following steps:

[0008] Build an integrated energy system framework;

[0009] Based on the integrated energy system framework, a green certificate trading model and a stepped carbon trading model are constructed. Furthermore, by exploring the carbon emission reduction properties of green certificates, a green certificate-carbon trading interaction model is constructed, which uses the green certificate trading volume and carbon emission rights trading volume as interactive media to link the green certificate market and the carbon market.

[0010] According to the heat delay and heat loss characteristics of the heating network in the integrated energy system framework, a dynamic model of the heating network is constructed;

[0011] Based on the interactive green certificate market, carbon market and heat network dynamic model, an optimization scheduling model is constructed with the minimum cost of the integrated energy system as the goal, and the solution is obtained, and the scheduling results are output to complete the scheduling process.

[0012] Furthermore, the integrated energy system framework includes three parts, namely the energy supply side, the energy conversion side and the load side. The energy supply side includes the upper-level power grid, the natural gas grid and wind power. The energy conversion side includes gas turbines, waste heat boilers, electric boilers, electric refrigerators, absorption refrigerators and power storage equipment. The gas turbines and waste heat boilers constitute a cogeneration unit. The load side includes three loads: electricity, heat and cold.

[0013] Furthermore, the expression of the green certificate trading model is:

[0014]

[0015] Where C GCT is the transaction cost of green certificates; c gc G is the unit price of green certificate transaction; re The amount of green certificates generated by wind power; G q The system meets the green certificate quantity required for assessment; re P is the conversion coefficient of wind power into green certificate; WT,t is the wind power; ω q is the green certificate quota coefficient of the system; P N,t is the total power demand of the system.

[0016] Furthermore, the tiered carbon trading model includes an actual carbon emission sub-model, a carbon emission quota sub-model, and a carbon emission rights trading quota sub-model, wherein the actual carbon emission sub-model is expressed as:

[0017]

[0018] Where, E r is the actual carbon emission; x1, y1, z1 and x2, y2, z2 are the carbon emission parameters of coal-fired generators and gas turbines respectively; P t grid P is the system's power purchase amount during period t; t GTh is the total output power of the gas turbine; P t GTh is the total output power of the gas turbine; P t GT,e is the output power of the gas turbine during period t; P t GT,h is the thermal output power of the gas turbine during period t;

[0019] The expression of the carbon emission quota sub-model is:

[0020]

[0021] Where, E q is the carbon emission quota; e , κ h are the carbon emission quotas per unit of electricity and heat supply respectively; e,h is the conversion factor of electrical and thermal power;

[0022] The expression of the carbon emission trading quota sub-model is:

[0023] E CET =E r -E q

[0024]

[0025] Where, E CET is the actual carbon emission trading volume of the system; C CET is the carbon trading cost; c ce is the unit carbon trading base price; l is the length of the carbon emission interval; θ is the price growth rate.

[0026] Furthermore, the steps of obtaining the green certificate trading volume and the carbon emission rights trading volume according to the green certificate-carbon trading interaction model include:

[0027] Calculate the green electricity carbon emissions contained in the green certificate, where the calculation expression is:

[0028] Q rc =Q cg -Q re

[0029] G rc =μ rc (G re -G q )

[0030] E of =Q rc ×G rc

[0031] Where Q rc The carbon emission reduction contained in the green certificate; Q cg , Q re are the carbon emissions equivalent of coal-fired power and renewable energy in their respective energy chain life cycles; G rc 、μ rc are the green certificate quantity and proportion coefficient for offsetting carbon emissions; G re The amount of green certificates generated by wind power; G q The amount of green certificates required for the system to meet the assessment requirements; E of Carbon emissions offset by green certificates generated by wind power;

[0032] Calculate the carbon emission trading volume of the integrated energy system after the interactive mechanism, where the calculation expression is:

[0033] E CET,a =E r -(E q +E of )

[0034] Where, E CET,a is the carbon emission trading volume of the integrated energy system after the interactive mechanism; E r is the actual carbon emissions; E q for carbon emission quotas;

[0035] Calculate the green certificate trading volume of the integrated energy system after the interactive mechanism, where the calculation expression is:

[0036] G GCT,a =G q -(G re -G rc )

[0037] Where G GCT,a Green certificate trading volume of the integrated energy system after the interactive mechanism.

[0038] Furthermore, the heat network dynamic model includes a heat delay sub-model and a heat loss sub-model, wherein the heat delay sub-model is expressed as:

[0039]

[0040] in:

[0041]

[0042] Where, are the terminal temperatures of the water supply and return pipe k in period t without considering heat loss, are the head end temperatures of the water supply and return pipe k during period t; γ k,t , δ k,t is the thermal delay correlation coefficient; N is the time set of the heat medium flowing through the pipeline; Δt is the time interval; ρ w is the density of the heat medium; A k is the cross-sectional area of ​​the pipe k; L k is the length of the pipeline k; m k is the mass flow rate of the heat medium at pipe k; R k,t t-γ t The total mass of heat medium flowing from time period to time period t; B k,t For k,t and δ k,t Determine the total mass of heat medium flowing through;

[0043] The expression of the heat loss sub-model is:

[0044]

[0045] Where, are the terminal temperatures of the supply and return pipes k at time period t respectively; is the ambient temperature outside the pipeline; J k is the heat loss coefficient of pipeline k; λ k is the thermal conductivity of the pipe k; c w is the specific heat capacity of the heat medium.

[0046] Furthermore, the optimization scheduling model includes an objective function and corresponding constraints, wherein the objective function aims to minimize the sum of energy purchase costs, equipment operation and maintenance costs, wind curtailment costs, green certificate trading costs, and carbon trading costs.

[0047] Furthermore, the objective function is expressed as:

[0048] C IES =min(C buy +C om +C wind +C GCT +C CET )

[0049] in:

[0050]

[0051] Where C IES is the total system cost; C buy is the energy purchase cost; C om C is the equipment operation and maintenance cost; wind is the cost of wind curtailment; C GCT is the transaction cost of green certificates; E CET is the actual carbon emission trading volume of the system; C ele,t is the unit electricity price during period t; P t grid C is the system's power purchase amount during period t; gas is the unit gas price; P t GT is the input power of the gas turbine during period t; H vg is the lower calorific value of natural gas; Δt is the time interval; are the operation and maintenance costs of fans, cogeneration units, electric boilers, electric refrigerators, absorption refrigerators, and energy storage equipment, and the operating power during period t; σ wind is the unit wind curtailment cost; P t WT,pre is the actual output power of wind power in period t; P t WT is the predicted wind power in period t.

[0052] Furthermore, the constraints include:

[0053] ① Power network constraints:

[0054]

[0055] P ij,min ≤P ij ≤P ij,max

[0056] Where, P ij is the active power of branch ij; B ij is the admittance of branch ij; θ ij is the voltage phase angle of branch ij; θ i ,θ j are the phase angles of node i and node j respectively; P ij,max 、P ij,min are the upper and lower limits of the branch ij power respectively;

[0057] ② Heating network constraints:

[0058]

[0059] T t S,min ≤T t S ≤T t S,max

[0060] T t R,min ≤T t R ≤T t R,max

[0061] Where, P t EB,h 、P t WHB,h are the output thermal power of the electric boiler and waste heat boiler during period t; P t AC is the input heat power of the absorption chiller during period t; c w is the specific heat capacity of the heat medium; is the heat medium flow rate at the heat source node i; are the supply and return water temperatures of heat source node i during period t, respectively; is the heat load demand of load node i during period t; is the heat medium flow rate at load node i; are the temperatures of heat medium flowing into and out of load node i during period t; T t S,max 、T t S ,min are the upper and lower limits of water supply temperature respectively; T t R,max 、T t R,min are the upper and lower limits of return water temperature respectively; T t S is the water supply temperature; T t R is the return water temperature;

[0062] ③Temperature fusion constraint:

[0063]

[0064] Where, is the mixed temperature of the heat medium at node i; m k is the mass flow rate of the heat medium at pipe k; x is the set of the water supply network S and the return network R; Φ pipe is a collection of pipelines;

[0065] ④Unit output constraints:

[0066] 1) Wind turbines

[0067] 0≤P t WT ≤P t WT,pre

[0068] Where, P t WT is the actual output power of wind power in period t; P t WT,pre Power forecasting for wind power;

[0069] 2) Gas turbine

[0070]

[0071] Where, P t GT,e is the output power of the gas turbine during period t; is the gas-to-electricity efficiency of the gas turbine; P t GT,h is the thermal output power of the gas turbine during period t; is the gas-to-heat efficiency of the gas turbine; are the upper and lower limits of gas turbine input power respectively; are the upper and lower limits of the gas turbine climbing power respectively;

[0072] 3) Waste heat boiler

[0073]

[0074] Where η WHB is the recovery efficiency of the waste heat boiler; P t WHB is the input thermal power of the waste heat boiler during period t; are the upper and lower limits of the waste heat boiler input power respectively;

[0075] 4) Electric boiler

[0076]

[0077] Where η EB is the energy conversion efficiency of the electric boiler; P t EB is the input power of the electric boiler during period t; They are the upper and lower limits of the electric boiler input power respectively; They are the upper and lower limits of the electric boiler's ramp power respectively;

[0078] 5) Electric Refrigerator

[0079]

[0080] Where, P t EC,c is the cooling output of the electric refrigerator during period t; COP ECis the conversion efficiency of the electric refrigerator; P t EC is the input electrical power of the electric refrigerator; They are the upper and lower limits of EC cooling output respectively; are the upper and lower limits of the electric refrigerator's climbing power respectively;

[0081] 6) Absorption refrigeration machine

[0082]

[0083] Where, P t AC,c is the cooling output of the absorption chiller during period t; COP AC is the conversion efficiency of the absorption chiller; They are the upper and lower limits of the cooling output of the absorption chiller respectively; They are the upper and lower limits of the ramp power of the absorption chiller respectively;

[0084] 7) Battery

[0085]

[0086] Where, E t is the energy stored in the battery during period t; cha ,η dis P is the battery charging and discharging efficiency; t cha 、P t dis are the battery charging and discharging power during period t; ε t is a binary variable, if ε t = 0, the battery discharges energy during period t, if ε t =1, the battery is charged during period t; E1 and E2 are the upper limits of battery charging and discharging power, respectively. T are the energy stored in the battery at the beginning and end of system dispatch; E max 、E min They are the upper and lower limits of battery capacity respectively;

[0087] ⑤Power balance constraint:

[0088]

[0089] Where A is the correlation matrix between nodes and energy units; B n is the imaginary part of the node admittance matrix; θ t is the voltage phase angle of the node during period t; They are electricity and cooling load demands respectively.

[0090] Furthermore, the CPLEX solver is used to solve the optimization scheduling model.

[0091] Compared with the prior art, the present invention has the following beneficial effects:

[0092] (1) The present invention takes into account the potential of green certificates to participate in the carbon market and the interaction between the green certificate market and the dynamic characteristics of the heat network in carbon emission reduction, optimizes scheduling with the goal of minimizing the overall cost of the system, and realizes the low-carbon economic operation of the integrated energy system.

[0093] (2) In the existing technology, the green certificate trading mechanism and the carbon trading mechanism are usually simple parallel or one-way interactions, lacking effective mutual linkage. The present invention, based on the green certificate trading mechanism and the ladder-type carbon trading mechanism, uses the whole life cycle evaluation to calculate the carbon emission equivalent of renewable energy and fossil energy, and then fully explores the carbon emission reduction attributes contained in the green certificate. The green certificate market and the carbon trading market are linked through the green certificate trading volume and the carbon emission rights trading volume as interactive media, thereby promoting the low-carbon economic operation of the system.

[0094] (3) Most existing technologies only consider the impact of the dynamic characteristics of the heating network on the economic efficiency of the system operation. However, the present invention constructs a dynamic characteristic model of the heating network based on the thermal inertia of the heating network, fully exploits the natural energy storage characteristics of the heating pipeline, and cooperates with the green certificate-carbon trading interaction mechanism to further improve the economy and low carbon efficiency of the system and reduce the wind curtailment rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 Schematic diagram of the method flow of the present invention;

[0096] Figure 2 It is a system structure diagram of the present invention;

[0097] Figure 3 This is a flow chart of the green certificate-ladder carbon trading interaction mechanism of the present invention;

[0098] Figure 4 Schematic diagram of the heating system of the present invention;

[0099] Figure 5 This is a schematic cross-sectional view of a heating pipeline according to the present invention;

[0100] Figure 6 The wind power output, load demand and ambient temperature curve of the present invention;

[0101] Figure 7 These are the wind power consumption curves under different scenarios of the present invention. DETAILED DESCRIPTION

[0102] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0103] This embodiment provides a comprehensive energy system scheduling method taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading interaction mechanism. Figure 1 As shown, the method includes the following steps:

[0104] Step S1: Build an integrated energy system framework.

[0105] like Figure 2 As shown, the integrated energy system of this embodiment is mainly divided into three parts: energy supply side, energy conversion side and load side; among them, the energy supply side includes the upper power grid, natural gas grid and wind power, and the system only purchases electricity from the upper power grid, and all purchased electricity is thermal power generation; the energy conversion side includes gas turbines, waste heat boilers, electric boilers, electric refrigerators, absorption refrigerators and power storage equipment, among which gas turbines and waste heat boilers constitute a cogeneration unit; the load side includes three loads: electricity, heat and cooling.

[0106] Step S2: Construct a green certificate trading model and a stepped carbon trading model.

[0107] In the green certificate trading model of this embodiment, when the green certificates held by the participating system through renewable energy power generation are greater than the assessment standard, the system can sell the remaining green certificates to obtain income; otherwise, the system needs to purchase enough green certificates to complete the assessment. The expression of the green certificate trading model is as follows:

[0108]

[0109] Where: C GCT is the transaction cost of green certificates; c gc G is the unit price of green certificate transaction; re The amount of green certificates generated by wind power; G q The system meets the green certificate quantity required for assessment; re P is the conversion coefficient of wind power into green certificate; WT,t is the wind power; ω q is the green certificate quota coefficient of the system; P N,t is the total power demand of the system.

[0110] In step S2, the stepped carbon trading model guides the system to control its carbon emissions in the form of free carbon emission quotas. When the carbon emissions of the participating system exceed the carbon emission quota, the excess amount needs to purchase additional carbon emission rights from the carbon trading market. The more carbon emission rights need to be purchased, the higher the carbon trading cost. The carbon emission sources come from the cogeneration units in the system and the electricity purchased by the system from the superior power grid. The stepped carbon trading model includes actual carbon emissions, carbon emission quotas and carbon emission rights trading amounts.

[0111] The carbon emission quotas are specifically:

[0112]

[0113] Where: E q is the carbon emission quota of the system; e , κ h are the carbon emission quotas per unit of electricity and heat supply respectively; e,h P is the conversion coefficient of electrical and thermal power conversion; t GT,e is the output power of the gas turbine during period t; P t GT,h is the thermal output power of the gas turbine during period t;

[0114] The actual carbon emissions are specifically:

[0115]

[0116] Where: E r is the actual carbon emission of the system; x1, y1, z1 and x2, y2, z2 are the carbon emission parameters of the coal-fired generator set and gas turbine respectively; P t grid P is the system's power purchase amount during period t; t GTh is the total output power of the gas turbine;

[0117] The carbon emission rights trading amount is specifically:

[0118] E CET =E r -E q

[0119]

[0120] Where: E CET is the actual carbon emission trading volume of IES; C CET is the carbon trading cost; c ce is the unit carbon trading base price; l is the length of the carbon emission interval; θ is the price growth rate.

[0121] See Figure 3 , flow chart of the green certificate-ladder carbon trading interaction mechanism in the embodiment proposed by the present invention.

[0122] Step S3: Construct a green certificate-carbon trading interaction mechanism model.

[0123] In step S3, the life cycle carbon emissions of fossil energy and renewable energy are calculated. The carbon emission equivalent of renewable energy in its entire life cycle is much lower than that of fossil energy. If the system's carbon emission quota is insufficient or the economic situation is not good, the carbon emission reduction contained in the green certificate can offset the carbon emissions; at this time, the green certificate participates in the green certificate market and the carbon trading market at the same time, realizing the interaction between the two.

[0124] Specifically, step S3 includes the following steps:

[0125] Step S31, calculating the green electricity carbon emissions contained in the green certificate;

[0126] Q rc =Q cg -Q re

[0127] G rc =μ rc (G re -G q )

[0128] E of =Q rc ×G rc

[0129] Where: Q rc The carbon emission reduction contained in the green certificate; Q cg , Q re are the carbon emissions equivalent of coal-fired power and renewable energy in their respective energy chain life cycles; G rc 、μ rc are the green certificate quantity and proportion coefficient for offsetting carbon emissions; E of The carbon emissions offset by the green certificates generated by wind power.

[0130] Step S32: Calculate the carbon emission rights trading volume of the system after the interactive mechanism:

[0131] E CET,a =E r -(E q +E of )

[0132] Where: E CET,a is the carbon emission rights trading volume of the system after the interactive mechanism;

[0133] Step S33: Calculate the green certificate trading volume of the system after the interactive mechanism:

[0134] G GCT,a =G q -(G re -G rc )

[0135] Where: G GCT,a Green certificate trading volume of the system after the interactive mechanism.

[0136] Step S4: construct a heating network dynamic model based on the thermal delay and heat loss characteristics of the heating network.

[0137] In step S4, the heating pipeline has heat loss and thermal delay during the transmission process, which will directly affect the scheduling result of IES. In the scheduling of IES, there is a big difference in the transmission inertia of electric energy and thermal energy. Compared with electric energy, thermal energy has thermal delay during the transmission process due to the lag in the temperature change of the heat medium; this feature makes the heating network have similar characteristics to energy storage, which can improve the flexibility of system operation to a certain extent. The heating network consists of four parts: heat source, heating pipeline, heat exchange station and heat load. It can be divided into primary heating network (heat energy transmission part) and secondary heating network (heat energy distribution part). The two transmit heat energy to the user side through the heat exchange station. As Figure 4 As shown in the figure, the secondary pipe network has a shorter length and its dynamic process is less significant. Therefore, only the dynamic characteristics of the primary pipe network are studied. The dynamic characteristics model of the heating network is constructed from the two aspects of thermal delay and heat loss.

[0138] 1) Thermal delay sub-model. Mass regulation is used as the control method of the heating network. Under this control mode, the mass flowing into the pipeline in any scheduling period is equal, that is, the mass flowing into the heating pipeline in each scheduling interval is equal. First, without considering the heat loss of the pipeline, the continuous working medium in the pipeline is divided into several mass blocks with constant temperature, such as Figure 5 As shown. t represents the time period when the last mass flows out of pipe k before the end of time period t; t-δ t represents the period when the last mass flows out of pipe k before the end of period t-1; R t represents t-γ t The total mass of heat medium flowing from time period to time period t. t , δ t 、R t The expression is as follows:

[0139]

[0140] Where: ρ w is the density of the heat medium; A k is the cross-sectional area of ​​the pipe k; L k is the length of the pipeline k; m kis the mass flow rate of the heat medium at pipe k.

[0141] At the same time, B k It also represents the total mass of the heat medium flowing through, but its value is determined by γ k,t and δ k,t The relationship between B k The expression is as follows:

[0142]

[0143] Therefore, the pipe outlet temperature without considering heat loss is It can be obtained by averaging the mass temperature in the gray area at the end of the pipe. The specific expression is as follows:

[0144]

[0145] Where: are the terminal temperatures of the water supply and return pipe k in period t without considering heat loss; are the head end temperatures of the water supply and return pipe k during period t respectively;

[0146] 2) Heat loss sub-model. When the temperature of the heat medium in the pipeline is higher than that of the surrounding environment, heat loss occurs, resulting in a temperature drop. Based on the thermal delay characteristics of the primary heating network described above, the temperature at the pipeline outlet is corrected.

[0147]

[0148] Where: are the terminal temperatures of the supply and return pipes k at time period t respectively; is the ambient temperature outside the pipeline; J k is the heat loss coefficient of pipeline k; λ k is the thermal conductivity of the pipe k; c w is the specific heat capacity of the heat medium.

[0149] In summary, the dynamic characteristic model of the heating network that comprehensively considers thermal delay and heat loss has been established.

[0150] Step S5: Configure the objective function and constraints of the integrated energy system operation, take the minimum total system cost as the goal, and call the CPLEX solver through YALMIP on the Matlab platform to solve the optimization scheduling model.

[0151] In step S5, the IES low-carbon economic dispatch model constructed in this embodiment of the present invention is a mixed-integer nonlinear model. The carbon emission function in step S2 must first be linearized to obtain a mixed-integer linear programming model, which is then solved using YALMIP and the CPLEX solver. The objective function is to minimize the sum of energy purchase costs, equipment operation and maintenance costs, wind curtailment costs, green certificate trading costs, and carbon trading costs; constraints include unit output constraints, power network constraints, heating network constraints, temperature fusion constraints, unit output constraints, and power balance constraints.

[0152] Specifically, the objective function is:

[0153] C IES =min(C buy +C om +C wind +C GCT +C CET )

[0154] Where: C buy is the energy purchase cost; C om C is the equipment operation and maintenance cost; wind is the cost of wind curtailment;

[0155] The specific types are:

[0156]

[0157] Where: C ele,t is the unit electricity price during period t; C gas is the unit gas price; P t GT is the input power of the gas turbine during period t; H vg It is the low calorific value of natural gas; are the operation and maintenance costs of fans, cogeneration units, electric boilers, electric refrigerators, absorption refrigerators, and energy storage equipment, and the operating power during period t; σ wind is the unit wind curtailment cost; P t WT,pre P is the predicted wind power during period t; t WT is the actual wind power output power in period t.

[0158] The specific constraints are:

[0159] ① Power network constraints

[0160]

[0161] P ij,min ≤P ij ≤P ij,max

[0162] Where: P ij is the active power of branch ij; B ij is the admittance of branch ij; θ ij is the voltage phase angle of branch ij; θ i ,θ j are the phase angles of node i and node j respectively; P ij,max 、P ij,min are the upper and lower limits of the branch ij power respectively;

[0163] ② Heating network constraints

[0164]

[0165] T t S,min ≤T t S ≤T t S,max

[0166] T t R,min ≤T t R ≤T t R,max

[0167] Where: P t EB,h 、P t WHB,h are the output thermal power of the electric boiler and waste heat boiler during period t; P t AC is the input thermal power of the absorption chiller during period t; is the heat medium flow rate at the heat source node i; are the supply and return water temperatures of heat source node i during period t, respectively; is the heat load demand of load node i during period t; is the heat medium flow rate at load node i; are the temperatures of heat medium flowing into and out of load node i during period t; T t S,max 、T t S,min are the upper and lower limits of water supply temperature respectively; T t R,max 、T t R,min are the upper and lower limits of return water temperature respectively;

[0168] ③Temperature fusion constraint

[0169]

[0170] Where: is the mixed temperature of the heat medium at node i; m k is the mass flow rate of the heat medium at pipe k; x is the set of the supply network (S) and the return network (R); Φ pipe is a collection of pipelines;

[0171] ④ Unit output constraints

[0172] 1) Wind turbines

[0173] 0≤P t WT ≤P t WT,pre

[0174] Where: P t WT is the actual output power of wind power in period t; P t WT,pre is the predicted wind power during period t;

[0175] 2) Gas turbine

[0176]

[0177] Where: P t GT,e is the output power of the gas turbine during period t; is the gas-to-electricity efficiency of the gas turbine; P t GT,h is the thermal output power of the gas turbine during period t; is the gas-to-heat efficiency of the gas turbine; are the upper and lower limits of gas turbine input power respectively; are the upper and lower limits of the gas turbine climbing power respectively;

[0178] 3) Waste heat boiler

[0179]

[0180] Where: η WHB is the recovery efficiency of the waste heat boiler; P t WHB is the input thermal power of the waste heat boiler during period t; are the upper and lower limits of the waste heat boiler input power respectively;

[0181] 4) Electric boiler

[0182]

[0183] Where: η EB is the energy conversion efficiency of the electric boiler; P t EBis the input power of the electric boiler during period t; They are the upper and lower limits of the electric boiler input power respectively; They are the upper and lower limits of the electric boiler's ramp power respectively;

[0184] 5) Electric Refrigerator

[0185]

[0186] Where: P t EC,c is the cooling output of the electric refrigerator during period t; COP EC is the conversion efficiency of the electric refrigerator; P t EC is the input electrical power of the electric refrigerator; They are the upper and lower limits of EC cooling output respectively; are the upper and lower limits of the electric refrigerator's climbing power respectively;

[0187] 6) Absorption refrigeration machine

[0188]

[0189] Where: P t AC,c is the cooling output of the absorption chiller during period t; COP AC is the conversion efficiency of the absorption chiller; They are the upper and lower limits of the cooling output of the absorption chiller respectively; They are the upper and lower limits of the ramp power of the absorption chiller respectively;

[0190] 7) Battery

[0191]

[0192] Where: E t is the energy stored in the battery during period t; cha ,η dis P is the battery charging and discharging efficiency; t cha 、P t dis are the battery charging and discharging power during period t; ε t is a binary variable, if ε t = 0, the battery discharges energy during period t, if ε t =1, the battery is charged during period t; E1 and E2 are the upper limits of battery charging and discharging power, respectively. T are the energy stored in the battery at the beginning and end of system dispatch; E max 、E minThey are the upper and lower limits of battery capacity respectively;

[0193] ⑤Power balance constraint

[0194]

[0195] Where: A is the correlation matrix between nodes and energy units; B n is the imaginary part of the node admittance matrix; θ t is the voltage phase angle of the node during period t; They are electricity and cooling load demands respectively.

[0196] To verify the effectiveness of the above method, this embodiment uses 24 hours as an optimization scheduling cycle and the scheduling time interval is set to 1 hour. The wind power output, load demand and ambient temperature of the embodiment are shown in FIG. Figure 6 The time-of-use electricity price is shown in Table 1, and the unit price of natural gas is 2.5 yuan / m 3 , the low calorific value is 9.7kWh / m 3 The Green Certificate quota coefficient is 0.2, the carbon emission interval is 200 / t, and the unit wind curtailment cost is 50 yuan / MWh. For the heating network, the supply water temperature range is 80-100°C, the return water temperature range is 60-80°C, and the water flow rate at nodes 4, 5, and 6 is 90 kg / s.

[0197] Table 1 Time-of-use electricity prices

[0198]

[0199]

[0200] The CPLEX solver is called through YALMIP to solve the model, and four scenarios are set for scenario comparison:

[0201] Scenario 1: Economic dispatch of an integrated energy system without considering the dynamic characteristics of the heat network under the green certificate-carbon trading mechanism;

[0202] Scenario 2: Economic dispatch of an integrated energy system taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading mechanism;

[0203] Scenario 3: Economic dispatch of an integrated energy system without considering the dynamic characteristics of the heat network under the green certificate-carbon trading interaction mechanism;

[0204] Scenario 4: Economic dispatch of an integrated energy system taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading interaction mechanism.

[0205] Table 2 below shows the operational results under different scenarios. In terms of carbon emissions, compared to Scenario 1, Scenario 3 reduced carbon emissions by 41.45 tons, a decrease of 2.99%. Compared to Scenario 2, Scenario 4 reduced carbon emissions by 48.74 tons, a decrease of 3.60%. These results indicate that the green certificate-carbon trading interaction mechanism is more effective in reducing the system's carbon emissions than the green certificate-carbon trading mechanism. In terms of economic benefits, although the green certificate transaction cost increased by 15,979.18 yuan in Scenario 3 compared to Scenario 1, it further promoted carbon emission reductions and reduced the total cost by 42,970.63 yuan. Compared to Scenario 2, the green certificate transaction cost in Scenario 4 increased by 16,672.41 yuan, but the total cost of the system was further reduced by 36,273.46 yuan. These results indicate that the green certificate-carbon trading interaction mechanism can effectively reduce the total cost of the system and improve the economic efficiency of system operation.

[0206] In summary, the proposed interactive mechanism can further stimulate the vitality of the green certificate market and the carbon trading market, promote the low-carbon economic operation of IES, and improve the level of wind power consumption.

[0207] Table 2 Operation results in different scenarios

[0208]

[0209] like Figure 7 To verify the impact of the dynamic characteristics of the heating network on the system operation, the wind power consumption curves in different scenarios are shown in Table 2 and Figure 7 Conduct scenario comparison analysis. As shown in Table 2, compared with scenario 1, scenario 2 reduces carbon emissions by 29.12t and wind curtailment rate by 0.79%; compared with scenario 3, scenario 4 reduces carbon emissions by 36.41t and wind curtailment rate by 0.65%, and the total system cost is also reduced in the above scenario comparison. Figure 7 It can be seen that when the dynamic characteristics of the heating network are considered in IES scheduling, the system can absorb more wind power. This is because the natural energy storage properties of heating pipelines increase the flexibility of system operation. Greater flexibility can better balance wind power generation and improve the system's wind power absorption capacity. Furthermore, considering the dynamic characteristics of the heating network can reduce the heat output of cogeneration units and electric boilers during the peak heat load period at night. Since part of the system's carbon emissions come from cogeneration units, the system's carbon emissions are also reduced.

[0210] The above method of this embodiment can be integrated into a scheduling system, and the scheduling system implements the scheduling of the above method through physical structures such as a memory and a controller.

[0211] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0212] It will be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0213] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0214] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0215] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0216] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0217] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A comprehensive energy system scheduling method taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading interactive mechanism, characterized by: The following steps are involved: Build an integrated energy system framework; Based on the integrated energy system framework, a green certificate trading model and a stepped carbon trading model are constructed. Furthermore, by exploring the carbon emission reduction properties of green certificates, a green certificate-carbon trading interaction model is constructed, which uses the green certificate trading volume and carbon emission rights trading volume as interactive media to link the green certificate market and the carbon market. According to the heat delay and heat loss characteristics of the heating network in the integrated energy system framework, a dynamic model of the heating network is constructed; Based on the interactive green certificate market, carbon market and heat network dynamic model, an optimization scheduling model is constructed with the minimum cost of the integrated energy system as the goal, and the solution is obtained, and the scheduling results are output to complete the scheduling process.

2. The integrated energy system scheduling method taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading interactive mechanism according to claim 1 is characterized in that: The integrated energy system framework includes three parts: energy supply side, energy conversion side and load side. The energy supply side includes the upper power grid, natural gas grid and wind power. The energy conversion side includes gas turbines, waste heat boilers, electric boilers, electric refrigerators, absorption refrigerators and power storage equipment. The gas turbines and waste heat boilers constitute a cogeneration unit. The load side includes three loads: electricity, heat and cold.

3. The integrated energy system scheduling method taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading interactive mechanism according to claim 1 is characterized in that: The expression of the green certificate trading model is: Where C GCT is the transaction cost of green certificates; c gc G is the unit price of green certificate transaction; re The amount of green certificates generated by wind power; G q The system meets the green certificate quantity required for assessment; re P is the conversion coefficient of wind power into green certificate; WT,t is the wind power; ω q is the green certificate quota coefficient of the system; P N,t is the total power demand of the system.

4. The integrated energy system scheduling method taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading interactive mechanism according to claim 1 is characterized in that: The tiered carbon trading model includes an actual carbon emission sub-model, a carbon emission quota sub-model, and a carbon emission rights trading quota sub-model, wherein the actual carbon emission sub-model is expressed as: Where, E r is the actual carbon emission; x1, y1, z1 and x2, y2, z2 are the carbon emission parameters of coal-fired generators and gas turbines respectively; P t grid P is the system's power purchase amount during period t; t GTh is the total output power of the gas turbine; P t GTh is the total output power of the gas turbine; P t GT,e is the output power of the gas turbine during period t; P t GT,h is the thermal output power of the gas turbine during period t; The expression of the carbon emission quota sub-model is: Where, E q is the carbon emission quota; e , κ h are the carbon emission quotas per unit of electricity and heat supply respectively; e,h is the conversion factor of electrical and thermal power; The expression of the carbon emission trading quota sub-model is: AND CET =And r -AND q Where, E CET is the actual carbon emission trading volume of the system; C CET is the carbon trading cost; c ce is the unit carbon trading base price; l is the length of the carbon emission interval; θ is the price growth rate.

5. The integrated energy system scheduling method taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading interactive mechanism according to claim 1 is characterized in that: The steps of obtaining the green certificate trading volume and the carbon emission rights trading volume according to the green certificate-carbon trading interaction model include: Calculate the green electricity carbon emissions contained in the green certificate, where the calculation expression is: Q rc =Q cg -Q re G rc =μ rc (G re -G q ) E of =Q rc ×G rc Where Q rc The carbon emission reduction contained in the green certificate; Q cg , Q re are the carbon emissions equivalent of coal-fired power and renewable energy in their respective energy chain life cycles; G rc 、μ rc are the green certificate quantity and proportion coefficient for offsetting carbon emissions; G re The amount of green certificates generated by wind power; G q The amount of green certificates required for the system to meet the assessment requirements; E of Carbon emissions offset by green certificates generated by wind power; Calculate the carbon emission trading volume of the integrated energy system after the interactive mechanism, where the calculation expression is: AND CET,a =And r -(AND q +E of ) Where, E CET,a is the carbon emission trading volume of the integrated energy system after the interactive mechanism; E r is the actual carbon emissions; E q for carbon emission quotas; Calculate the green certificate trading volume of the integrated energy system after the interactive mechanism, where the calculation expression is: G GCT,a =G q -(G re -G rc ) Where G GCT,a Green certificate trading volume of the integrated energy system after the interactive mechanism.

6. The integrated energy system scheduling method taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading interactive mechanism according to claim 1 is characterized in that: The heat network dynamic model includes a heat delay sub-model and a heat loss sub-model, wherein the heat delay sub-model is expressed as: in: Where, are the terminal temperatures of the water supply and return pipe k in period t without considering heat loss, are the head end temperatures of the water supply and return pipe k during period t; γ k,t , δ k,t is the thermal delay correlation coefficient; N is the time set of the heat medium flowing through the pipeline; Δt is the time interval; ρ w is the density of the heat medium; A k is the cross-sectional area of ​​the pipe k; L k is the length of the pipeline k; m k is the mass flow rate of the heat medium at pipe k; R k,t t-γ t The total mass of heat medium flowing from time period to time period t; B k,t For k,t and δ k,t Determine the total mass of heat medium flowing through; The expression of the heat loss sub-model is: Where, are the terminal temperatures of the supply and return pipes k at time period t respectively; is the ambient temperature outside the pipeline; J k is the heat loss coefficient of pipeline k; λ k is the thermal conductivity of the pipe k; c w is the specific heat capacity of the heat medium.

7. The integrated energy system scheduling method taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading interactive mechanism according to claim 1 is characterized in that: The optimization scheduling model includes an objective function and corresponding constraints, wherein the objective function aims to minimize the sum of energy purchase costs, equipment operation and maintenance costs, wind curtailment costs, green certificate trading costs, and carbon trading costs.

8. The integrated energy system scheduling method taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading interactive mechanism according to claim 7 is characterized in that: The expression of the objective function is: C IES =min(C buy +C om +C wind +C GCT +C CET ) in: Where C IES is the total system cost; C buy is the energy purchase cost; C om C is the equipment operation and maintenance cost; wind is the cost of wind curtailment; C GCT is the transaction cost of green certificates; E CET is the actual carbon emission trading volume of the system; C ele,t is the unit electricity price during period t; P t grid C is the system's power purchase amount during period t; gas is the unit gas price; P t GT is the input power of the gas turbine during period t; H vg is the lower calorific value of natural gas; Δt is the time interval; are the operation and maintenance costs of fans, cogeneration units, electric boilers, electric refrigerators, absorption refrigerators, and energy storage equipment, and the operating power during period t; σ wind is the unit wind curtailment cost; P t WT,pre P is the predicted wind power during period t; t WT is the actual wind power output power in period t.

9. The integrated energy system scheduling method taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading interactive mechanism according to claim 7 is characterized in that: The constraints include: ① Power network constraints: P ij,min ≤P ij ≤P ij,max Where, P ij is the active power of branch ij; B ij is the admittance of branch ij; θ ij is the voltage phase angle of branch ij; θ i ,θ j are the phase angles of node i and node j respectively; P ij,max 、P ij,min are the upper and lower limits of the branch ij power respectively; ② Heating network constraints: T t S,min ≤T t S ≤T t S,max T t R,min ≤T t R ≤T t R,max Where, P t EB,h 、P t WHB,h are the output thermal power of the electric boiler and waste heat boiler during period t; P t AC is the input heat power of the absorption chiller during period t; c w is the specific heat capacity of the heat medium; is the heat medium flow rate at the heat source node i; are the supply and return water temperatures of heat source node i during period t, respectively; is the heat load demand of load node i during period t; is the heat medium flow rate at load node i; are the temperatures of heat medium flowing into and out of load node i during period t; T t S,max 、T t S,min are the upper and lower limits of water supply temperature respectively; T t R,max 、T t R,min are the upper and lower limits of return water temperature respectively; T t S is the water supply temperature; T t R is the return water temperature; ③Temperature fusion constraint: Where, is the mixed temperature of the heat medium at node i; m k is the mass flow rate of the heat medium at pipe k; x is the set of the water supply network S and the return network R; Φ pipe For pipeline collection; ④Unit output constraints: 1) Wind turbines 0≤P t WT ≤P t WT,pre Where, P t WT is the actual output power of wind power in period t; P t WT,pre Power forecasting for wind power; 2) Gas turbine Where, P t GT,e is the output power of the gas turbine during period t; is the gas-to-electricity efficiency of the gas turbine; P t GT,h is the thermal output power of the gas turbine during period t; is the gas-to-heat efficiency of the gas turbine; are the upper and lower limits of gas turbine input power respectively; are the upper and lower limits of the gas turbine climbing power respectively; 3) Waste heat boiler Where η WHB is the recovery efficiency of the waste heat boiler; P t WHB is the input thermal power of the waste heat boiler during period t; are the upper and lower limits of the waste heat boiler input power respectively; 4) Electric boiler Where η EB is the energy conversion efficiency of the electric boiler; P t EB is the input power of the electric boiler during period t; They are the upper and lower limits of the electric boiler input power respectively; They are the upper and lower limits of the electric boiler's ramp power respectively; 5) Electric Refrigerator Where, P t EC,c is the cooling output of the electric refrigerator during period t; COP EC is the conversion efficiency of the electric refrigerator; P t EC is the input electrical power of the electric refrigerator; They are the upper and lower limits of EC cooling output respectively; are the upper and lower limits of the electric refrigerator's climbing power respectively; 6) Absorption refrigeration machine Where, P t AC,c is the cooling output of the absorption chiller during period t; COP AC is the conversion efficiency of the absorption chiller; They are the upper and lower limits of the cooling output of the absorption chiller respectively; They are the upper and lower limits of the ramp power of the absorption chiller respectively; 7) Battery Where, E t is the energy stored in the battery during period t; cha ,η dis P is the battery charging and discharging efficiency; t cha 、P t dis are the battery charging and discharging power during period t; ε t is a binary variable, if ε t = 0, the battery discharges energy during period t, if ε t =1, the battery is charged during period t; E1 and E2 are the upper limits of battery charging and discharging power, respectively. T are the energy stored in the battery at the beginning and end of system dispatch; E max 、E min They are the upper and lower limits of battery capacity respectively; ⑤Power balance constraint: Where A is the correlation matrix between nodes and energy units; B n is the imaginary part of the node admittance matrix; θ t is the voltage phase angle of the node during period t; They are electricity and cooling load demands respectively.

10. The integrated energy system scheduling method taking into account the dynamic characteristics of the heat network under the green certificate-carbon trading interactive mechanism according to claim 1 is characterized in that: The CPLEX solver is used to solve the optimization scheduling model.

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