Demand response and carbon transaction-based carbon reduction and emission reduction method for integrated energy system

Abstract

The invention discloses an integrated energy system carbon reduction and emission reduction method based on demand response and carbon transaction, and the method comprises the steps: constructing a multi-element energy equipment model of an integrated energy system, and carrying out the modeling of an energy flow in the integrated energy system; power generation equipment, energy storage equipment and outsourcing energy are coordinated based on demand response of the integrated energy system and real-time external energy supply conditions in combination with a time sequence model and constraint conditions; by introducing key parameters such as carbon transaction cost, an economic objective function and energy conversion efficiency, an operation strategy of the integrated energy system is optimized; the power output, the energy storage state and the like of various devices are dynamically adjusted in real time; and analyzing and verifying the result of the operation process. According to the invention, through innovative means such as multi-energy cooperation, linkage of demand response and carbon transaction and multi-scene verification, the comprehensive ability of the energy system in the aspects of carbon reduction and emission reduction, economic benefit and user side demand response is significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy scheduling, and in particular to a method for reducing carbon emissions in an integrated energy system based on demand response and carbon trading. Background Art

[0002] The ongoing shift towards sustainable energy sources places a high emphasis on reducing carbon emissions, driving the transformation of traditional power systems to integrated energy systems (IES). Integrated energy systems, which include multiple forms of energy such as electricity, natural gas, and thermal energy, are becoming critical in supporting demand-side carbon reduction. The coupling of multiple energy forms can achieve a more efficient balance between supply and demand, enhance overall system resilience, and enable large-scale decarbonization. In these systems, the synergistic integration of demand response (DR) and carbon trading mechanisms provides a viable path to achieve significant carbon reductions and improved economic performance.

[0003] Patent application number CN 118586538 A discloses a method for coordinated optimization scheduling of virtual power plants based on demand response and carbon trading mechanisms, which mainly revolves around virtual power plants (VPPs) and aims to optimize the scheduling of the overall power system by aggregating the resources of multiple users and distributed energy sources, and independently uses demand response and carbon trading mechanisms;

[0004] The patent application number CN 118941034 A discloses an energy optimization scheduling method and system considering carbon trading mechanism and demand response, which mainly focuses on the application of traditional energy and carbon emission reduction strategies. Although carbon trading and demand response are considered, the complex coupling and coordinated optimization between multiple energy types are not involved;

[0005] Many previous approaches have focused either on optimizing energy supply or managing demand independently, which limits the full potential for carbon reduction. Some approaches are limited to carbon trading or demand response only, ignoring the potential for synergistic interactions between the two strategies. Summary of the invention

[0006] The technical problem to be solved by the present invention is to overcome the existing defects and provide a method for carbon reduction and emission reduction of an integrated energy system based on demand response and carbon trading. Through innovative means such as multi-energy synergy, linkage between demand response and carbon trading, and multi-scenario verification, the comprehensive capabilities of the energy system in carbon reduction and emission reduction, economic benefits and user-side demand response are significantly improved, which can effectively solve the problems in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for reducing carbon emissions in an integrated energy system based on demand response and carbon trading, comprising the following steps:

[0008] S1. Construct a multi-energy equipment model for an integrated energy system and model the energy flow in the integrated energy system;

[0009] S2. Based on the demand response of the integrated energy system and the real-time external energy supply conditions, combined with the timing model and constraints, coordinate power generation equipment, energy storage equipment and purchased energy to achieve a dynamic balance between supply and demand and ensure smooth energy distribution in different time periods;

[0010] S3. By introducing key parameters such as carbon trading costs, economic objective functions, and energy conversion efficiency, we can optimize the operation strategy of the integrated energy system, solve the cost minimization problem in different scenarios, and optimize the multi-energy complementary strategy;

[0011] S4. Real-time dynamic adjustment of the power output and energy storage status of various equipment to optimize the energy flow path and improve the flexibility and robustness of system operation;

[0012] S5. Based on the calculation results of the optimization model and operation strategy, the power distribution, carbon emissions and cost analysis data of the integrated energy system are output, and the results of the operation process are analyzed and verified.

[0013] Preferably, the integrated energy system in step S1 includes a combined heat and power unit (CHP), a gas boiler (GB), an electric energy storage (ES) and a thermal energy storage (HS);

[0014] 1) Combined heat and power unit model, CHP consists of two parts, one part generates electricity through GT (gas turbine) and organic Rankine cycle (ORC), and the other part generates heat through GT. Its expression is:

[0015]

[0016] represents the electric power generated by the CHP unit at time t; represents the electric power generated by GT at time t; represents the power generated by ORC at time t; represents the thermal power generated by the CHP unit at time t; represents the thermal power generated by GT at time t; β t represents the heat distribution ratio at time t; τ WHB Indicates the efficiency of the waste heat boiler;

[0017] GT is an important part of the CHP system. It converts natural gas into electricity and heat. The relationship between the amount of natural gas consumed and the electricity and heat generated is as follows:

[0018]

[0019] represents the amount of natural gas delivered to the CHP unit at time t; τ eGTrepresents the power conversion efficiency of GT at time t; HV g Indicates the calorific value of the gas; τ hGT represents the thermal conversion efficiency of GT; represents the electric power generated by ORC at time t; α t represents the heat distribution coefficient at time t; δ ORC represents the efficiency of the ORC unit;

[0020] 2) Electric energy storage model. The energy balance of the electric energy storage system (ES) is closely related to the state of charge (SOC) at the current and previous time points and the charging and discharging power. In addition, charging and discharging are subject to physical constraints. The system cannot charge and discharge at the same time. In order to ensure normal charging on the next day, the SOC at the beginning and end is usually set to be equal;

[0021]

[0022] Indicates the state of charge of the energy storage at time t; Indicates the state of charge of the energy storage at time t-1; represents the charging power at time t; Indicates the energy storage discharge power at time t; Indicates the 0-1 variable indicating the energy storage charging at time t; Indicates the 0-1 variable indicating the discharge of the electric energy storage at time t; Indicates the charging efficiency of electric energy storage; Indicates the discharge efficiency of electrical energy storage; Indicates the maximum power capacity of electric energy storage; Indicates the minimum state of charge of electrical energy storage; Indicates the maximum state of charge of the electric energy storage; Indicates the charge state of the energy storage at the initial moment; Indicates the charge state at the end of energy storage;

[0023] The power of an ES at any given time is affected by the charging and discharging processes as follows:

[0024]

[0025] Indicates the actual output power of the electric energy storage at time t;

[0026] 3) Thermal energy storage model, the SOC balance and charge and discharge constraints of thermal energy storage (HS) are the same as those of ES, as shown below:

[0027]

[0028] Indicates the charge state of the thermal energy storage at time t; Indicates the charge state of the thermal energy storage at time t-1; Indicates the heating power at time t; It represents the heat release power of thermal energy storage at time t; Indicates the thermal energy storage charging indication 0-1 variable at time t; Indicates the thermal energy storage discharge indication 0-1 variable at time t; Indicates the thermal efficiency of electric energy storage; It indicates the heat release efficiency of electric energy storage; Indicates the maximum power capacity of thermal energy storage; Indicates the minimum state of charge of thermal energy storage; Indicates the maximum state of charge of thermal energy storage; Indicates the charge state of the thermal energy storage at the initial moment; Indicates the charge state at the end of thermal energy storage;

[0029] The power of the HS at any given time is affected by the charging and discharging process as follows:

[0030]

[0031] Represents the actual output power of the thermal energy storage at time t.

[0032] Preferably, the carbon trading in step S3 adopts unpaid carbon quotas, and the carbon quotas for power generation and heat generation are allocated in GT and GB respectively. The total carbon quota of IES is expressed as follows:

[0033]

[0034] E p，t represents free carbon quota; κ represents the carbon emission factor of IES; represents the thermal power output from GT at time t; represents the electric power output by GT at time t; represents the thermal power output by GB at time t; φ represents the electric-to-heat conversion coefficient of the GT unit;

[0035] The actual carbon emissions during the production of gas turbines GT and gas boilers GB can be expressed as:

[0036]

[0037] E ac，t represents the actual carbon emissions at time t; κ GT represents the carbon emission coefficient of GT; κ GB represents the carbon emission factor of GB;

[0038] IES calculates its carbon trading costs based on the difference between the allocated carbon quota and the actual carbon emissions. If the difference is positive, the remaining carbon quota can be sold. If the difference is negative, additional carbon quotas need to be purchased to maintain normal production. The carbon trading cost model is:

[0039] C IES，t =k Ca (E ac，t -E p，t ) (twenty two)

[0040] C IES，t represents the carbon trading cost of IES at time t.

[0041] Preferably, the demand response in step S2 can be divided into load reduction or acceptance of transferable load, and the increase and decrease rates of the import and export loads are restricted. In addition, in order to ensure the stability of daily power and heat load operation, the accumulated transfer-in and transfer-out loads need to offset each other; the demand response model is:

[0042]

[0043] Indicates the minimum transfer power of the electric load; Indicates the maximum transfer power of the electric load; Indicates the minimum reduction power of the electric load; Indicates the maximum reduction power of the electric load; Indicates the minimum transfer power of heat load; Indicates the maximum transfer power of heat load; Indicates the minimum reduction power of heat load; Indicates the maximum reduction power of heat load, represents the electric power transferred in at time t; represents the thermal power transferred in at time t; represents the electric power cut at time t; Represents the electric power cut at time t.

[0044] Preferably, the economic objective function in step S3 consists of three parts. The first component is the energy purchase cost, which includes the cost of purchasing electricity from the main power grid. When excess electricity is generated, it can be sold back to the power grid. This component also includes the cost of purchasing natural gas; the second component is the carbon emission cost; the third component is the maintenance cost of each operating equipment unit; the economic objective function is expressed as:

[0045]

[0046] C Total represents the total cost of the integrated energy system; represents the amount of electricity purchased from the main power grid at time t; The price of electricity purchased from the main grid at any given time; The amount of electricity sold to the main grid at any given moment; The price of electricity sold to the main power plant at any time; Q b，t represents the amount of gas purchased from the main network at time t; κ g represents the natural gas price; ω i represents the operation and maintenance cost coefficient of unit i; P i，t t represents the electric power generated by the i-th electrical equipment (CHP, GT, ORC, wind turbine, ES, HS) at time t;

[0047] The constraints include:

[0048] 1) Electric balance constraint, the electric balance equation is as follows:

[0049]

[0050] represents the electric power generated by the wind turbine at time t; It represents the power consumption of the original electric load at time t; Indicates the power reduction of the electric load at time t; represents the power transferred by the electric load at time t; It represents the amount of alternative electrical load at time t;

[0051] 2) Thermal balance constraint, the thermal balance equation is as follows:

[0052]

[0053] It represents the original heat load power consumption at time t; represents the amount of alternative heat load at time t;

[0054] 3) Gas balance constraint, the gas balance equation is as follows:

[0055]

[0056] Indicates the gas consumption of GB at time t.

[0057] Compared with the prior art, the beneficial effects of the present invention are: it proposes the joint implementation of demand response and carbon trading mechanisms, which produces significant improvements in economic and environmental performance compared with the separate implementation, greatly reduces the total cost and carbon emissions, while improving operational efficiency and load balance; the use of renewable energy improves system flexibility and reduces dependence on grid electricity and gas boilers. In addition, carbon trading effectively reduces emission peaks and improves energy management efficiency. The integration of DR, carbon trading and renewable resources can optimize the energy system, thereby achieving more sustainable, cost-effective and resilient operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the process of the present invention;

[0059] Figure 2 It is the power load distribution diagram under different DR scenarios;

[0060] Figure 3 It is the heat load distribution diagram under different DR conditions;

[0061] Figure 4 This is the power load distribution diagram of different units in Case 3;

[0062] Figure 5 is the heat load distribution diagram of different units in Case 3;

[0063] Figure 6 is the CO2 emissions from different sources in Case 1;

[0064] Figure 7 is the CO2 emissions from different sources in Case 2. DETAILED DESCRIPTION

[0065] The present invention can be explained in detail through the following embodiments. The purpose of disclosing the present invention is to protect all technical improvements within the scope of the present invention. In the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "front", "back", "left", "right", etc. indicating directions or positional relationships, they only correspond to the drawings of the present application for the convenience of describing the present invention, and do not indicate or imply that the device or element referred to must have a specific direction.

[0066] See also Figure 1-7 As shown, the present invention provides a technical solution: a method for reducing carbon emissions in an integrated energy system based on demand response and carbon trading, comprising the following steps:

[0067] S1. Construct a multi-energy equipment model for an integrated energy system and model the energy flow in the integrated energy system;

[0068] The integrated energy system includes a combined heat and power unit (CHP), a gas boiler (GB), an electric energy storage (ES) and a thermal energy storage (HS);

[0069] 1) Combined heat and power unit model, CHP consists of two parts, one part generates electricity through GT (gas turbine) and organic Rankine cycle (ORC), and the other part generates heat through GT. Its expression is:

[0070]

[0071] represents the electric power generated by the CHP unit at time t; represents the electric power generated by GT at time t; represents the power generated by ORC at time t; represents the thermal power generated by the CHP unit at time t; represents the thermal power generated by GT at time t; β t represents the heat distribution ratio at time t; τ WHB Indicates the efficiency of the waste heat boiler;

[0072] GT is an important part of the CHP system. It converts natural gas into electricity and heat. The relationship between the amount of natural gas consumed and the electricity and heat generated is as follows:

[0073]

[0074] represents the amount of natural gas delivered to the CHP unit at time t; τ eGT represents the power conversion efficiency of GT at time t; HV g Indicates the calorific value of the gas; τ hGT represents the thermal conversion efficiency of GT; represents the electric power generated by ORC at time t; α t represents the heat distribution coefficient at time t; δ ORC represents the efficiency of the ORC unit;

[0075] 2) Electric energy storage model. The energy balance of the electric energy storage system (ES) is closely related to the state of charge (SOC) at the current and previous time points and the charging and discharging power. In addition, charging and discharging are subject to physical constraints. The system cannot charge and discharge at the same time. In order to ensure normal charging on the next day, the SOC at the beginning and end is usually set to be equal;

[0076]

[0077] Indicates the state of charge of the energy storage at time t; Indicates the state of charge of the energy storage at time t-1; represents the charging power at time t; Indicates the energy storage discharge power at time t; Indicates the 0-1 variable indicating the energy storage charging at time t; Indicates the 0-1 variable indicating the discharge of the electric energy storage at time t; Indicates the charging efficiency of electric energy storage; Indicates the discharge efficiency of electrical energy storage; Indicates the maximum power capacity of electric energy storage; Indicates the minimum state of charge of electrical energy storage; Indicates the maximum state of charge of the electric energy storage; Indicates the charge state at the initial moment of the energy storage; Indicates the charge state at the end of energy storage;

[0078] The power of an ES at any given time is affected by the charging and discharging processes as follows:

[0079]

[0080] Indicates the actual output power of the electric energy storage at time t;

[0081] 3) Thermal energy storage model, the SOC balance and charge and discharge constraints of thermal energy storage (HS) are the same as those of ES, as shown below:

[0082]

[0083] Indicates the charge state of the thermal energy storage at time t; Indicates the charge state of the thermal energy storage at time t-1; Indicates the heating power at time t; It represents the heat release power of thermal energy storage at time t; Indicates the thermal energy storage charging indication 0-1 variable at time t; Indicates the thermal energy storage discharge indication 0-1 variable at time t; Indicates the thermal efficiency of electric energy storage; It indicates the heat release efficiency of electric energy storage; Indicates the maximum power capacity of thermal energy storage; Indicates the minimum state of charge of thermal energy storage; Indicates the maximum state of charge of thermal energy storage; Indicates the charge state of the thermal energy storage at the initial moment; Indicates the charge state at the end of thermal energy storage;

[0084] The power of the HS at any given time is affected by the charging and discharging process as follows:

[0085]

[0086] Represents the actual output power of the thermal energy storage at time t.

[0087] S2. Based on the demand response of the integrated energy system and the real-time external energy supply conditions, combined with the timing model and constraints, coordinate power generation equipment, energy storage equipment and purchased energy to achieve a dynamic balance between supply and demand and ensure smooth energy distribution in different time periods;

[0088] Demand response can be divided into load reduction or acceptance of transferable load. The rise and fall rates of import and export loads are restricted. In addition, in order to ensure the stability of daily power and heat load operation, the cumulative transfer-in and transfer-out loads need to offset each other. The demand response model is:

[0089]

[0090]

[0091] Indicates the minimum transfer power of the electric load; Indicates the maximum transfer power of the electric load; Indicates the minimum reduction power of the electric load; Indicates the maximum reduction power of the electric load; Indicates the minimum transfer power of heat load; Indicates the maximum transfer power of heat load; Indicates the minimum reduction power of heat load; Indicates the maximum reduction power of heat load, express t The electrical power input at any time; express t The thermal power transferred in at any time; express t The electrical power that is reduced at any time; express t Time-reduced electrical power.

[0092] S3. By introducing key parameters such as carbon trading costs, economic objective functions, and energy conversion efficiency, we can optimize the operation strategy of the integrated energy system, solve the cost minimization problem in different scenarios, and optimize the multi-energy complementary strategy;

[0093] Carbon trading uses unpaid carbon quotas, with GT and GB used to allocate carbon quotas for power generation and heat generation respectively. The total carbon quota of IES is expressed as follows:

[0094]

[0095] E p，t represents free carbon quota; κ represents the carbon emission factor of IES; represents the thermal power output from GT at time t; represents the electric power output by GT at time t; represents the thermal power output by GB at time t; φ represents the electric-to-heat conversion coefficient of the GT unit;

[0096] The actual carbon emissions during the production of gas turbines GT and gas boilers GB can be expressed as:

[0097]

[0098] E ac，t represents the actual carbon emissions at time t; κ GT represents the carbon emission coefficient of GT; κ GB represents the carbon emission factor of GB;

[0099] IES calculates its carbon trading costs based on the difference between the allocated carbon quota and the actual carbon emissions. If the difference is positive, the remaining carbon quota can be sold. If the difference is negative, additional carbon quotas need to be purchased to maintain normal production. The carbon trading cost model is:

[0100] C IES，t =k Ca (E ac，t -E p，t ) (twenty two)

[0101] C IES，t represents the carbon trading cost of IES at time t.

[0102] The economic objective function consists of three components. The first component is the energy purchase cost, which includes the cost of purchasing electricity from the main grid. When excess electricity is generated, it can be sold back to the grid. This component also includes the cost of purchasing natural gas. The second component is the carbon emission cost. The third component is the maintenance cost of each operating equipment unit. The economic objective function is expressed as:

[0103]

[0104] C Total represents the total cost of the integrated energy system; represents the amount of electricity purchased from the main power grid at time t; The price of electricity purchased from the main grid at any given time; The amount of electricity sold to the main grid at any given moment; The price of electricity sold to the main power plant at any time; Q b，t represents the amount of gas purchased from the main network at time t; κ g represents the natural gas price; ω i represents the operation and maintenance cost coefficient of unit i; P i，t, t represents the electric power generated by the i-th electrical equipment (CHP, GT, ORC, wind turbine, ES, HS) at time t;

[0105] The constraints include:

[0106] 1) Electric balance constraint, the electric balance equation is as follows:

[0107]

[0108] represents the electric power generated by the wind turbine at time t; It represents the power consumption of the original electric load at time t; Indicates the power reduction of the electric load at time t; represents the power transferred by the electric load at time t; It represents the amount of alternative electrical load at time t;

[0109] 2) Thermal balance constraint, the thermal balance equation is as follows:

[0110]

[0111] It represents the original heat load power consumption at time t; represents the replacement heat load at time t;

[0112] 3) Gas balance constraint, the gas balance equation is as follows:

[0113]

[0114] Indicates the gas consumption of GB at time t.

[0115] S4. Make real-time dynamic adjustments to the power output, energy storage status, etc. of various types of equipment to optimize the energy flow path and improve the flexibility and robustness of system operation.

[0116] S5. Based on the calculation results of the optimization model and operation strategy, the power distribution, carbon emissions and cost analysis data of the integrated energy system are output, and the results of the operation process are analyzed and verified.

[0117] In order to verify the effectiveness of carbon trading policies and flexible load control demand response in reducing carbon emissions in the integrated energy system of electricity, gas and heat in the park, we conducted three comparative experiments: Case 1 considers DR technology, Case 2 examines carbon trading strategies, and Case 3 comprehensively evaluates DR and carbon trading strategies. The scenarios given are as follows:

[0118] (1) Case 1: DR procedure is available;

[0119] (2) Case 2: Adoption of carbon trading;

[0120] (3) Case 3: Carbon trading and DR program.

[0121] The comprehensive comparison of IES costs and carbon emissions under different scenarios is shown in Table 1. From the comparison of system costs and carbon emissions under different scenarios, it can be seen that combining DR technology with carbon trading policies can produce significant benefits;

[0122] In case 1, only considering DR technology, the total cost is 20696CNY and the carbon emissions are 40740kg. In contrast, case 2, which only includes carbon trading policies, reduces the total cost to 19314CNY, a decrease of 6.67% compared with case 1, and the carbon emissions are also reduced to 39664kg, a decrease of 2.64%;

[0123] In case 3, DR and carbon trading strategies were considered comprehensively, and further improvements were observed. The total cost of case 3 was 19241CNY, which was 0.38% lower than case 2 and 7.04% lower than case 1. Carbon emissions were reduced to 39578 kg, which was 0.22% less than case 2 and 2.86% less than case 1. These results demonstrated the synergistic effect of combining the two strategies, producing the lowest total cost and carbon emissions in all scenarios. In terms of energy trading costs, case 3 also achieved a reduction of 18320CNY, which was 2.58% lower than case 1 and 0.35% lower than case 2. In all scenarios, operating costs remained relatively stable, with only a small decrease observed. Overall, the results show that the comprehensive approach combining DR and carbon trading mechanisms is the most effective way to achieve comprehensive energy system cost minimization and carbon emission reduction.

[0124] Table 1. Comparison of system costs and carbon emissions under different scenarios

[0125]

[0126] Figure 2The distribution diagram of power load under different DR cases is shown. It can be observed that Case 1, which uses an independent demand response strategy, produces relatively stable fluctuations, with a peak value of 2619kW and a minimum value of 1560kW. On the contrary, in Case 2, which includes a carbon trading strategy, the load shows greater changes, with the peak value increasing to 2700kW and the lower limit increasing to 1725kW. In Case 3, which uses both strategies in combination, the load fluctuation is relatively stable compared with Case 2, with a peak value of 2619kW and a trough value of 1624kW. Compared with Case 1, the peak power load in Case 2 increased by about 3.09%, which shows that integrating carbon trading without demand response leads to higher load peaks. However, by combining demand response and carbon trading strategies, Case 3 shows a balanced effect, with a peak value reduced by about 3.0% compared with Case 2. This shows that the comprehensive approach in Case 3 is more effective in alleviating excessive load peaks, which helps to improve grid stability.

[0127] Figure 3 The heat load distribution diagram under different operating conditions is shown. In Case 1, the peak heat load is 2460kW and the minimum heat load is 1500kW; in Case 2, the peak load remains at 2460kW, but the variability is significantly reduced, with the lower load point being approximately 1750kW; Case 3 uses DR and carbon trading, achieving a similar peak of 2460kW, but presents a more uniform heat load distribution, especially between 6 and 14 hours, resulting in a more consistent energy usage pattern. In terms of load reduction, Case 3 shows a 1.5% reduction in heat load peak compared to Case 1. In addition, the overall load distribution becomes more uniform, especially late at night, which highlights the synergistic effect of combining the two strategies. By balancing the load, the integrated approach in Case 3 contributes to a more efficient distribution of thermal energy, ultimately improving the energy efficiency of the integrated energy system of the campus.

[0128] exist Figure 4 From the electricity load distribution diagram shown, it is clear that IES helps diversify energy supply, CHP, WT and PV are consistent, thus reducing dependence on grid electricity and helping to reduce overall carbon emissions. Figure 5 It can be seen that CHP and heat pumps are the main contributors to meeting the heat load demand, while gas boilers are mainly used to supplement heating during high demand periods.

[0129] Figure 6 and Figure 7 CO2 emissions from different sources in Case 1 and Case 2 are shown, and CO2 emissions from CHP heat production remain roughly the same in both cases. This consistency suggests that carbon trading strategies have no significant impact on emissions associated with CHP heat production, which may be attributed to the fact that CHP heat production is usually designed to meet fixed heat demand, which is less flexible in responding to economic incentives such as carbon pricing.

[0130] However, there are significant differences in the temporal distribution of CO2 emissions from CHP generation compared to GB generation in Case 1 and Case 2. In Case 1, the emission peak from CHP generation is more significant, while in Case 2, the emission distribution is more even and the overall emissions are lower. This reduction in peak emissions can be linked to the implementation of the carbon trading policy in Case 2, which incentivizes more efficient energy production and optimizes the dispatch of CHP generation to minimize emissions. Similarly, GB emissions are significantly reduced in Case 2 compared to Case 1, especially during off-peak hours. This effect can be attributed to the carbon pricing mechanism, which encourages less reliance on GB generation and supports other low-emission or more efficient options, such as optimizing the use of CHP.

[0131] The parts of the present invention that are not described in detail are prior art. It is obvious to those skilled in the art that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention; therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive, and it is intended that all changes that fall within the meaning and scope of equivalent elements are included in the content of the present invention.

Claims

1. A method for reducing carbon emissions in an integrated energy system based on demand response and carbon trading, characterized by: The following steps are involved: S1. Construct a multi-energy equipment model for an integrated energy system and model the energy flow in the integrated energy system; S2. Based on the demand response of the integrated energy system and the real-time external energy supply conditions, combined with the timing model and constraints, coordinate power generation equipment, energy storage equipment and purchased energy to achieve a dynamic balance between supply and demand and ensure smooth energy distribution in different time periods; S3. By introducing key parameters such as carbon trading costs, economic objective functions, and energy conversion efficiency, we can optimize the operation strategy of the integrated energy system, solve the cost minimization problem in different scenarios, and optimize the multi-energy complementary strategy; S4. Real-time dynamic adjustment of the power output and energy storage status of various equipment to optimize the energy flow path and improve the flexibility and robustness of system operation; S5. Based on the calculation results of the optimization model and operation strategy, the power distribution, carbon emissions and cost analysis data of the integrated energy system are output, and the results of the operation process are analyzed and verified.

2. According to claim 1, a method for reducing carbon emissions in an integrated energy system based on demand response and carbon trading is characterized by: The integrated energy system in step S1 includes a combined heat and power unit (CHP), a gas boiler (GB), an electric energy storage (ES) and a thermal energy storage (HS); 1) Combined heat and power unit model, CHP consists of two parts, one part generates electricity through GT (gas turbine) and organic Rankine cycle (ORC), and the other part generates heat through GT. Its expression is: represents the electric power generated by the CHP unit at time t; represents the electric power generated by GT at time t; represents the power generated by ORC at time t; represents the thermal power generated by the CHP unit at time t; represents the thermal power generated by GT at time t; β t represents the heat distribution ratio at time t; τ WHB Indicates the efficiency of the waste heat boiler; GT is an important part of the CHP system. It converts natural gas into electricity and heat. The relationship between the amount of natural gas consumed and the electricity and heat generated is as follows: represents the amount of natural gas delivered to the CHP unit at time t; τ eGT represents the power conversion efficiency of GT at time t; HV g Indicates the calorific value of the gas; τ hGT represents the thermal conversion efficiency of GT; represents the electric power generated by ORC at time t; α t represents the heat distribution coefficient at time t; δ ORC represents the efficiency of the ORC unit; 2) Electric energy storage model. The energy balance of the electric energy storage system (ES) is closely related to the state of charge (SOC) at the current and previous time points and the charging and discharging power. In addition, charging and discharging are subject to physical constraints. The system cannot charge and discharge at the same time. In order to ensure normal charging on the next day, the SOC at the beginning and end is usually set to be equal; Indicates the state of charge of the energy storage at time t; Indicates the state of charge of the energy storage at time t-1; represents the charging power at time t; Indicates the energy storage discharge power at time t; Indicates the 0-1 variable indicating the energy storage charging at time t; Indicates the 0-1 variable indicating the discharge of the electric energy storage at time t; Indicates the charging efficiency of electric energy storage; Indicates the discharge efficiency of electrical energy storage; Indicates the maximum power capacity of electric energy storage; Indicates the minimum state of charge of electrical energy storage; Indicates the maximum state of charge of the electric energy storage; Indicates the charge state at the initial moment of the energy storage; Indicates the charge state at the end of energy storage; The power of an ES at any given time is affected by the charging and discharging processes as follows: Indicates the actual output power of the electric energy storage at time t; 3) Thermal energy storage model, the SOC balance and charge and discharge constraints of thermal energy storage (HS) are the same as those of ES, as shown below: Indicates the charge state of the thermal energy storage at time t; Indicates the charge state of the thermal energy storage at time t-1; Indicates the heating power at time t; It represents the heat release power of thermal energy storage at time t; Indicates the thermal energy storage charging indication 0-1 variable at time t; Indicates the thermal energy storage discharge indication 0-1 variable at time t; Indicates the thermal efficiency of electric energy storage; It indicates the heat release efficiency of electric energy storage; Indicates the maximum power capacity of thermal energy storage; Indicates the minimum state of charge of thermal energy storage; Indicates the maximum state of charge of thermal energy storage; Indicates the charge state of the thermal energy storage at the initial moment; Indicates the charge state at the end of thermal energy storage; The power of the HS at any given time is affected by the charging and discharging process as follows: Represents the actual output power of the thermal energy storage at time t.

3. According to claim 1, a method for reducing carbon emissions in an integrated energy system based on demand response and carbon trading is characterized by: In step S3, the carbon trading adopts unpaid carbon quotas, and the carbon quotas for power generation and heat generation are allocated in GT and GB respectively. The total carbon quota of IES is expressed as follows: E p，t represents free carbon quota; κ represents the carbon emission factor of IES; represents the thermal power output from GT at time t; represents the electric power output by GT at time t; represents the thermal power output by GB at time t; φ represents the electric-to-heat conversion coefficient of the GT unit; The actual carbon emissions during the production of gas turbines GT and gas boilers GB can be expressed as: E ac，t represents the actual carbon emissions at time t; κ GT represents the carbon emission coefficient of GT; κ GB represents the carbon emission factor of GB; IES calculates its carbon trading costs based on the difference between the allocated carbon quota and the actual carbon emissions. If the difference is positive, the remaining carbon quota can be sold. If the difference is negative, additional carbon quotas need to be purchased to maintain normal production. The carbon trading cost model is: C IES，t =k Ca (AND ac，t -AND p，t ) (22) C IES，t represents the carbon trading cost of IES at time t.

4. According to claim 1, a method for reducing carbon emissions in an integrated energy system based on demand response and carbon trading is characterized by: The demand response in step S2 can be divided into load reduction or acceptance of transferable load. The rise and fall rates of import and export loads are restricted. In addition, in order to ensure the stability of daily power and heat load operation, the accumulated import and export loads need to offset each other. The demand response model is: Indicates the minimum transfer power of the electric load; Indicates the maximum transfer power of the electric load; Indicates the minimum reduction power of the electric load; Indicates the maximum reduction power of the electric load; Indicates the minimum transfer power of heat load; Indicates the maximum transfer power of heat load; Indicates the minimum reduction power of heat load; Indicates the maximum reduction power of heat load, represents the electric power transferred in at time t; represents the thermal power transferred in at time t; represents the electric power cut at time t; Represents the electric power cut at time t.

5. The method for reducing carbon emissions in an integrated energy system based on demand response and carbon trading according to claim 1 is characterized by: The economic objective function in step S3 consists of three parts: The first component is the energy purchase cost, which includes the cost of purchasing electricity from the main grid. When excess electricity is generated, it can be sold back to the grid. This component also includes the cost of purchasing natural gas. The second component is the carbon emission cost. The third component is the maintenance cost of each operating equipment unit. The economic objective function is expressed as: C Total represents the total cost of the integrated energy system; represents the amount of electricity purchased from the main power grid at time t; The price of electricity purchased from the main grid at any given time; The amount of electricity sold to the main grid at any given moment; The price of electricity sold to the main power plant at any time; Q b，t represents the amount of gas purchased from the main network at time t; κ g represents the natural gas price; ω i represents the operation and maintenance cost coefficient of unit i; P i，t t represents the electric power generated by the i-th electrical equipment (CHP, GT, ORC, wind turbine, ES, HS) at time t; The constraints include: 1) Electric balance constraint, the electric balance equation is as follows: represents the electric power generated by the wind turbine at time t; It represents the power consumption of the original electric load at time t; Indicates the power reduction of the electric load at time t; represents the power transferred by the electric load at time t; It represents the amount of alternative electrical load at time t; 2) Thermal balance constraint, the thermal balance equation is as follows: It represents the original heat load power consumption at time t; represents the amount of alternative heat load at time t; 3) Gas balance constraint, the gas balance equation is as follows: Indicates the gas consumption of GB at time t.

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