Comprehensive energy system optimization operation system and method considering demand response under carbon transaction mechanism

Abstract

The invention discloses an integrated energy system optimization operation system and method considering demand response under a carbon transaction mechanism. The method comprises the steps of optimizing an initial electrical load value and an initial thermal load value of an integrated energy system according to a preset price elasticity matrix and an adjustment weight coefficient to obtain a corresponding electrical load optimization value and a thermal load optimization value; equivalently optimizing the electrical load optimization value and the thermal load optimization value by using a preset equivalent replacement proportionality coefficient to obtain an equivalent electrical load optimization value and an equivalent thermal load optimization value; the total carbon emission amount is obtained according to the output power of the gas equipment and the unit power emission factor, and the carbon market transaction cost corresponding to the total carbon emission amount is obtained through a carbon transaction cost expression; constructing a total cost minimization objective function according to the operation and maintenance cost of all the devices, the preset energy purchase cost and the carbon market transaction cost; and setting a constraint function optimization total cost minimization objective function based on the basic operation data, the equivalent electric load optimization values and the equivalent thermal load optimization values of all the equipment.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system optimization scheduling and low-carbon energy management, specifically to an integrated energy system optimization operation system and method that considers demand response under a carbon trading mechanism. Background Technology

[0002] Integrated Energy Systems (IES) achieve cascaded energy utilization and overall operational efficiency improvement through the coordinated supply and coupling conversion of multiple energy forms such as electricity, heat, and gas, serving as a crucial supporting technology for building a low-carbon energy structure. In traditional IES optimization operation models, the system operation objective is typically to minimize the sum of energy purchase costs and operation and maintenance costs, treating user-side electricity and heat loads as fixed and unadjustable rigid demands. In this approach, the system achieves balance by adjusting the output of equipment such as combined heat and power (CHP) units, gas boilers (GB), and electric heat pumps (HP), but fails to utilize the potential energy flexibility of the user side. With the advancement of carbon emission control policies, carbon emission trading mechanisms are gradually being introduced into integrated energy system dispatch. Based on carbon emission factors, the energy consumption of gas turbines (GT) and GB is converted into CO2 emissions, forming carbon trading costs, which are then incorporated into the optimization objective function. This method can reflect the differences in carbon costs under different unit operating states, but it still treats the load as rigid, thus failing to fully leverage the demand-side peak shaving, load shifting, and energy substitution capabilities under carbon emission constraints. On the other hand, demand response (DR) technology can guide user-side load to adaptively adjust through price signals. A typical DR model divides the load into reducible load (CL), transferable load (SL), and replaceable load (RL), and optimizes fixed load adjustments based on the price elasticity matrix and the electricity-heat substitution coefficient, thereby improving the load curve. However, such models typically only aim to minimize economic costs and do not jointly model carbon emission costs with the DR adjustment mechanism, resulting in the synergistic effect of DR in reducing carbon emissions not being effectively realized. Furthermore, while energy storage systems can provide operational flexibility through peak shaving and off-peak electricity storage, existing models often fail to simultaneously consider the dynamic evolution characteristics of electrical and thermal energy storage and the mutual exclusion mechanism of charging and discharging, preventing energy storage potential from forming a synergistic optimization relationship with DR and carbon trading mechanisms.

[0003] In summary, existing technologies have the following shortcomings: 1) Models that only consider carbon trading do not take into account the adjustable characteristics of demand response (DR), resulting in insufficient system flexibility; 2) Models that only consider DR do not consider carbon emissions and carbon trading costs, and their optimization objectives lack low-carbon characteristics; 3) Energy storage is not optimized in synergy with DR and carbon trading mechanisms, making it difficult to achieve overall linkage between the load side, energy storage side, and energy supply side. Therefore, there is an urgent need for a comprehensive energy system operation method that can model and jointly optimize carbon trading mechanisms, demand response, and energy storage systems in a unified manner to achieve the optimal balance between system economy and low carbon emissions. Summary of the Invention

[0004] The purpose of this invention is to provide, on the one hand, a comprehensive energy system optimization operation system considering demand response under a carbon trading mechanism, and on the other hand, a comprehensive energy system optimization operation method considering demand response under a carbon trading mechanism. This system and method can comprehensively consider the user-side electric and thermal load response characteristics, the operating characteristics of gas-driven combined heat and power equipment, and the dynamic coupling mechanism of electric energy storage and thermal energy storage systems. It integrates carbon emission cost assessment and energy purchase cost optimization, belonging to the intersection of energy management, artificial intelligence optimization decision-making, power system operation and dispatching, and carbon emission management. It is particularly suitable for low-carbon economic operation and real-time dispatching in industrial parks, multi-energy complementary regional energy stations, and smart grid scenarios.

[0005] To achieve this objective, the present invention provides an integrated energy system optimization operation system considering demand response under a carbon trading mechanism, comprising: The demand response module is used to obtain the initial electrical load and initial heat load values ​​of the integrated energy system based on the basic operating data of all equipment in the integrated energy system, and to calculate the load changes of the integrated energy system on the user side at different time periods based on the preset price elasticity matrix and preset adjustment weight coefficients. The module then optimizes the initial electrical load and initial heat load values ​​based on the load changes of the integrated energy system on the user side at different time periods to obtain the optimized electrical load and optimized heat load values ​​of the integrated energy system. Finally, the module uses a preset equivalent replacement ratio coefficient to perform equivalent optimization on the optimized electrical load and optimized heat load values ​​of the integrated energy system to obtain the equivalent optimized electrical load and optimized heat load values ​​of the integrated energy system. The carbon trading module is used to obtain the total carbon emissions based on the output power and unit power emission factor of the gas equipment in the integrated energy system, and to obtain the carbon market trading cost corresponding to the total carbon emissions through the carbon trading cost expression. The system optimization module is used to obtain the operation and maintenance costs of all equipment in the integrated energy system based on the basic operating data of all equipment. Based on the operation and maintenance costs of all equipment, the preset energy purchase cost and carbon market trading cost, a total cost minimization objective function is constructed. Based on the basic operating data of all equipment in the integrated energy system, the optimized values ​​of equivalent electrical load and equivalent heat load, dynamic constraints of energy storage, mutual exclusion constraints of energy storage, equipment operation constraints, grid mutual exclusion and capacity constraints, and demand response and user satisfaction constraints are set to optimize the objective function and obtain the minimum total operating cost of the integrated energy system that meets the preset constraints.

[0006] Furthermore, the method for obtaining the initial electrical load value and initial thermal load value of the integrated energy system based on the basic operating data of all equipment in the integrated energy system includes: the equipment in the integrated energy system includes gas turbines, waste heat boilers, organic Rankine cycle generators, gas boilers, heat pumps, electric energy storage systems, thermal energy storage systems, wind turbines, and photovoltaic power generation units; = ; ; in, Let be the power output of the wind turbine at time t. Let be the photovoltaic power generation capacity of the photovoltaic power generation unit at time t. Let be the total electrical power of the gas turbine at time t. Let be the power output of the organic Rankine cycle generator at time t. Let be the discharge power of the energy storage system at time t. Let be the charging power of the energy storage system at time t. Let t be the power purchased by the power grid. The power output of the power grid at time t. Let be the electrical power of the heat pump at time t. Let be the initial electrical load value of the integrated energy system at time t. Let be the power of the waste heat boiler at time t. Let be the electrical power of the gas-fired boiler at time t. For heat pump performance coefficient, Let be the heat release power of the thermal energy storage system at time t. Let be the thermal energy storage power of the thermal energy storage system at time t. Let be the initial heat load value of the integrated energy system at time t.

[0007] Furthermore, the method for calculating the load variation of the integrated energy system at different time periods based on the preset price elasticity matrix and preset adjustment weight coefficients, and optimizing the initial electrical load and initial heat load values ​​based on the load variation of the integrated energy system at different time periods to obtain the optimized electrical load and optimized heat load values ​​of the integrated energy system includes: ; ; ; in, This refers to the variation in load on the user side of the integrated energy system during different time periods. The preset adjustment weight coefficient, This refers to the initial electrical load value or the initial thermal load value. For the preset price elasticity matrix, For time-of-use electricity pricing, The benchmark electricity price, This is the optimized value for electrical load. Let be the initial electrical load at time t. This is the optimized value for heat load. Let be the initial heat load at time t. Let be the amount of electrical load transfer at time t. The amount of heat load transferred at time t; Let be the amount of electrical load reduction at time t. Let t be the amount of heat load reduction at time t, and T be the total operating time of the integrated energy system.

[0008] Furthermore, the method for equivalently optimizing the electrical load and thermal load of the integrated energy system using preset equivalent replacement ratios to obtain the equivalent electrical load and thermal load optimization values ​​of the integrated energy system includes: , ; ; ; in, For electrical load, For heat load, This is the equivalent replacement ratio coefficient. Let be the electrothermal conversion efficiency constant. This is the optimal value for the equivalent electrical load. This is the optimized value for the equivalent heat load.

[0009] Furthermore, methods for obtaining total carbon emissions based on the output power and emission factor per unit power of gas-fired equipment in an integrated energy system include: ; in, Indicates total carbon emissions. This represents the emission coefficient per unit power of the gas turbine. The emission coefficient per unit power of a gas-fired boiler. For the input power of the gas turbine, This refers to the power output of the gas-fired boiler.

[0010] Furthermore, methods for obtaining the carbon market transaction costs corresponding to total carbon emissions through the carbon trading cost expression include: ; in, For carbon market transaction costs, For carbon emission unit price, This is the quota coefficient for the unit price of carbon emissions.

[0011] Furthermore, based on the basic operational data of all equipment in the integrated energy system, the operation and maintenance costs of all equipment are obtained. Based on these operation and maintenance costs, and considering the preset energy purchase cost and carbon market trading cost, a method for constructing a total cost minimization objective function includes: Equipment maintenance costs: ; Preset energy purchase cost: ; Minimum total cost: ; in, Total operating costs For the operation and maintenance costs of all equipment, For electricity purchase costs, For carbon market transaction costs, This represents the unit power maintenance coefficient of the gas turbine. Let be the total electrical power of the gas turbine at time t. The unit power operation and maintenance coefficient of the boiler. Let be the electrical power of the gas-fired boiler at time t. The unit power operation and maintenance factor of the heat pump. Let be the electrical power of the gas-fired boiler at time t. This represents the unit power operation and maintenance coefficient of the wind turbine generator. Let be the power output of the wind turbine at time t. The unit power operation and maintenance factor for photovoltaic power. Let be the photovoltaic power generation capacity of the photovoltaic power generation unit at time t. The purchase price of electricity from the grid. The electricity price sold by the power grid. This refers to the unit price of gas.

[0012] Furthermore, methods for constraining and optimizing the objective function based on the basic operating data, equivalent electrical load optimization value, and equivalent heat load optimization value of all equipment in the integrated energy system, including setting dynamic constraints on energy storage, mutual exclusion constraints on energy storage, equipment operation constraints, grid mutual exclusion and capacity constraints, and demand response and user satisfaction constraints, include: Energy storage dynamic constraints: ; ; in, The electrical energy storage state of the integrated energy system at time t. This represents the electrical energy storage state of the integrated energy system at time t-1. The self-loss rate of electrical energy storage in a comprehensive energy system. To improve the charging efficiency of electric energy storage in integrated energy systems, The charging power for the electric energy storage of the integrated energy system, For the discharge power of the electrical storage in the integrated energy system, To improve the discharge efficiency of the energy storage system in a comprehensive energy system, The thermal energy storage state of the integrated energy system at time t. To determine the thermal energy storage state of the energy system at time t-1, The self-loss rate of thermal energy storage in a comprehensive energy system. To improve the charging efficiency of thermal energy storage in integrated energy systems, The charging power for thermal energy storage in the integrated energy system, For the thermal energy storage discharge power of the integrated energy system, For the thermal energy storage discharge efficiency of the integrated energy system; Energy storage mutual exclusion constraints: ; ; ; in, To represent the binary variable indicating whether an energy storage device is in charging or discharging mode, This refers to the maximum charging power of the energy storage device. This refers to the maximum discharge power of the energy storage device. To characterize whether a thermal energy storage device is in charging or discharging mode, This refers to the maximum charging power of the thermal energy storage device. This refers to the maximum discharge power of the thermal energy storage device. Equipment operating constraints: ; ; ; ; ; in, Let be the total electrical power of the gas turbine at time t. This represents the maximum electrical power of the gas turbine. Let be the electrical power of the gas-fired boiler at time t. This represents the maximum electrical power of the gas-fired boiler. Let be the electrical power of the combined heat and power system at time t. Let be the electrical power of the organic Rankine cycle generator at time t. The thermal power of the combined heat and power system at time t. The total thermal power output of the integrated energy system at time t. Let be the heat transfer coefficient of the waste heat boiler at time t. Let be the output electrical power of the gas turbine at time t. The electrical energy output of the combined heat and power system at time t. For the gas turbine's electrical energy conversion efficiency, This refers to the calorific value of the gas. The heat energy output of the combined heat and power system at time t. For the thermal energy conversion efficiency of gas turbines, Power grid mutual exclusion and capacity constraints: Introducing binary variables Constraints on power grid purchase and sale status: ; in, Let t be the power purchased by the power grid. Let be the power output of the power grid at time t. The maximum trading power of the power grid. A binary variable representing whether the power grid is in a state of purchasing or selling electricity; Demand response and user satisfaction constraints: ; in, To assess the overall responsiveness of the integrated energy system, The preset minimum user satisfaction threshold, Let be the amount of electrical load reduction at time t. Let be the amount of electrical load transfer at time t. Let t be the heat load or electrical load at time t. The initial electrical load at time t.

[0013] Furthermore, a comprehensive energy system optimization operation method considering demand response under the carbon trading mechanism of the aforementioned system includes: The initial electrical load and initial heat load of the integrated energy system are obtained based on the basic operating data of all equipment in the integrated energy system. The load variation of the integrated energy system at each time period is calculated based on the preset price elasticity matrix and preset adjustment weight coefficient. The initial electrical load and initial heat load are optimized based on the load variation of the integrated energy system at each time period to obtain the optimized electrical load and optimized heat load of the integrated energy system. The optimized values ​​of electrical load and thermal load of the integrated energy system are optimized by using a preset equivalent replacement ratio coefficient to obtain the optimized values ​​of electrical load and thermal load of the integrated energy system. The total carbon emissions are obtained based on the output power and unit power emission factor of the gas equipment in the integrated energy system, and the carbon market transaction cost corresponding to the total carbon emissions is obtained through the carbon trading cost expression. Based on the basic operating data of all equipment in the integrated energy system, the operation and maintenance costs of all equipment are obtained. Based on the operation and maintenance costs of all equipment, the preset energy purchase cost and carbon market trading cost, a total cost minimization objective function is constructed. Based on the basic operating data, equivalent electrical load optimization value, and equivalent heat load optimization value of all equipment in the integrated energy system, dynamic constraints, mutual exclusion constraints, equipment operation constraints, grid mutual exclusion and capacity constraints, and demand response and user satisfaction constraints are set to optimize the objective function and obtain the minimum total operating cost of the integrated energy system that meets the preset constraints.

[0014] The beneficial effects of this invention are as follows: This invention addresses the problems in existing integrated energy systems, such as insufficient coordination between carbon trading mechanisms and demand response, poor system flexibility, difficulty in achieving low-carbon goals, and ineffective linkage with energy storage. It constructs a unified mixed-integer linear programming (MILP) optimization framework to couple and model carbon trading mechanisms with demand response mechanisms, including price-based demand response (IBDR) and substitution-based demand response (RBDR). The load curve is dynamically adjusted using a time-of-use price elasticity matrix and an equivalent substitution coefficient for electricity and heat. Simultaneously, a dynamic constraint mechanism for carbon emissions and free allowances is introduced to accurately reflect the relationship between gas equipment output and carbon costs. Furthermore, it combines multi-energy complementarity and energy storage collaborative operation strategies. This invention optimizes energy time-shifting and balance by using mutually exclusive constraints and dynamic evolution models for electrical and thermal energy storage. During implementation, it aims to minimize total system cost, comprehensively considering operation and maintenance costs, energy purchase costs, and carbon trading costs. In a typical industrial park scenario, it achieves a reduction of approximately 3.57% in total system cost and approximately 21.94% in carbon emissions. This effectively reduces peak and valley loads and promotes the consumption of renewable energy. The carbon trading mechanism, in conjunction with demand response, achieves additional emission reductions at extremely low marginal costs. It establishes a scalable and implementable low-carbon economic operation method for integrated energy systems, enabling the dual goals of minimizing system operating costs and optimizing carbon emissions while ensuring energy supply and demand balance. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the integrated energy system structure of the present invention; Figure 2 This is a time-of-use electricity price and heat price curve diagram of the present invention; Figure 3 This is the wind and solar power prediction curve of the present invention; Figure 4 This is a comparison chart of the load before and after optimization under the demand response of the present invention; Figure 5 This is a schematic diagram of the energy balance of an electrical load in the scenario described in this invention; Figure 6 This is a schematic diagram of the energy balance of the preferred heat load in scenario one of the present invention; Figure 7 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 a part of the embodiments of the present invention, not all of them. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1 like Figure 7 As shown, a comprehensive energy system optimization operation system considering demand response under a carbon trading mechanism includes: The demand response module is used to obtain the initial electrical load and initial heat load values ​​of the integrated energy system based on the basic operating data of all equipment in the integrated energy system, and to calculate the load changes of the integrated energy system on the user side at different time periods based on the preset price elasticity matrix and preset adjustment weight coefficients. The module then optimizes the initial electrical load and initial heat load values ​​based on the load changes of the integrated energy system on the user side at different time periods to obtain the optimized electrical load and optimized heat load values ​​of the integrated energy system. Finally, the module uses a preset equivalent replacement ratio coefficient to perform equivalent optimization on the optimized electrical load and optimized heat load values ​​of the integrated energy system to obtain the equivalent optimized electrical load and optimized heat load values ​​of the integrated energy system. The carbon trading module is used to obtain the total carbon emissions based on the output power and unit power emission factor of the gas equipment in the integrated energy system, and to obtain the carbon market trading cost corresponding to the total carbon emissions through the carbon trading cost expression. The system optimization module is used to obtain the operation and maintenance costs of all equipment in the integrated energy system based on the basic operating data of all equipment. Based on the operation and maintenance costs of all equipment, the preset energy purchase cost and carbon market trading cost, a total cost minimization objective function is constructed. Based on the basic operating data of all equipment in the integrated energy system, the optimized values ​​of equivalent electrical load and equivalent heat load, dynamic constraints of energy storage, mutual exclusion constraints of energy storage, equipment operation constraints, grid mutual exclusion and capacity constraints, and demand response and user satisfaction constraints are set to optimize the objective function and obtain the minimum total operating cost of the integrated energy system that meets the preset constraints.

[0018] To achieve synergy between economic operation and low-carbon goals in multi-energy coupled integrated energy systems (IES), this invention proposes a novel optimized operation system and method. This system and method integrate a carbon trading mechanism (CTM) and a demand response (DR) mechanism by constructing a unified mixed-integer linear programming (MILP) model. Based on the inclusion of electrothermal energy storage units and multiple energy devices, it aims to minimize the overall system operating cost, thus providing an optimized operation strategy for integrated energy systems that balances economic efficiency and low carbon emissions.

[0019] like Figure 1 As shown, the integrated energy system includes a gas turbine (GT), a waste heat boiler (WHB), an organic Rankine cycle (ORC), a gas boiler (GB), a heat pump (HP), electric energy storage (ES), thermal energy storage (HS), and a grid interface. The integrated energy system operates in a multi-energy coupled state of electricity, heat, and gas, following a specific energy balance relationship. On the electricity side, the integrated energy system integrates wind power and photovoltaic power as the main power generation units, while connecting to the external grid as power support and backup. It is also equipped with lithium battery energy storage devices for storing and transferring electrical energy, enhancing the system's operational flexibility and reliability. On the thermal side, the integrated energy system uses natural gas as a primary energy source, providing heat through two paths: one is direct combustion of heat by the gas boiler; the other is combined heat and power (CHP) through the gas turbine, where the high-temperature flue gas waste heat emitted by the gas turbine is recovered by the waste heat boiler to produce heat energy, achieving cascaded energy utilization. The integrated energy system is also equipped with an electric-driven heat pump, which serves as a key link between the power system and the heating system. It can efficiently convert electrical energy into heat energy, thereby establishing a flexible, two-way energy conversion and complementarity relationship between the electric and heating networks.

[0020] In some technical solutions, the methods for obtaining the initial electrical load value and initial thermal load value of the integrated energy system based on the basic operating data of all equipment in the integrated energy system include: the equipment in the integrated energy system includes gas turbines, waste heat boilers, organic Rankine cycle generators, gas boilers, heat pumps, electric energy storage systems, thermal energy storage systems, wind turbines, and photovoltaic power generation units; Electric power balance equation: = ; Heat load balance equation ; in, Let be the power output of the wind turbine at time t. Let be the photovoltaic power generation capacity of the photovoltaic power generation unit at time t. Let be the total electrical power of the gas turbine at time t. Let be the power output of the organic Rankine cycle generator at time t. Let be the discharge power of the energy storage system at time t. Let be the charging power of the energy storage system at time t. Let t be the power purchased by the power grid. The power output of the power grid at time t. Let be the electrical power of the heat pump at time t. Let be the initial electrical load value of the integrated energy system at time t. Let be the power of the waste heat boiler at time t. Let be the electrical power of the gas-fired boiler at time t. For heat pump performance coefficient, Let be the heat release power of the thermal energy storage system at time t. Let be the thermal energy storage power of the thermal energy storage system at time t. This represents the initial heat load value of the integrated energy system at time t. Basic operating data for all equipment includes the operating power data of the gas turbine, waste heat boiler, organic Rankine cycle generator, gas boiler, heat pump, electric energy storage system, thermal energy storage system, wind turbine, and photovoltaic power generation unit.

[0021] Based on the basic operating data of all equipment in the integrated energy system, power balance equations and heat load balance equations are established to calculate the initial power load and initial heat load values, ensuring that the energy input and output of the system are conserved at any time, and providing accurate load boundary conditions for subsequent optimization models.

[0022] In some technical solutions, the changes in load on the user side of the integrated energy system during different time periods are calculated based on a preset price elasticity matrix and preset adjustment weight coefficients. The initial electrical load and initial heat load values ​​are then optimized based on these changes to obtain the optimized electrical load and heat load values ​​for the integrated energy system. The methods include: ; ; ; in, This refers to the variation in load on the user side of the integrated energy system during different time periods. The preset adjustment weight coefficient, This refers to the initial electrical load value or the initial thermal load value. The preset price elasticity matrix is ​​a function used to represent the minute-by-minute electricity price and the benchmark electricity price. For time-of-use electricity pricing, The benchmark electricity price, This is the optimized value for electrical load. Let be the initial electrical load at time t. This is the optimized value for heat load. Let be the initial heat load at time t. Let be the amount of electrical load transfer at time t. The amount of heat load transferred at time t; Let be the amount of electrical load reduction at time t. Let t be the amount of heat load reduction at time t, and T be the total operating time of the integrated energy system.

[0023] In price-based demand response, it is assumed that users' energy consumption behavior is affected by price changes, and there is an elastic relationship between loads shifting between different time periods. A price elasticity matrix is ​​defined. , indicating the load during time period t relative to time period t Sensitivity to changes in electricity prices. Price elasticity matrix. The diagonal elements represent the price response coefficient for their own time period, while the off-diagonal elements represent the load transfer elasticity between different time periods.

[0024] Both the price elasticity matrix and the adjustment weight coefficient are statistically derived based on the general principle that "electricity price changes affect electricity consumption behavior." The price elasticity matrix uses pre-set diagonal and off-diagonal elements with different numerical signs according to time-of-use electricity price levels (high, medium, and low) to characterize the sensitivity of user load to changes in electricity prices—that is, using fixed negative coefficients to reflect the load suppression effect during high-price periods and fixed positive coefficients to reflect the tendency of load to migrate to low-price periods. The adjustment weight coefficient is used as a control parameter for the overall response intensity, constraining the total magnitude of load adjustment and ensuring that the total magnitude of load adjustment is within a reasonable physical range. This simulates typical user responses while also considering the feasibility of system operation and user satisfaction constraints.

[0025] By introducing a price elasticity matrix and adjusting weight coefficients to optimize the initial electrical and thermal load values, it is possible to effectively simulate users' response behavior to time-of-use pricing, guide load migration from high-price periods to low-price periods, thereby significantly smoothing the load curve, reducing peak-valley differences, and improving the stability and economy of system operation. The optimized electrical and thermal load values ​​serve as key inputs for energy dispatch, reducing dependence on the external power grid during high-price periods through peak shaving and valley filling, thus lowering energy purchase costs. At the same time, the optimized electrical and thermal load values ​​can also indirectly suppress the output of carbon-source equipment such as gas turbines, promoting a reduction in carbon emissions and achieving optimization of electricity economy and low-carbon practices.

[0026] In some technical solutions, the method of using a preset equivalent replacement ratio coefficient to perform equivalent optimization on the electrical load optimization value and thermal load optimization value of the integrated energy system to obtain the equivalent electrical load optimization value and equivalent thermal load optimization value of the integrated energy system includes: , ; ; ; in, For electrical load, For heat load, This is the equivalent replacement ratio coefficient. Let be the electrothermal conversion efficiency constant. This is the optimal value for the equivalent electrical load. This is the optimized value for the equivalent heat load.

[0027] In integrated energy systems, electrical and thermal energy are often interchangeable. For example, an electric heat pump can use electricity to generate heat, allowing for the substitution of electrical energy for thermal energy during periods of higher heat prices and lower electricity prices. Therefore, this invention further establishes a substitution-based demand response model, describing the mutual conversion characteristics between electricity and heat through an energy equivalence relationship. That is, when the heat load decreases, the electrical load will increase proportionally to compensate for the equivalent heat energy demand. A substitution ratio coefficient is introduced. , In some embodiments, the electrothermal conversion efficiency constant It can be set to 1.83, but is not limited to, representing the electrical energy required per unit of heat energy. The Substitutional Demand Response (RBDR) model can simultaneously characterize the time shift effect caused by price changes and the substitution effect between electrical and thermal energy, enabling bidirectional flexible adjustment of the demand side in both time and energy dimensions, and providing a load-side adjustment channel for the low-carbon optimization of the system.

[0028] Some technical solutions involve methods for determining total carbon emissions based on the output power and emission factor per unit power of gas-fired equipment in an integrated energy system, including: ; in, Indicates total carbon emissions. This represents the emission coefficient per unit power of the gas turbine. The emission coefficient per unit power of a gas-fired boiler. For the input power of the gas turbine, This refers to the power output of the gas-fired boiler.

[0029] A carbon trading mechanism is introduced into the economic optimization model of the integrated energy system to internalize the pricing of carbon emission costs. The main carbon emission sources of the system include gas turbines and gas-fired boilers. The carbon emissions from these sources are related to their output, gas consumption, and energy efficiency. Higher output or lower efficiency results in higher carbon emissions. The emission coefficients per unit power of the gas turbines and gas-fired boilers are obtained from the equipment design specifications.

[0030] Accurately calculate total carbon emissions based on the output power of gas equipment and the emission factor per unit power, objectively quantify the carbon consumption of the system, provide accurate carbon emission measurement values ​​for the carbon trading mechanism, and encourage integrated energy systems to proactively avoid high-carbon operation modes during optimized scheduling, prioritize the use of low-carbon equipment and renewable energy, and significantly reduce carbon emission intensity.

[0031] Some technical solutions derive the carbon market trading cost corresponding to total carbon emissions through carbon trading cost expressions, including: ; in, For carbon market transaction costs, For carbon emission unit price, This is the quota coefficient for the unit price of carbon emissions.

[0032] Integrated energy systems are typically allocated a certain amount of free carbon emission allowances to encourage energy conservation and emission reduction. When the actual emissions of the integrated energy system exceed the free allowances, the carbon trading cost is positive, representing the payment of carbon emission fees; conversely, it is negative, representing the system's carbon trading revenue. By linearizing the carbon trading cost and embedding it into the objective function, real-time feedback on emission constraints can be achieved during the optimization process, enabling the system to dynamically balance economic efficiency and low carbon emissions. This ensures that the integrated energy system can dynamically weigh the carbon costs and economic benefits of carbon-emitting equipment (gas turbines and gas boilers) output in each scheduling period, automatically driving the energy scheduling strategy towards a direction that balances low carbon emissions and economic efficiency.

[0033] Some technical solutions involve obtaining the operation and maintenance costs of all equipment in an integrated energy system based on basic operational data, and then constructing a total cost minimization objective function based on these costs, along with preset energy purchase costs and carbon market trading costs. Equipment maintenance costs: ; Preset energy purchase cost: ; Minimum total cost: ; in, Total operating costs For the operation and maintenance costs of all equipment, For electricity purchase costs, For carbon market transaction costs, This represents the unit power maintenance coefficient of the gas turbine. Let be the total electrical power of the gas turbine at time t. The unit power operation and maintenance coefficient of the boiler. Let be the electrical power of the gas-fired boiler at time t. The unit power operation and maintenance factor of the heat pump. Let be the electrical power of the gas-fired boiler at time t. This represents the unit power operation and maintenance coefficient of the wind turbine generator. Let be the power output of the wind turbine at time t. The unit power operation and maintenance factor for photovoltaic power. Let be the photovoltaic power generation capacity of the photovoltaic power generation unit at time t. The purchase price of electricity from the grid. The electricity price sold by the power grid. This refers to the unit price of gas.

[0034] The above formula illustrates the interaction between the integrated energy system and the external energy market. When renewable energy output is sufficient or energy storage discharges, the amount of electricity purchased can be reduced; when electricity prices are high or energy storage is charging, electricity sales can be chosen to maximize electricity price revenue. The unit power operation and maintenance coefficients (basic operating data of the equipment) of the gas turbine, boiler, heat pump, wind turbine, and photovoltaic are all obtained from the equipment manufacturer's specifications. By constructing a total cost minimization objective function, equipment operation and maintenance costs, energy purchase costs, and carbon trading costs are uniformly incorporated into the integrated energy system optimization framework. The intrinsic correlation of various costs is quantified, guiding the integrated energy system to balance equipment output, energy procurement, and carbon emissions during the scheduling process, thereby reducing total operating costs. At the same time, it drives the system to prioritize the use of low-carbon equipment and renewable energy, reducing dependence on high-carbon sources and significantly reducing carbon emissions.

[0035] Some technical solutions involve setting constraints on the objective function based on the basic operating data of all equipment in the integrated energy system, the optimized values ​​of equivalent electrical load and equivalent heat load, and setting dynamic constraints on energy storage, mutual exclusion constraints on energy storage, equipment operation constraints, grid mutual exclusion and capacity constraints, and demand response and user satisfaction constraints. To ensure the physical rationality and operational feasibility of the integrated energy system optimization operation model during the solution process, this invention sets constraints in the integrated energy system operation model, including dynamic evolution of energy storage, equipment operation limitations, and grid interaction logic. These constraints not only ensure that the model satisfies supply and demand balance and equipment safety boundaries in each time period, but also enable the model to have solution convergence and engineering implementation characteristics within the MILP framework.

[0036] To accurately describe the temporal evolution of stored energy, this invention establishes dynamic constraints on the state of stored energy.

[0037] Energy storage dynamic constraints: ; ; in, The electrical energy storage state of the integrated energy system at time t. This represents the electrical energy storage state of the integrated energy system at time t-1. The self-loss rate of electrical energy storage in a comprehensive energy system. To improve the charging efficiency of electric energy storage in integrated energy systems, The charging power for the electric energy storage of the integrated energy system, For the discharge power of the electrical storage in the integrated energy system, To improve the discharge efficiency of the energy storage system in a comprehensive energy system, The thermal energy storage state of the integrated energy system at time t. To determine the thermal energy storage state of the energy system at time t-1, The self-loss rate of thermal energy storage in a comprehensive energy system. To improve the charging efficiency of thermal energy storage in integrated energy systems, The charging power for thermal energy storage in the integrated energy system, For the thermal energy storage discharge power of the integrated energy system, The discharge efficiency of thermal energy storage in the integrated energy system; the charge and discharge efficiency of electrical energy storage, the charge and discharge efficiency of thermal energy storage, the self-loss rate of electrical energy storage, and the self-loss rate of thermal energy storage are the basic operating data of the equipment. The status of electrical energy storage, the status of thermal energy storage, the charge and discharge efficiency of electrical energy storage, the charge and discharge efficiency of thermal energy storage, the self-loss rate of electrical energy storage, and the self-loss rate of thermal energy storage are all measured in real time based on the operating status of the integrated energy system.

[0038] To prevent energy storage devices from charging and discharging simultaneously, mutual exclusion constraints are set, and binary variables are introduced. , Controlling the energy storage mode ensures that the energy storage device can only be in charging or discharging mode at any given time: Energy storage mutual exclusion constraints: ; ; ; in, To represent the binary variable indicating whether an energy storage device is in charging or discharging mode, This refers to the maximum charging power of the energy storage device. This refers to the maximum discharge power of the energy storage device. To characterize whether a thermal energy storage device is in charging or discharging mode, This refers to the maximum charging power of the thermal energy storage device. This refers to the maximum discharge power of the thermal energy storage device. Equipment operating constraints: This is used to ensure that the output of the gas turbine and gas boiler is within the rated capacity range; The energy coupling relationship between the gas turbine, waste heat boiler, and ORC satisfies the following equation: ; ; ; ; in, Let be the total electrical power of the gas turbine at time t. This represents the maximum electrical power of the gas turbine. Let be the electrical power of the gas-fired boiler at time t. This represents the maximum electrical power of the gas-fired boiler. Let be the electrical power of the combined heat and power system at time t. Let be the electrical power of the organic Rankine cycle generator at time t. The thermal power of the combined heat and power system at time t. The total thermal power output of the integrated energy system at time t. Let be the heat transfer coefficient of the waste heat boiler at time t. Let be the output electrical power of the gas turbine at time t. The electrical energy output of the combined heat and power system at time t. For the gas turbine's electrical energy conversion efficiency, This refers to the calorific value of the gas. The heat energy output of the combined heat and power system at time t. For the thermal energy conversion efficiency of the gas turbine; Power grid mutual exclusion and capacity constraints: Introducing binary variables Constraints on power grid purchase and sale status: ; in, Let t be the power purchased by the power grid. Let be the power output of the power grid at time t. The maximum trading power of the power grid. A binary variable representing whether the power grid is in a state of purchasing or selling electricity; To balance system economy and user comfort, this invention introduces a user satisfaction constraint to ensure that the demand response does not exceed acceptable limits.

[0039] Demand response and user satisfaction constraints: ; in, To assess the overall responsiveness of the integrated energy system, The preset minimum user satisfaction threshold, Let be the amount of electrical load reduction at time t. Let be the amount of electrical load transfer at time t. Let t be the heat load or electrical load at time t. The initial electrical load at time t. In some embodiments, the minimum user satisfaction threshold may include, but is not limited to, 0.95, meaning that the user's allowable load fluctuation does not exceed 5%.

[0040] By introducing dynamic and mutual exclusion constraints on energy storage, unreasonable operations such as charging and discharging of energy storage devices simultaneously can be prevented. This ensures safe operation of the equipment and enhances the system's ability to absorb renewable energy fluctuations and improves operational flexibility through peak shaving and valley filling. Combining the operational constraints of equipment such as gas turbines and gas boilers with thermoelectric coupling, it ensures that all energy supply equipment operates efficiently and stably within its rated parameter range, avoiding the risk of equipment overload. At the same time, the mutual exclusion constraints of power purchase and sale status strictly follow the grid dispatch rules, optimizing the economic efficiency of energy interaction between the system and the external grid. Furthermore, user satisfaction constraints balance the economic benefits of demand response regulation with user energy comfort, avoiding the impact on user experience due to excessive load adjustments. Ultimately, under the synergistic protection of multiple constraints, an optimal energy dispatch scheme that combines physical feasibility, operational safety, economy, and user acceptance is achieved.

[0041] Example 2 A comprehensive energy system optimization operation method considering demand response under the carbon trading mechanism of the aforementioned system includes: The initial electrical load and initial heat load of the integrated energy system are obtained based on the basic operating data of all equipment in the integrated energy system. The load variation of the integrated energy system at each time period is calculated based on the preset price elasticity matrix and preset adjustment weight coefficient. The initial electrical load and initial heat load are optimized based on the load variation of the integrated energy system at each time period to obtain the optimized electrical load and optimized heat load of the integrated energy system. The optimized values ​​of electrical load and thermal load of the integrated energy system are optimized by using a preset equivalent replacement ratio coefficient to obtain the optimized values ​​of electrical load and thermal load of the integrated energy system. The total carbon emissions are obtained based on the output power and unit power emission factor of the gas equipment in the integrated energy system, and the carbon market transaction cost corresponding to the total carbon emissions is obtained through the carbon trading cost expression. Based on the basic operating data of all equipment in the integrated energy system, the operation and maintenance costs of all equipment are obtained. Based on the operation and maintenance costs of all equipment, the preset energy purchase cost and carbon market trading cost, a total cost minimization objective function is constructed. Based on the basic operating data, equivalent electrical load optimization value, and equivalent heat load optimization value of all equipment in the integrated energy system, dynamic constraints, mutual exclusion constraints, equipment operation constraints, grid mutual exclusion and capacity constraints, and demand response and user satisfaction constraints are set to optimize the objective function and obtain the minimum total operating cost of the integrated energy system that meets the preset constraints.

[0042] The implementation path of this method is as follows: First, all basic data is collected, including system operating parameters, energy prices (electricity and gas), carbon prices, and renewable energy output forecasts. Next, price-based and substitution-based demand response models are invoked to adjust the original electricity and heat load to an optimized adjustable load. Then, using the corrected load as boundary conditions, a mixed-integer linear programming model is constructed with the objective of minimizing the total cost of carbon trading, energy purchase, and operation and maintenance, incorporating constraints such as energy balance and energy storage dynamics. Finally, the optimal output of each device, the energy storage charge and discharge state, and the carbon trading results are calculated using the Gurobi solver on the Python platform, achieving a synergy between system economics and low carbon emissions.

[0043] The optimization method of this invention has been verified in a typical industrial park integrated energy system example. The research objects include major equipment such as gas turbines, waste heat boilers, organic Rankine cycle units, gas boilers, heat pumps, and electric and thermal energy storage devices. The system operates for 24 hours. The time-of-use electricity price and heat price within one operating cycle are as follows: Figure 2 As shown, the price of natural gas is 3.08 yuan / m³. 3 The predicted power output curve of wind and solar power is as follows: Figure 3 As shown, this describes the expected power generation trends of wind and photovoltaic power generation systems over a specific future period (e.g., 24 hours).

[0044] This invention compares the results of four operating scenarios: Scenario 1, with demand response under a carbon trading mechanism; Scenario 2, without demand response under a carbon trading mechanism; Scenario 3, considering only demand response without carbon trading; and Scenario 4, serving as a baseline, representing both a carbon trading mechanism and demand response. The actual results are shown in Table 1.

[0045] Table 1 Costs for Each Scenario Load comparison before and after optimization under demand response Figure 4 As shown, Figure 4The horizontal axis represents time, and the vertical axis represents the electrical load value before optimization, the thermal load value before optimization, the electrical load value after optimization, and the thermal load value after optimization. The electrical load and thermal load curves after demand response optimization become smoother, and the peak-valley difference is significantly reduced. That is, the load during high-price periods is reduced, and some load is transferred to low-price periods. This shows that the method proposed in this invention can transform the originally rigid load demand into a flexibly adjustable system resource, and ultimately achieve multi-objective collaborative optimization of the safe, economical, and low-carbon operation of the integrated energy system.

[0046] The synergistic effect of the method of this invention is most fully demonstrated in Scenario 1. Compared with the baseline scenario, the total cost of Scenario 1 is reduced by 3.57%, while carbon emissions are significantly reduced by 21.94%. In contrast, the effects of individual strategies are limited; carbon emissions in Scenario 2 are reduced by only 4.70%, and the total cost actually increases by 1.38% due to the increased carbon allowance expenditure; Scenario 3 reduces the total cost by 4.33% and carbon emissions by 8.56%. This shows that DR (Demand Response) is the core driver of system cost reduction, while the carbon trading mechanism enhances the emission reduction effect on this basis. Among them, the cost savings in Scenario 1 compared with the baseline model are mainly due to the reduction in energy purchase costs and operation and maintenance costs. Although carbon trading itself brings an additional expenditure of 328.02 yuan, the savings completely cover this cost, achieving a net cost reduction of 392.76 yuan. Demand response optimization is as follows: Figure 4 As shown. Demand response significantly smooths peak and valley loads, narrowing the peak-valley difference. Price-based response shifts load from high-price periods to low-price periods; substitution-based response utilizes heat pumps to replace heat with electricity during off-peak hours, reducing gas consumption. This dual-dimensional regulation, the energy shifting of ES (Electric Energy Storage System) and HS (Heat Pure Energy Storage System), and the active heating of HP during off-peak hours ultimately improves the utilization rate of renewable energy and significantly reduces the amount of electricity purchased by the grid. The energy balance of electric and heat loads is as follows: Figure 5 and Figure 6 As shown, Figure 5 and Figure 6 The horizontal axis represents the scheduling period, and the vertical axis represents power. Figure 5 It demonstrates how the optimized electrical load (top outline) is met by the combined output of various power sources, including wind power, photovoltaics, gas turbines, grid purchases, and energy storage discharge, at each time period. Meanwhile, excess electricity can be used to charge energy storage or sold to the grid. Figure 6 (Thermal power balance) shows how the optimized heat load is met by multiple heat sources such as gas boilers, waste heat boilers, heat pumps, and thermal storage and release at each time period.

[0047] Furthermore, from the perspective of marginal emission reduction costs, the advantages of considering demand response under the carbon trading mechanism are more significant. In Scenario 2, reducing 1 kg of CO2 requires an additional expenditure of 0.40 yuan. In Scenario 3, reducing 1 kg of CO2 saves 0.69 yuan. In Scenario 1, reducing 1 kg of CO2 saves 0.22 yuan. With demand response already implemented, adding carbon trading only requires an additional 83.96 yuan to reduce 1077 kg of CO2, resulting in a marginal emission reduction cost of only 0.078 yuan / kg CO2. Conversely, by adding demand response to existing carbon trading, both 1388 kg of CO2 is reduced, saving 544.40 yuan, resulting in a marginal emission reduction cost of -0.39 yuan / kg CO2. This further illustrates that demand response is the main mechanism for cost reduction and emission reduction; carbon trading, in conjunction with demand response, can achieve higher emission reductions at extremely low cost.

[0048] In summary, this invention analyzes an integrated energy system that considers demand response under a carbon trading mechanism, introduces demand-side response and carbon cost feedback, and improves the integrated energy system in terms of economy, low carbon emissions, and operational flexibility. It forms a reproducible, implementable, and sustainably optimized method for the low-carbon economic operation of integrated energy systems, which has significant engineering application value and promotion potential.

[0049] Example 3 The present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, performs the steps of the method described in Embodiment 2.

[0050] This invention can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0051] Those skilled in the art will readily understand that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, combinations, substitutions, improvements, etc., made under the spirit and principles of the present invention are included within the protection scope of the present invention.

[0052] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A comprehensive energy system optimization operation system considering demand response under a carbon trading mechanism, characterized in that, It includes: The demand response module is used to optimize the initial electrical load and initial heat load values ​​of the integrated energy system based on the preset price elasticity matrix and adjustment weight coefficients, and obtain the corresponding optimized electrical load and optimized heat load values. The equivalent electrical load optimization value and the equivalent thermal load optimization value are obtained by using a preset equivalent replacement ratio coefficient. The carbon trading module is used to obtain the total carbon emissions based on the output power and unit power emission factor of the gas equipment in the integrated energy system, and to obtain the carbon market trading cost corresponding to the total carbon emissions through the carbon trading cost expression. The system optimization module is used to construct a total cost minimization objective function based on the operation and maintenance costs of all equipment in the integrated energy system, the preset energy purchase cost, and the carbon market trading cost; and to set constraint functions to optimize the total cost minimization objective function based on the basic operating data of all equipment, the optimized value of equivalent electrical load, and the optimized value of equivalent thermal load.

2. The integrated energy system optimization operation system considering demand response under a carbon trading mechanism as described in claim 1, characterized in that: The methods for obtaining the initial electrical load and initial thermal load values ​​of an integrated energy system include: obtaining the initial electrical load and initial thermal load values ​​of the integrated energy system based on the basic operating data of all equipment in the integrated energy system, including gas turbines, waste heat boilers, organic Rankine cycle generators, gas boilers, heat pumps, electric energy storage systems, thermal energy storage systems, wind turbines, and photovoltaic power generation units; = ; ； in, Let be the power output of the wind turbine at time t. Let be the photovoltaic power generation capacity of the photovoltaic power generation unit at time t. Let be the total electrical power of the gas turbine at time t. Let be the power output of the organic Rankine cycle generator at time t. Let be the discharge power of the energy storage system at time t. Let be the charging power of the energy storage system at time t. Let t be the power purchased by the power grid. The power output of the power grid at time t. Let be the electrical power of the heat pump at time t. Let be the initial electrical load value of the integrated energy system at time t. Let be the power of the waste heat boiler at time t. Let be the electrical power of the gas-fired boiler at time t. For heat pump performance coefficient, Let be the heat release power of the thermal energy storage system at time t. Let be the thermal energy storage power of the thermal energy storage system at time t. Let be the initial heat load value of the integrated energy system at time t.

3. The integrated energy system optimization operation system considering demand response under a carbon trading mechanism as described in claim 2, characterized in that: Methods for optimizing the initial electrical load and initial thermal load values ​​of a comprehensive energy system based on a preset price elasticity matrix and adjustment weighting coefficients to obtain the corresponding optimized electrical load and thermal load values ​​include: ； ； ； in, This refers to the variation in load on the user side of the integrated energy system during different time periods. The preset adjustment weight coefficient, This refers to the initial electrical load value or the initial thermal load value. For the preset price elasticity matrix, For time-of-use electricity pricing, The benchmark electricity price, This is the optimized value for electrical load. Let be the initial electrical load at time t. This is the optimized value for heat load. Let be the initial heat load at time t. Let be the amount of electrical load transfer at time t. The amount of heat load transferred at time t; Let be the amount of electrical load reduction at time t. Let t be the amount of heat load reduction at time t, and T be the total operating time of the integrated energy system.

4. The integrated energy system optimization operation system considering demand response under a carbon trading mechanism as described in claim 3, characterized in that: Methods for obtaining equivalent optimized electrical load and equivalent optimized thermal load values ​​by using preset equivalent replacement ratios to optimize electrical load and thermal load values ​​include: ， ； ； ； in, For electrical load, For heat load, This is the equivalent replacement ratio coefficient. Let be the electrothermal conversion efficiency constant. This is the optimal value for the equivalent electrical load. This is the optimized value for the equivalent heat load.

5. The integrated energy system optimization operation system considering demand response under a carbon trading mechanism as described in claim 1, characterized in that: Methods for determining total carbon emissions based on the output power and emission factor per unit power of gas-fired equipment in an integrated energy system include: ； in, Indicates total carbon emissions. This represents the emission coefficient per unit power of the gas turbine. The emission coefficient per unit power of a gas-fired boiler. For the input power of the gas turbine, This refers to the power output of the gas-fired boiler.

6. The integrated energy system optimization operation system considering demand response under a carbon trading mechanism according to claim 5, characterized in that, Methods for obtaining the carbon market transaction cost corresponding to total carbon emissions through the carbon trading cost expression include: ； in, For carbon market transaction costs, For carbon emission unit price, This is the quota coefficient for the unit price of carbon emissions.

7. The integrated energy system optimization operation system considering demand response under a carbon trading mechanism as described in claim 2, characterized in that: Based on the operation and maintenance costs of all equipment in the integrated energy system, methods for constructing a total cost minimization objective function based on preset energy purchase costs and carbon market trading costs include: Equipment maintenance costs: ; Preset energy purchase cost: ; Minimum total cost: ; in, Total operating costs For the operation and maintenance costs of all equipment, For electricity purchase costs, For carbon market transaction costs, This represents the unit power maintenance coefficient of the gas turbine. Let be the total electrical power of the gas turbine at time t. The unit power operation and maintenance coefficient of the boiler. Let be the electrical power of the gas-fired boiler at time t. The unit power operation and maintenance factor of the heat pump. Let be the electrical power of the gas-fired boiler at time t. This represents the unit power operation and maintenance coefficient of the wind turbine generator. Let be the power output of the wind turbine at time t. The unit power operation and maintenance factor for photovoltaic power. Let be the photovoltaic power generation capacity of the photovoltaic power generation unit at time t. The purchase price of electricity from the grid. The electricity price sold by the power grid. This refers to the unit price of gas.

8. A comprehensive energy system optimization operation system considering demand response under a carbon trading mechanism as described in claim 2 or 3, characterized in that: The method for optimizing the objective function of minimizing total cost by setting constraint functions based on the basic operating data, equivalent electrical load optimization value, and equivalent thermal load optimization value of all the equipment includes: the constraint functions include energy storage dynamic constraints, energy storage mutual exclusion constraints, equipment operation constraints, grid mutual exclusion and capacity constraints, and demand response and user satisfaction constraints; Energy storage dynamic constraints: ; ； in, The electrical energy storage state of the integrated energy system at time t. This represents the electrical energy storage state of the integrated energy system at time t-1. The self-loss rate of electrical energy storage in a comprehensive energy system. To improve the charging efficiency of electric energy storage in integrated energy systems, The charging power for the electric energy storage of the integrated energy system, For the discharge power of the electrical storage in the integrated energy system, To improve the energy storage and discharge efficiency of the integrated energy system, The thermal energy storage state of the integrated energy system at time t. To determine the thermal energy storage state of the energy system at time t-1, The self-loss rate of thermal energy storage in a comprehensive energy system. To improve the charging efficiency of thermal energy storage in integrated energy systems, The charging power for thermal energy storage in the integrated energy system. For the thermal energy storage discharge power of the integrated energy system, For the thermal energy storage discharge efficiency of the integrated energy system; Energy storage mutual exclusion constraints: ; ； ； in, To represent the binary variable indicating whether an energy storage device is in charging or discharging mode, This refers to the maximum charging power of the energy storage device. This refers to the maximum discharge power of the energy storage device. To characterize whether a thermal energy storage device is in charging or discharging mode, This refers to the maximum charging power of the thermal energy storage device. This refers to the maximum discharge power of the thermal energy storage device. Equipment operating constraints: ; ； ； ； ； in, Let be the total electrical power of the gas turbine at time t. This represents the maximum electrical power of the gas turbine. Let be the electrical power of the gas-fired boiler at time t. This represents the maximum electrical power of the gas-fired boiler. Let be the electrical power of the combined heat and power system at time t. Let be the electrical power of the organic Rankine cycle generator at time t. The thermal power of the combined heat and power system at time t. The total thermal power output of the integrated energy system at time t. Let be the heat transfer coefficient of the waste heat boiler at time t. Let be the output electrical power of the gas turbine at time t. The electrical energy output of the combined heat and power system at time t. For the gas turbine's electrical energy conversion efficiency, This refers to the calorific value of the gas. The heat energy output of the combined heat and power system at time t. For the thermal energy conversion efficiency of gas turbines, Power grid mutual exclusion and capacity constraints: Introducing binary variables Constraints on power grid purchase and sale status: ; in, Let t be the power purchased by the power grid. Let be the power output of the power grid at time t. The maximum trading power of the power grid. A binary variable representing whether the power grid is in a state of purchasing or selling electricity; Demand response and user satisfaction constraints: ； in, To assess the overall responsiveness of the integrated energy system, The preset minimum user satisfaction threshold, Let be the amount of electrical load reduction at time t. Let be the amount of electrical load transfer at time t. Let t be the heat load or electrical load at time t. Let t be the initial electrical load at time t.

9. A method for optimizing the operation of a comprehensive energy system considering demand response under the carbon trading mechanism of the system described in claim 1, characterized in that, include: Based on the preset price elasticity matrix and adjustment weight coefficients, the initial electrical load and initial heat load values ​​of the integrated energy system are optimized to obtain the corresponding optimized electrical load and optimized heat load values. The equivalent electrical load optimization value and the equivalent thermal load optimization value are obtained by using a preset equivalent replacement ratio coefficient. The total carbon emissions are obtained by calculating the output power and emission factor per unit power of the gas equipment in the integrated energy system, and the carbon market transaction cost corresponding to the total carbon emissions is obtained by the carbon trading cost expression. Based on the operation and maintenance costs of all equipment in the integrated energy system, a total cost minimization objective function is constructed using preset energy purchase costs and carbon market trading costs. Furthermore, constraint functions are set based on the basic operating data of all equipment, the optimized values ​​of equivalent electrical load and equivalent thermal load to optimize the total cost minimization objective function.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 9.

Images