A low-carbon optimization operation method for an integrated energy system based on energy-carbon coupling and carbon emission density

Abstract

The application provides a low-carbon optimal operation method of a comprehensive energy system based on energy-carbon coupling and carbon emission density; initial carbon emission densities of each energy bus in the comprehensive energy system are set; an optimization model with minimization of total operation cost of the system as an objective function is constructed based on the set initial carbon emission densities of each energy bus, and an optimal operation strategy under current iteration is obtained by solving the optimization model; carbon emission densities of output ends of energy conversion equipment in the comprehensive energy system are calculated, since the energy bus is a convergence point of energy flow of the same type to the load, the carbon emission density of the energy bus is set, and the carbon emission density of each energy bus is dynamically updated; convergence discrimination is performed on the energy bus, if the convergence condition is met, the iteration is stopped and the final optimal scheduling result is output, if the convergence condition is not met, the carbon emission density of each energy bus is updated based on a weighted average method, and the iteration is continued, and finally the carbon emission density of the energy bus is determined. The economic and low-carbon collaborative optimal scheduling of the comprehensive energy system under a complex policy background is realized.

Description

Technical Field

[0001] This invention relates to the technical field of integrated energy system scheduling, and in particular to a method for low-carbon optimized operation of integrated energy systems. The main content is to achieve synergistic optimization of the economy and environment of integrated energy systems by accurately tracking the carbon emission flow corresponding to energy within the system. Background Technology

[0002] Currently, global climate change is becoming increasingly severe, making the low-carbon transition of energy systems an urgent priority. Integrated energy systems, due to their high energy efficiency and strong capacity to absorb renewable energy, are considered key to achieving low-carbon goals. However, existing methods for optimizing the operation of integrated energy systems still have significant shortcomings in carbon emission management.

[0003] Traditional methods typically use fixed external grid carbon emission factors and gas emission factors to calculate system carbon emissions, leading to inaccurate carbon emission accounting. For example, invention patent CN120562652A discloses an optimized scheduling method and system for integrated energy systems based on carbon emission factors and wind power forecasting. This method establishes a carbon emission measurement model based on dynamic carbon emission factors by measuring the direct and indirect carbon emissions of the integrated energy system. It extracts meteorological characteristics based on an improved wind power hybrid forecasting model and generates wind power forecasting intervals using kernel density estimation. An economic scheduling model is constructed with the objective function of minimizing the sum of energy purchase costs, carbon trading costs, and wind curtailment costs. Based on the economic scheduling model and the objective function, and under a set of constraints, the optimal scheduling scheme is solved using the dynamic carbon emission measurement interval and the wind power forecasting interval. This solves the technical problems of large carbon emission calculation errors and low wind power forecasting accuracy in the optimized scheduling process of existing integrated energy systems. This static and isolated accounting method cannot capture the dynamic transfer and accumulation effects of carbon emissions during the conversion and transmission of different energy sources within the system. For example, the carbon emissions from electric boilers should be traced back to the source and production process of the electricity they consume, rather than simply multiplying the electricity-to-heat efficiency by a fixed factor. This calculation method leads to carbon cost assessments that do not match reality, affecting the formulation of low-carbon strategies. Real-world carbon emission policies and renewable energy policies often exhibit nonlinear, dynamic, and multi-constraint characteristics. Existing optimization scheduling models struggle to efficiently and accurately integrate these complex policy mechanisms, resulting in optimization results that fail to fully reflect the actual policy impact and hindering the system from achieving optimal operation. These problems limit the realization of true economic and low-carbon synergistic optimization in the practical application of integrated energy systems. Summary of the Invention

[0004] To address the technical problem of inaccurate carbon emission accounting in existing technologies, this invention proposes a low-carbon optimization operation method for integrated energy systems based on energy-carbon coupling and carbon emission density. The aim is to achieve dynamic, accurate attribution, and traceability of carbon emissions from various energy forms within the system through an iterative calculation method for carbon emission density based on energy-carbon coupling. Building upon this accurate carbon emission accounting, it deeply integrates complex policy mechanisms such as tiered carbon taxes and green certificate trading to ensure that the optimization results simultaneously satisfy economic efficiency and clean, low-carbon requirements. Therefore, this invention enables the optimal coordinated scheduling of integrated energy systems for both economic and low-carbon purposes under complex policy backgrounds, improving system operating efficiency and environmental benefits.

[0005] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0006] A low-carbon optimization operation method for an integrated energy system based on energy-carbon coupling and carbon emission density, comprising the following steps:

[0007] S1. Based on the known carbon emission factors of externally purchased electricity and externally purchased gas, set the initial carbon emission density for each energy bus within the integrated energy system.

[0008] S2. Based on the initial carbon emission density of each energy bus set in S1, construct an optimization model with the objective function of minimizing the total operating cost of the system, and solve the optimization model to obtain the optimal operating strategy under the current iteration;

[0009] S3. Based on the optimal operating strategy obtained from S2, calculate the carbon emission density of the energy output at the energy conversion equipment in the integrated energy system, and then dynamically update the carbon emission density of each energy bus according to the energy aggregation characteristics of the energy bus.

[0010] S4. Based on the carbon emission density of each energy bus calculated in S3, perform a convergence judgment. If the convergence condition is met, stop the iteration and output the final optimization result. If the convergence condition is not met, update the carbon emission density of each energy bus based on the weighted average method and return to step S2 to continue the iteration.

[0011] Furthermore, the integrated energy system includes a source side, an energy conversion equipment side, an energy storage equipment side, a load side, and an energy bus side. The source side includes purchased electricity, purchased gas, wind power, and photovoltaic power. The energy conversion equipment side includes a combined heat and power unit (CHP), a gas turbine (GT), a gas boiler (GB), and an electric boiler (EB). The energy storage equipment side includes electrical energy storage, thermal energy storage, and gas energy storage. The load side includes electrical load, thermal load, and gas load. The energy bus includes an electric bus, a thermal bus, and a gas bus.

[0012] Furthermore, the initial carbon emission density setting rule in step S1 is as follows: the initial carbon emission density of the power bus is set to the carbon emission factor of externally purchased electricity, the initial carbon emission density of the heat bus is set to 0, and the initial carbon emission density of the gas bus is set to the carbon emission factor of externally purchased gas.

[0013] Furthermore, in step S2, the optimization model is a mixed-integer linear programming model, and the objective function is:

[0014] ;

[0015] in, This represents the cost of purchasing various energy sources from the source side; This signifies punishment for abandoning wind and light; Indicates the cost of tiered carbon taxes; This indicates the operating and maintenance costs of the equipment; This indicates the start-up and shutdown costs of large generating units; This indicates the profit or cost of trading green certificates.

[0016] Furthermore, the cost of tiered carbon taxes The calculation method is as follows:

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] in, This represents the carbon tax rate during time period t. For the portion of the energy system that exceeds its carbon quota. This indicates the system's original carbon quota. Indicates total carbon emissions; This represents the carbon emissions required to meet the k-th energy load during time period t. This represents the carbon emissions contained in the energy stored in the k-th type of energy storage device during time period t. This represents the carbon emissions contained in the energy released to the k-th type of energy storage device during time period t. This indicates the carbon emissions from equipment wear and tear at CHP. This indicates the carbon emissions from GT's equipment wear and tear. This indicates the carbon emissions from equipment wear and tear in GB. This indicates the carbon emissions from equipment wear and tear on the EB. , , , , , , Let represent the carbon emission density of the bus for the k-th energy source, the carbon emission density of the equipment during the CHP conversion process, the carbon emission density of the equipment during the GT conversion process, the carbon emission density of the equipment during the GB conversion process, the carbon emission density of the equipment during the EB conversion process, the carbon emission density of the charging branch during time period t, and the carbon emission density of the discharging branch during time period t, respectively. , , , , , , These represent the k-th energy load in the system during time period t, the output of the CHP device in the system, the output of the GT device in the system, the output of the GB device in the system, the output of the EB device in the system, the charging power of energy storage, and the charging power of energy release, respectively. , , , , , These represent the carbon quota coefficients corresponding to the output thermal and electrical energy of CHP, GT, GB, and EB, respectively.

[0023] Furthermore, the optimization model described in step S2 also includes constraints, which include at least power balance constraints, equipment operation constraints, and green electricity quota constraints.

[0024] The power balance constraints include at least electrical balance, thermal balance, and gas balance. Electrical balance is defined as the sum of the power input power of the power bus equal to the sum of the power output power of the power bus and the power of the electrical load. Thermal balance is defined as the sum of the power input power of the thermal bus equal to the sum of the power output power of the thermal bus and the power of the thermal load. Gas balance is defined as the sum of the power input power of the gas bus equal to the sum of the power output power of the gas bus and the power of the gas load.

[0025] The equipment operation constraints include at least EB equipment constraints, GB equipment constraints, GT equipment constraints, CHP equipment constraints, WT equipment constraints, PV equipment constraints, and energy storage equipment constraints.

[0026] Furthermore, in step S3, when calculating the carbon emission density at the output of the energy conversion equipment, the energy conversion equipment is divided into two categories: single-input-single-output equipment and single-input-multiple-output equipment, which are calculated separately.

[0027] For single-input, single-output devices, including electric boilers and gas-fired boilers, the method for calculating the outlet carbon emission density is as follows:

[0028] ;

[0029] in, Carbon emission density of power at the export end, Indicates the equipment's operational efficiency. Carbon emission density of the energy bus at the inlet end;

[0030] For single-input multi-output (SIMP) equipment, including gas turbines and combined heat and power (CHP) units, the method for calculating the outlet carbon emission density is as follows:

[0031] ;

[0032] in, Carbon emission density of electrical power at the outlet of the gas turbine / cogeneration plant. This indicates the carbon emission density of the gas at the inlet of the gas turbine / cogeneration plant. The carbon emission density is the thermal power output at the outlet of the gas turbine / cogeneration plant. and The efficiency of natural gas to heat and natural gas to electricity in gas turbine / cogeneration systems.

[0033] Furthermore, the method for dynamically updating the carbon emission density of each energy bus is as follows:

[0034] ;

[0035] Where k represents the type of energy bus; when k=1, it is an electric bus; when k=2, it is a thermal bus; and when k=3, it is a gas bus. Let be the real-time carbon emission density of the k-th energy bus at time t. Let be the carbon emission density of the energy flow input to the k-th type bus at time t. Let be the power of the energy flow input to the i-th type of bus at time t. Represents the total carbon emissions of all input energy flows on the energy bus. This represents the total power of all input energy flows.

[0036] Furthermore, convergence judgment is performed based on the carbon emission densities of each energy bus calculated by S3, including:

[0037] Compare the differences between the carbon emission densities of each energy bus calculated in step S3 and the carbon emission densities of the previous iteration:

[0038] ;

[0039] in, , , These represent the newly calculated carbon emission densities for the power bus, heating bus, and gas bus, respectively. , , These represent the carbon emission densities calculated in one iteration on the power bus, heat bus, and gas bus, respectively.

[0040] if , If the preset convergence threshold is used, it is determined that the convergence condition is met; otherwise, it does not converge.

[0041] Furthermore, the carbon emission density of each energy bus is updated based on a weighted average method, including:

[0042]

[0043]

[0044]

[0045] in, It is a relaxation factor.

[0046] The beneficial effects of this invention are as follows: This invention achieves deep binding between carbon flow and energy flow through a dynamic carbon emission density iterative update mechanism for the three major energy sources: electricity, heat, and gas. It overcomes the limitations of traditional static carbon emission factors and accurately tracks the dynamics of carbon flow during energy conversion and transmission. As carbon flow tracking hubs, the dynamic carbon density of these three major energy sources guides the optimization model to prioritize the consumption of green electricity and select low-carbon energy conversion methods, effectively improving the utilization rate of renewable energy and optimizing the multi-energy complementary energy structure. At the same time, the iterative convergence mechanism ensures the stability and reliability of the optimization results, enabling the system scheduling scheme to meet both physical operation constraints and policy requirements such as green electricity quotas and tiered carbon taxes, ultimately achieving the optimal balance between economic cost and low-carbon environmental protection. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of the method of the present invention.

[0049] Figure 2 This is a structural diagram of the integrated energy system of the present invention.

[0050] Figure 3 The maximum available power output curves for wind power and photovoltaic power are shown in the embodiments of the present invention.

[0051] Figure 4 The actual carbon flow induced by the terminal load in each time period of this embodiment of the invention.

[0052] Figure 5 This is a comparison of the seasonal system operating cost composition and the total carbon emissions of the system in each season according to an embodiment of the present invention.

[0053] Figure 6 The dynamic carbon flow density of the electric busbar under different seasons in this embodiment of the invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] A low-carbon optimization operation method for integrated energy systems based on energy-carbon coupling and carbon emission density is proposed. The integrated energy system includes a source side, an energy conversion equipment side, an energy storage equipment side, a load side, and an energy bus side. The source side includes purchased electricity, purchased gas, wind power, and photovoltaic power. The energy conversion equipment side includes combined heat and power (CHP) units, gas turbines (GT), gas-fired boilers (GB), and electric boilers (EB). The energy storage equipment side includes electrical energy storage, thermal energy storage, and gas energy storage. The load side includes electrical load, thermal load, and gas load. The energy bus includes an electric bus, a thermal bus, and a gas bus. Figure 2 As shown.

[0056] Specifically, source-side modeling includes renewable energy device modeling:

[0057] Photovoltaic generator modeling, output power of photovoltaic generator at time t:

[0058] ;

[0059] Where N is the number of solar panels. It can be viewed as the theoretical maximum output power of the system, which takes into account the number of components, the rated power of a single component, and multiplied by a comprehensive efficiency coefficient. This factor represents the power reduction coefficient due to factors such as photovoltaic panel aging and dust accumulation. In practical applications, it is typically set to 0.9. This refers to the rated power of a single photovoltaic module; Let be the actual solar irradiance at time t. The reference irradiance under standard test conditions. This is a light intensity correction factor, which converts the power under standard conditions to the power under the current actual light intensity. For example, if the actual light intensity is... If the factor is 0.5, then the power will be approximately halved due to the light intensity being halved. It is a temperature correction factor. If the cell temperature... equal to reference temperature If the temperature is above 25°C, then this factor is 1, indicating that temperature has no effect on power. A positive value will result in the entire factor being less than 1, leading to a decrease in final power. The denominator 200 is an empirical coefficient that quantifies the sensitivity of power to temperature changes. This value means that for every 1°C increase in battery temperature, its output power will decrease by approximately [a certain amount]. This coefficient will vary depending on the type of photovoltaic cell.

[0060] Mathematical model of wind power (WT):

[0061]

[0062] in, This refers to the real-time output power of the fan. This refers to the current real-time wind speed;

[0063] when hour: .in This is the cut-in wind speed—when the wind speed is below this value, the wind turbine cannot generate electricity due to insufficient aerodynamic performance, and the output power is 0.

[0064] when hour: .here That is the rated wind speed. This is the rated power. This range is the linear increase segment: as the wind speed increases from the cut-in wind speed to the rated wind speed, the power generation increases linearly from 0 to the rated power.

[0065] when hour: . This refers to the cut-off wind speed. Within this range, the wind speed is high enough that the fan operates at full rated power; when... hour: When the wind speed exceeds the cut-out wind speed, the wind turbine stops generating electricity to avoid damage to its structure, and the output power returns to 0.

[0066] Further, the energy conversion equipment side is modeled as follows:

[0067] In the established model, the combined heat and power (CHP) unit is not only a power generation device, but also a physical center connecting the gas grid, the power grid, and the heat grid. Its physical model can be described as follows:

[0068]

[0069] in, Let t be the electrical output of CHP. Let t be the thermal output of CHP. For CHP input power, This refers to the efficiency of CHP in converting natural gas into electricity. This refers to the efficiency of CHP in converting natural gas into heat energy.

[0070] Gas turbines (GTs), as devices that convert natural gas into heat and electricity, can compensate for some of the shortcomings of CHP in the combustion process. Used in conjunction with CHP, they form a highly efficient, stable, and large-capacity energy conversion device. Their physical model can be described as follows:

[0071]

[0072] in, Let be the electric output of GT at time t. Let t be the thermal output of GT. For GT input power, The efficiency of GT's natural gas conversion into electricity. The efficiency of converting GT natural gas into heat energy.

[0073] A gas-fired boiler (GB) is a device that converts purchased natural gas into heat energy. From an energy utilization perspective, this device is less efficient than a CHP (Gas-fired Power Plant), but it improves the flexibility of system scheduling. Its physical model can be described as follows:

[0074]

[0075] in, Let GB be the heat output at time t. For GB input power, The efficiency of GB gas-to-heat conversion.

[0076] An electric boiler (EB), as a device that converts electrical energy into heat energy, is a typical power-to-heat (P2H) device. More than just a heating device, an electric boiler can convert excess and unstable electrical energy into stable heat energy, thereby achieving wind and solar energy integration and economical system operation. Its physical model can be described as follows:

[0077]

[0078] in, Let EB be the thermal output at time t. For EB input power, The efficiency by which EB converts electrical energy into heat energy.

[0079] Further, the energy storage device is modeled as follows:

[0080] ;

[0081] in, This represents the state of charge of the i-th battery at time t, reflecting the proportion of the battery's remaining charge. This represents the state of charge of the i-th battery at time t-1; This represents the charging power at time t (a positive value, indicating that the battery has absorbed energy). It is the charging efficiency (because there are losses during charging, the actual energy converted into SOC needs to be multiplied by the efficiency). This represents the discharge power at time t; a positive value indicates that the battery has released energy. Because of discharge losses, the actual energy consumed corresponding to the state of charge (SOC) needs to be divided by the efficiency. It is the discharge efficiency; : The maximum SOC corresponding to the capacity of the i-th battery, which can also be understood as the SOC when the battery is fully charged.

[0082] The low-carbon optimized operation method of the integrated energy system, such as Figure 1 As shown, the steps include:

[0083] S1. Based on the known carbon emission factors of externally purchased electricity and externally purchased gas, set the initial carbon emission density for the power bus, heat bus, and gas bus within the integrated energy system.

[0084] In this embodiment of the application, the carbon emission density of each energy bus (power bus, heat bus, gas bus) within the integrated energy system is initialized in this step. These carbon emission densities are used to evaluate the carbon flow and carbon emission costs within the system in the first iteration.

[0085] Specifically, the initial carbon emission density can be expressed as:

[0086] First, the initial carbon emission density of the power bus is set as the carbon emission factor of externally purchased electricity;

[0087] ;

[0088] in, Carbon emission density of power busbars Let T be the carbon emission density of purchased electricity and T be the total dispatch duration. The initial carbon emission density of the thermal bus is set to zero because the thermal energy is mainly produced by internal equipment, and its carbon emissions must be traced back to the source. In the first iteration, this will result in the carbon emissions of the thermal equipment coming entirely from its input energy.

[0089] Furthermore, the initial carbon emission density of the thermal bus is set to 0.

[0090] ;

[0091] in, This represents the carbon emission density of the thermal busbar.

[0092] Furthermore, the initial carbon emission density of the gas bus is set as the carbon emission factor of externally purchased gas:

[0093] ;

[0094] in, The carbon emission density of the gas bus. The carbon emission factor is fixed for purchased natural gas.

[0095] S2. Based on the initial carbon emission densities of the power bus, heat bus, and gas bus set in S1, construct an optimization model with the objective function of minimizing the total operating cost of the system, and solve the optimization model to obtain the optimal operating strategy under the current iteration.

[0096] In this embodiment, the objective function is to minimize the total operating cost of the system during the scheduling period, as follows:

[0097] ;

[0098] in, This represents the cost of purchasing various energy sources from the source side, including the cost of electricity and the cost of gas. This signifies punishment for abandoning wind and light; This refers to the tiered carbon tax cost, which means that when carbon emissions exceed the system's original carbon allowance, the required shortfall will be purchased from the carbon trading market, thus incurring carbon trading penalties. This represents the operating and maintenance costs of all equipment used during the conversion and storage process; This indicates the start-up and shutdown costs of large units such as GT and CHP; This refers to the profit from buying and selling green certificates. When the number of green certificates exceeds the government's quota, green certificates are sold to generate revenue. When the number of green certificates falls below the government's quota, green certificates are purchased from the green certificate trading market to meet the demand.

[0099] Among them, the cost of purchasing various energy sources from the source side As shown below:

[0100] ;

[0101] in, This indicates the system's electricity purchase cost. This indicates the system's gas purchase cost. This indicates the time-of-use electricity price for a given moment within a 24-hour period. This indicates the amount of electricity purchased from the power grid at the corresponding time within a 24-hour period. This indicates the hourly gas price for a given moment within a 24-hour period. This indicates the amount of gas purchased from the gas network at the corresponding time within 24 hours.

[0102] Furthermore, penalties for abandoning wind and solar power. As shown below:

[0103] ;

[0104] in, As punishment for abandoning light, As punishment for abandoning the wind, , This refers to the penalty coefficient for wind and solar power curtailment. The predicted output of photovoltaic power generation during time period t. The actual output of photovoltaic power generation during time period t. The predicted output of wind power generation during time period t. This represents the actual output of photovoltaic power generation during time period t.

[0105] Furthermore, based on the carbon flow model accompanying the flow of electricity, heat, and gas, a carbon tax mechanism is added. Taxing the carbon emissions of individuals or businesses makes implementation more flexible, thereby achieving low-carbon management of integrated energy and minimizing costs. The establishment of the energy-carbon coupling model makes calculation and research more convenient. (Tiered carbon tax costs) As shown below:

[0106] ;

[0107] in, This represents the carbon tax rate during time period t. For the portion of the carbon quota that the energy system exceeds, a carbon tax needs to be levied on the excess. This indicates the system's original carbon quota. This represents total carbon emissions.

[0108] ;

[0109] in, This represents the carbon tax rate during time period t.

[0110] Total carbon emissions:

[0111] ;

[0112] in, This represents the carbon emissions during time period t to meet the k-th energy load (k=1 for electricity, k=2 for natural gas, and k=3 for heat). This represents the carbon emissions contained in the energy stored in the k-th type of energy storage device during time period t. This represents the carbon emissions contained in the energy released to the k-th type of energy storage device during time period t. This indicates the carbon emissions from equipment wear and tear at CHP. This indicates the carbon emissions from GT's equipment wear and tear. This indicates the carbon emissions from equipment wear and tear in GB. This indicates the carbon emissions from equipment wear and tear on the EB.

[0113] in:

[0114] ;

[0115] in, , , , , , , Let represent the carbon emission density of the bus for the k-th energy source, the carbon emission density of the equipment during the CHP conversion process, the carbon emission density of the equipment during the GT conversion process, the carbon emission density of the equipment during the GB conversion process, the carbon emission density of the equipment during the EB conversion process, the carbon emission density of the charging branch during time period t, and the carbon emission density of the discharging branch during time period t, respectively. , , , , , , These represent the k-th energy load in the system during time period t, the output of the CHP device in the system, the output of the GT device in the system, the output of the GB device in the system, the output of the EB device in the system, the charging power of energy storage, and the charging power of energy release, respectively.

[0116] Carbon quotas:

[0117] ;

[0118] in, , , , , , These represent the carbon quota coefficients corresponding to the output thermal and electrical energy of CHP, GT, GB, and EB, respectively.

[0119] Furthermore, the operation and maintenance costs of all equipment during the conversion and storage process. As shown below:

[0120] ;

[0121] in, This indicates the operating and maintenance costs of wind turbine units. This indicates the operating and maintenance costs of photovoltaic power generation units. This indicates the operating and maintenance costs of the remaining equipment.

[0122] ;

[0123] in, Indicates the number of wind turbine units. Indicates the price of wind power. Indicates the first Typhoon generator maintenance cost coefficient Indicates the first The power generation capacity of the generator unit during the typhoon period t.

[0124] ;

[0125] in, Indicates the number of wind turbine units. Indicates the price of wind power. Indicates the first Typhoon generator maintenance cost coefficient Indicates the first The power generation capacity of the generator unit during the typhoon period t.

[0126] The operating and maintenance costs of other equipment are positively correlated with their power output.

[0127] ;

[0128] in, This represents the maintenance cost per unit power of device i. Let i be the power output of device i. The unit is yuan / kW.

[0129] Furthermore, the start-up and shutdown costs of large units like GT and CHP... As shown below:

[0130] ;

[0131] in, This indicates the cost of starting CHP once. This indicates the start / stop status of CHP. =1 indicates startup; This indicates the cost of starting GT once. This indicates the start / stop status of GT. =1 indicates startup.

[0132] Furthermore, the green certificate quota system mandates that renewable energy generation must reach a certain percentage of total electricity consumption within a dispatch cycle. Failure to meet this percentage will result in economic penalties. The profit from buying and selling green certificates, i.e., the transaction cost, is as follows:

[0133] ;

[0134] in, , These represent the purchase and sale prices of the green certificate, respectively. , This indicates the number of green certificates required by the system and the number of green certificates the system can provide.

[0135] In this embodiment of the application, the constraints include at least power balance constraints, equipment operation constraints, and green electricity quota constraints, as detailed below:

[0136] The power balance constraints include at least electrical balance, thermal balance, and gas balance.

[0137] Electrical balance is defined as the sum of the power input to the power bus equal to the sum of the power output to the power bus and the power of the electrical load.

[0138] ;

[0139] in, The power input from the power grid to the power bus, The wind power input from the wind turbine generator (WT) to the power bus. Photovoltaic power generation input from photovoltaic (PV) modules to the power bus. The power input to the power bus of a combined heat and power (CHP) unit. The power generated by the gas turbine (GT) and input to the power bus. This refers to the power output to the power bus when the energy storage device discharges. The total electrical load power consumed on the power bus, The power consumption of the electric boiler (EB). The power absorbed from the power bus when the energy storage device is charging.

[0140] Thermal balance is achieved when the total input power to the thermal bus equals the sum of the total output power to the thermal bus and the sum of the heat load power.

[0141] ;

[0142] in, The power input from the combined heat and power (CHP) unit to the heat bus, The power input from the electric boiler (EB) to the heat bus, The power input from the gas turbine (GT) to the thermal bus, The power input to the heat busbar of a gas-fired boiler (GB) The heat output power of the heat storage device to the thermal bus at time t is the heat power released by the heat storage device. The heat load power borne by the thermal bus at time t This refers to the heat power absorbed by the thermal bus during the charging of the thermal storage device at time t.

[0143] Gas balance is defined as the sum of the input power of the gas bus equal to the sum of the output power of the gas bus and the gas load power.

[0144] ;

[0145] in, The gas power input from the natural gas pipeline network to the gas bus at time t, The gas power output by the gas storage device to the gas bus at time t is the gas discharge rate. The gas load power borne by the gas bus at time t The gas power consumed by a gas-fired boiler (GB) at time t, The gas power consumed by a combined heat and power (CHP) unit at time t, The gas power consumed by the gas turbine (GT) at time t, This refers to the gas power absorbed by the gas bus when the gas storage device is being filled at time t.

[0146] The equipment operation constraints include at least EB equipment constraints, GB equipment constraints, GT equipment constraints, CHP equipment constraints, WT equipment constraints, PV equipment constraints, and energy storage equipment constraints.

[0147] EB equipment constraints:

[0148] ;

[0149] GB equipment constraints:

[0150] ;

[0151] GT device constraints:

[0152] ;

[0153] in, This is the upper limit of GT power. , These represent the maximum landslide rate and climbing rate of the GT air power, respectively.

[0154] CHP equipment constraints:

[0155] ;

[0156] In the formula, This represents the upper limit of CHP power. , These represent the maximum landslide rate and the climbing rate of the CHP gas power, respectively.

[0157] WT equipment constraints:

[0158] ;

[0159] in, This represents the predicted maximum photovoltaic output at time t.

[0160] PV device constraints:

[0161] ;

[0162] in, This represents the predicted maximum photovoltaic output at time t.

[0163] Constraints of energy storage devices:

[0164] ;

[0165] in, This represents the maximum capacity ratio of a single energy storage device, taken as 0.3. and These represent the upper and lower limits of energy storage for the energy storage device, respectively, and are taken as 0.1 and 0.9. and These represent the single charge / discharge capacity of the energy storage device. and These represent the capacity and rated capacity of the energy storage device, respectively.

[0166] The core idea of ​​the green electricity quota system is to mandate that, within a dispatch cycle, the generation of renewable energy (green electricity) must reach a certain proportion of total electricity consumption. Failure to meet this proportion will result in economic penalties. The green electricity quota constraints are as follows:

[0167]

[0168] in, Indicates the green electricity quota ratio and the total green electricity supply. Total green certificate demand .

[0169] In this embodiment of the application, the constructed mixed integer linear programming (MILP) model is input to the optimization solver to obtain the optimal running strategy and the values ​​of each decision variable under the current iteration.

[0170] S3. Based on the optimal operating strategy obtained from S2, calculate the carbon emission density at the output end of the energy conversion equipment in the integrated energy system, and then dynamically update the carbon emission density of each energy bus according to the energy convergence characteristics of the energy bus (since the energy bus is the convergence point of various energy flows to the load).

[0171] Carbon emissions are a key factor in integrated energy dispatch. Integrated energy systems meet load demands for different forms of energy through consumption, conversion, transmission, storage, and supply. The carbon emission flow introduced with the energy flow is also transformed between different devices in the integrated energy system, resulting in differentiated carbon emission flows at the inlet and outlet ends of individual devices. To accurately describe this carbon emission flow, this invention introduces carbon intensity (CI) to describe this physical quantity. Carbon intensity (CI) represents the amount of carbon emissions contained in a unit of corresponding energy flow. The unit of carbon intensity (CI) is tCO2 / MWh, indicating how many tCO2 of carbon emissions accompany every 1 MWh of energy. Port carbon intensity (PCI) represents the average carbon emissions associated with a unit of energy input or output from an energy component. If the traditional carbon emission density is used to calculate the carbon emission of the output end by multiplying it by the carbon emission factor, the carbon emission of equipment loss will not be calculated. Alternatively, multiplying only the input end by the carbon emission factor will result in the carbon emission of energy storage equipment and load being uncalculated. However, by adding the bus mixed carbon emission density, the carbon emission of all equipment in the entire system can be calculated, which greatly improves the accuracy of carbon emission calculation.

[0172] In this embodiment, an auxiliary carbon density calculation is first performed: the carbon emission density output by the internal energy conversion device is calculated. In step S3, when calculating the carbon emission density at the output of the energy conversion device, the energy conversion device is divided into two categories: single-input-single-output devices and single-input-multiple-output devices, which are calculated separately.

[0173] For single-input, single-output devices, including electric boilers (EB) and gas-fired boilers (GB), the method for calculating the outlet carbon emission density is as follows:

[0174] ;

[0175] in, Carbon emission density of power at the export end, Indicates the equipment's operational efficiency. The carbon emission density of the energy bus at the inlet end.

[0176] For single-input multiple-output (SIMO) equipment, including gas turbines (GT) and combined heat and power (CHP) systems, the method for calculating the outlet carbon emission density is as follows:

[0177] ;

[0178] in, Carbon emission density of electrical power at the outlet of the gas turbine / cogeneration plant. This indicates the carbon emission density of the gas at the inlet of the gas turbine / cogeneration plant. The carbon emission density is the thermal power output at the outlet of the gas turbine / cogeneration plant. and The efficiency of natural gas to heat and natural gas to electricity in gas turbine / cogeneration systems.

[0179] In this embodiment of the application, the method for dynamically updating the carbon emission density of each energy bus is as follows:

[0180] ;

[0181] Where k represents the type of energy bus; when k=1, it is an electric bus; when k=2, it is a thermal bus; and when k=3, it is a gas bus. Let be the real-time carbon emission density of the k-th energy bus at time t. Let be the carbon emission density of the energy flow (output of each device) input to the i-th bus at time t, which is the carbon emission density of the power output at the device outlet calculated above. Let be the power of the energy flow (from each device) input to the i-th type of bus at time t. Represents the total carbon emissions of all input energy flows on the energy bus. This represents the total power of all input energy flows.

[0182] S4. Based on the carbon emission density of each energy bus calculated in S3, perform a convergence judgment. If the convergence condition is met, stop the iteration and output the final optimization result. Otherwise, update the carbon emission density of each energy bus based on the weighted average method and return to step S2 to continue the iteration.

[0183] In this embodiment of the application, the convergence determination of the carbon emission density of each energy bus calculated based on S3 includes:

[0184] Compare the differences between the carbon emission densities of each energy bus calculated in step S3 and the carbon emission densities of the previous iteration:

[0185] ;

[0186] in, , , These represent the carbon emission densities calculated in this iteration for the power bus, thermal bus, and gas bus (in the optimization model). ), , , These represent the carbon emission densities calculated in one iteration on the power bus, heat bus, and gas bus, respectively, with the initial values ​​being the first values.

[0187] Iterative updates and convergence criteria:

[0188] if , If the preset convergence threshold is met, the model is considered to have converged, the iteration stops, and the final optimization result is output.

[0189] Otherwise, the carbon emission density used for the next iteration is updated using a weighted average method to improve iteration stability.

[0190] In this embodiment of the application, updating the carbon emission density of each energy bus based on a weighted average method includes:

[0191] ;

[0192] ;

[0193] ;

[0194] in, This is the relaxation factor. Then, proceed to S2, and continue with the next iteration of optimization until the convergence condition is met or the maximum number of iterations is reached.

[0195] This invention verifies the 24-hour optimized scheduling of a typical integrated energy system comprising electric, heat, gas loads, and various energy devices. To verify the practical application effect of the green electricity optimized scheduling method for integrated energy systems based on energy-carbon coupling, the following case study is analyzed and studied. Tables 1-5 below contain specific data from the case study. Additionally, the maximum available output curves for wind and solar power are provided, as shown below. Figure 3 As shown, the curves of the maximum output power of wind and photovoltaic generators over time are displayed during the optimized scheduling cycle of the integrated energy system, intuitively reflecting the temporal fluctuations and peak-valley characteristics of wind and solar power output.

[0196] Table 1. Time-of-use electricity price (RMB / kW)

[0197]

[0198] Table 2. Hourly Gas Prices (RMB / m3)

[0199]

[0200] The pricing method for natural gas will be changed from being based on volume (yuan / cubic meter) to being based on energy (yuan / kilowatt-hour) coefficient: 10 cubic meters / kilowatt-hour.

[0201] Table 3 Operating costs of each piece of equipment (RMB / kW)

[0202]

[0203] Table 4 Conversion Efficiency of Each Device

[0204]

[0205] Table 5 Maximum and minimum output of each device

[0206]

[0207] (iv) Carbon Flow Tracking and Carbon Emission Density Analysis

[0208] like Figure 4 As shown, by comparing the actual load carbon footprint based on dynamic carbon flow tracking in an integrated energy system with the theoretical total carbon emissions calculated using traditional fixed-factor accounting, it is evident that the traditional static method neglects the real-time absorption of new energy sources such as wind and solar power within the system and the benefits of multi-energy complementarity, thus significantly overestimating the actual carbon emissions for most periods. Dynamic carbon flow tracking, on the other hand, not only accurately describes the emission contributions of electricity, heat, and gas loads but also profoundly reveals the decrease in carbon emission intensity during periods of high new energy generation. This not only strongly confirms the low-carbon superiority of integrated energy coordinated dispatch but also provides a scientific standard for the precise implementation of end-user carbon tax collection, green certificate trading, and guiding users to actively absorb wind and solar power demand-side response. Figure 6 As shown, the dynamic change curves of carbon emission density of power busbars during the 24-hour dispatch period are displayed in different seasons such as summer, winter, and transition season, reflecting the iterative update results of carbon flow density under energy-carbon coupling.

[0209] (V) Analysis of Equipment Operation and Energy Balance under Different Mechanisms

[0210] Case 1: Traditional Economic Dispatch

[0211] Case 1 employs a traditional economic dispatch model, with minimizing system operating costs as the sole optimization objective. Under this model, fossil fuels dominate the system's energy supply, with heat load heavily reliant on gas-fired boilers and turbines, and significant amounts of electricity needing to be purchased from the upper-level grid to meet demand. Due to a lack of market incentives for renewable energy consumption, the system's renewable energy utilization rate is low. Ultimately, this results in a high overall carbon emission level, with renewable energy penetration remaining at a low level, failing to achieve the goal of low-carbon operation.

[0212] Case 2: Single Carbon Trading Mechanism

[0213] Case 2 introduced a single carbon trading mechanism based on economic dispatch. Carbon emission cost constraints forced the system to proactively reduce fossil fuel consumption, resulting in a slight decrease in total system carbon emissions. However, due to the lack of positive incentives for renewable energy, the system's energy structure did not fundamentally change, remaining highly dependent on natural gas power generation and electricity purchased from the external grid. To meet carbon emission constraints, the system incurred high emission reduction costs, and the space for dispatch optimization was very limited, allowing only minor adjustments to operating modes within the cost boundary. This made it difficult to achieve synergistic optimization of economic efficiency and low-carbon practices, resulting in poor low-carbon transformation effects.

[0214] Case 3: Joint Mechanism of Carbon Trading + Green Certificate Trading

[0215] Case 3 employs a joint operation mechanism that integrates carbon trading and green certificate trading. Driven by both carbon cost constraints and green certificate revenue incentives, the overall system operation strategy is significantly optimized. The system proactively utilizes electric boilers to absorb excess wind and solar power, replacing fossil fuel heating from gas-fired boilers with clean electric heating, directly and significantly reducing the system's natural gas procurement and fossil fuel consumption. Simultaneously, the level of renewable energy absorption is significantly improved; wind and solar power that might otherwise be abandoned can be converted into green certificates to participate in market trading and generate revenue, creating a win-win situation for renewable energy absorption, carbon emission reduction, and economic benefits.

[0216] Conclusion: Through comparative analysis of multiple typical cases and simulation of multi-seasonal scenarios, this invention verifies the effectiveness and superiority of the proposed method. The core conclusions can be summarized in the following four aspects:

[0217] First, the combined mechanism of carbon trading and green certificate trading can achieve a synergistic improvement in both environmental and economic benefits. Compared with traditional dispatch models and single carbon trading mechanisms, the combined mechanism, driven by both carbon cost constraints and green certificate revenue incentives, not only curbs high-carbon energy consumption and reduces total carbon emissions through carbon trading, but also incentivizes renewable energy consumption and increases the proportion of clean energy through green certificate revenue incentives. Results show that under the combined mechanism, system carbon emissions are reduced by approximately 15.38% compared to traditional dispatch, the proportion of renewable energy increases from 35.40% to 38.12%, and the total operating cost further decreases by approximately 2.59% compared to the single carbon trading mechanism, achieving a win-win situation of emission reduction and cost reduction.

[0218] Secondly, the refined energy flow-carbon flow coupled tracking model provides precise quantitative support for low-carbon scheduling. Compared to traditional fixed carbon intensity accounting methods, iterative carbon flow tracking can track energy flow and carbon emission transfer processes in real time, avoiding overestimation of carbon emissions and providing a reliable basis for optimal system scheduling and energy structure optimization. Equipment-level carbon flow accounting can also clearly distinguish the carbon emission differences of various energy conversion devices such as CHP, GT, EB, and GB, providing quantitative guidance for targeted emission reduction and low-carbon transformation.

[0219] Furthermore, multi-type energy storage systems are core equipment for achieving high-proportion renewable energy consumption and stable system operation. Electric energy storage optimizes energy allocation across time periods through nighttime charging and daytime discharging, effectively mitigating fluctuations in wind and solar power output. Thermal and gas energy storage closely track changes in heating, cooling, and gas loads, complementing combined heat and power units and boiler equipment to stabilize power output and reduce frequent equipment start-ups and shutdowns. Under the joint trading mechanism, the coordinated operation of energy storage and multi-energy flow conversion equipment transforms fluctuating clean energy into a stable and controllable energy supply, providing a crucial guarantee for the integration of high-proportion renewable energy.

[0220] Finally, the composition of system operating costs and the total carbon emissions of the system in each season are presented, such as... Figure 5 As shown, the proposed model exhibits good seasonal adaptability and robustness. Comparisons of typical summer, typical winter, and transitional season scenarios demonstrate that the model can flexibly adjust its scheduling strategy according to the differences in source-load characteristics across seasons: in winter, relying on CHP (Consumer Power Plant) for heat-driven power generation and electric boilers to absorb wind power, a high utilization rate of new energy sources can still be achieved under high heating demand; in summer, photovoltaic and energy storage work together to support peak electricity consumption and control carbon emission levels; the transitional season shows optimal source-load matching, highest equipment operating efficiency, and the best performance in terms of renewable energy proportion and economic efficiency. This indicates that the model can adapt to annual load fluctuations and achieve stable, efficient, and low-carbon operation in different seasonal scenarios.

[0221] In summary, the joint scheduling model and carbon flow tracking method proposed in this paper demonstrate significant advantages in improving renewable energy consumption, reducing carbon emissions, and optimizing operating costs. They can provide theoretical support and technical reference for the low-carbon transformation and market-oriented operation of integrated energy systems. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A low-carbon optimized operation method for a comprehensive energy system based on energy-carbon coupling and carbon emission density, characterized in that, The steps are as follows: S1. Based on the known carbon emission factors of externally purchased electricity and externally purchased gas, set the initial carbon emission density for each energy bus within the integrated energy system. S2. Based on the initial carbon emission density of each energy bus set in S1, construct an optimization model with the objective function of minimizing the total operating cost of the system, and solve the optimization model to obtain the optimal operating strategy under the current iteration; S3. Based on the optimal operating strategy obtained from S2, calculate the carbon emission density of the energy output at the energy conversion equipment in the integrated energy system, and then dynamically update the carbon emission density of each energy bus according to the energy aggregation characteristics of the energy bus. S4. Based on the carbon emission density of each energy bus calculated in S3, perform a convergence judgment. If the convergence condition is met, stop the iteration and output the final optimization result. If the convergence condition is not met, update the carbon emission density of each energy bus based on the weighted average method and return to step S2 to continue the iteration.

2. The low-carbon optimized operation method for a comprehensive energy system based on energy-carbon coupling and carbon emission density according to claim 1, characterized in that, The integrated energy system includes a source side, an energy conversion equipment side, an energy storage equipment side, a load side, and an energy bus side. The source side includes purchased electricity, purchased gas, wind power, and photovoltaic power. The energy conversion equipment side includes a combined heat and power (CHP) unit, a gas turbine (GT), a gas boiler (GB), and an electric boiler (EB). The energy storage equipment side includes electrical energy storage, thermal energy storage, and gas energy storage. The load side includes electrical load, thermal load, and gas load. The energy bus includes an electric bus, a thermal bus, and a gas bus.

3. The low-carbon optimized operation method for a comprehensive energy system based on energy-carbon coupling and carbon emission density according to claim 2, characterized in that, The initial carbon emission density setting rule in step S1 is as follows: the initial carbon emission density of the power bus is set to the carbon emission factor of externally purchased electricity, the initial carbon emission density of the heat bus is set to 0, and the initial carbon emission density of the gas bus is set to the carbon emission factor of externally purchased gas.

4. The low-carbon optimized operation method for a comprehensive energy system based on energy-carbon coupling and carbon emission density according to claim 3, characterized in that, In step S2, the optimization model is a mixed-integer linear programming model, and the objective function is: ； in, This represents the cost of purchasing various energy sources from the source side; This signifies punishment for abandoning wind and light; Indicates the cost of tiered carbon taxes; This indicates the operating and maintenance costs of the equipment; This indicates the start-up and shutdown costs of large generating units; This indicates the profit or cost of trading green certificates.

5. The low-carbon optimized operation method for a comprehensive energy system based on energy-carbon coupling and carbon emission density according to claim 4, characterized in that, Tiered carbon tax costs The calculation method is as follows: ； ； ； ； ； in, This represents the carbon tax rate during time period t. For the portion of the energy system that exceeds its carbon quota. This indicates the system's original carbon quota. Indicates total carbon emissions; This represents the carbon emissions required to meet the k-th energy load during time period t. This represents the carbon emissions contained in the energy stored in the k-th type of energy storage device during time period t. This represents the carbon emissions contained in the energy released to the k-th type of energy storage device during time period t. This indicates the carbon emissions from equipment wear and tear at CHP. This indicates the carbon emissions from GT's equipment wear and tear. This indicates the carbon emissions from equipment wear and tear in GB. This indicates the carbon emissions from equipment wear and tear on the EB. , , , , , , Let represent the carbon emission density of the bus for the k-th energy source, the carbon emission density of the equipment during the CHP conversion process, the carbon emission density of the equipment during the GT conversion process, the carbon emission density of the equipment during the GB conversion process, the carbon emission density of the equipment during the EB conversion process, the carbon emission density of the charging branch during time period t, and the carbon emission density of the discharging branch during time period t, respectively. , , , , , , These represent the k-th energy load in the system during time period t, the output of the CHP device in the system, the output of the GT device in the system, the output of the GB device in the system, the output of the EB device in the system, the charging power of energy storage, and the charging power of energy release, respectively. , , , , , These represent the carbon quota coefficients corresponding to the output thermal and electrical energy of CHP, GT, GB, and EB, respectively.

6. The low-carbon optimized operation method for an integrated energy system based on energy-carbon coupling and carbon emission density according to any one of claims 1-5, characterized in that, The optimization model described in step S2 also includes constraints, which include at least power balance constraints, equipment operation constraints, and green electricity quota constraints. The power balance constraints include at least electrical balance, thermal balance, and gas balance. Electrical balance is defined as the sum of the power input power of the power bus equal to the sum of the power output power of the power bus and the power of the electrical load. Thermal balance is defined as the sum of the power input power of the thermal bus equal to the sum of the power output power of the thermal bus and the power of the thermal load. Gas balance is defined as the sum of the power input power of the gas bus equal to the sum of the power output power of the gas bus and the power of the gas load. The equipment operation constraints include at least EB equipment constraints, GB equipment constraints, GT equipment constraints, CHP equipment constraints, WT equipment constraints, PV equipment constraints, and energy storage equipment constraints.

7. The low-carbon optimized operation method for a comprehensive energy system based on energy-carbon coupling and carbon emission density according to claim 6, characterized in that, In step S3, when calculating the carbon emission density at the output of the energy conversion equipment, the energy conversion equipment is divided into two categories: single-input-single-output equipment and single-input-multiple-output equipment, which are calculated separately. For single-input, single-output devices, including electric boilers and gas-fired boilers, the method for calculating the outlet carbon emission density is as follows: ； in, Carbon emission density of power at the export end, Indicates the equipment's operational efficiency. Carbon emission density of the energy bus at the inlet end; For single-input multi-output (SIMP) equipment, including gas turbines and combined heat and power (CHP) units, the method for calculating the outlet carbon emission density is as follows: ； in, Carbon emission density of electrical power at the outlet of the gas turbine / cogeneration plant. This indicates the carbon emission density of the gas at the inlet of the gas turbine / cogeneration plant. The carbon emission density is the thermal power output at the outlet of the gas turbine / cogeneration plant. and The efficiency of natural gas to heat and natural gas to electricity in gas turbine / cogeneration systems.

8. The low-carbon optimized operation method for a comprehensive energy system based on energy-carbon coupling and carbon emission density according to claim 7, characterized in that, The method for dynamically updating the carbon emission density of each energy bus is as follows: ； Where k represents the type of energy bus; when k=1, it is an electric bus; when k=2, it is a thermal bus; and when k=3, it is a gas bus. Let be the real-time carbon emission density of the k-th energy bus at time t. Let be the carbon emission density of the energy flow input to the k-th type bus at time t. Let be the power of the energy flow input to the i-th type of bus at time t. Represents the total carbon emissions of all input energy flows on the energy bus. This represents the total power of all input energy flows.

9. The low-carbon optimized operation method for a comprehensive energy system based on energy-carbon coupling and carbon emission density according to claim 8, characterized in that, Convergence judgment is performed based on the carbon emission densities of each energy bus calculated using S3, including: Compare the differences between the carbon emission densities of each energy bus calculated in step S3 and the carbon emission densities of the previous iteration: ； in, , , These represent the carbon emission densities calculated in this iteration for the power bus, thermal bus, and gas bus, respectively. , , These represent the carbon emission densities calculated in one iteration on the power bus, heat bus, and gas bus, respectively. if , If the preset convergence threshold is used, it is determined that the convergence condition is met; otherwise, it does not converge.

10. The low-carbon optimized operation method for a comprehensive energy system based on energy-carbon coupling and carbon emission density according to claim 9, characterized in that, The carbon emission density of each energy bus is updated based on a weighted average method, including: ； ； ； in, It is a relaxation factor.

Images