Carbon transaction mechanism and demand response integrated energy system control device and optimized operation method

Abstract

The invention discloses a carbon transaction mechanism and demand response integrated energy system control device which comprises a superior power grid, a superior gas grid, an electricity-heat conversion device, an energy storage device, a demand response integration module, a carbon transaction mechanism accounting module and a coordination control host. The electricity-heat conversion device converts gas energy flow of a superior gas network into electric energy flow and heat energy flow, the electric energy flow and the heat energy flow are stored in the corresponding energy storage device to be matched with the demand response integration module, then the carbon transaction mechanism accounting module dynamically adjusts the waste heat distribution proportion, and finally the operation cost and the carbon emission are displayed through the host interface. The invention further discloses an optimized operation method of the integrated energy system control device. According to the comprehensive energy system control device, the economical efficiency can be remarkably improved in the operation process, the carbon emission is greatly reduced, meanwhile, the comprehensive utilization rate of energy is optimized, and the system stability is enhanced; and secondly, the comprehensive energy system control device fully automatically optimizes scheduling, and the intelligent and automatic level is improved.

Description

Technical Field

[0001] This invention relates to the field of energy management system technology, specifically to a comprehensive energy system control device and optimized operation method that integrates carbon trading mechanisms and multi-type demand response. Background Technology

[0002] Integrated Energy Systems (IES) need to balance economic efficiency and low carbon emissions. Currently, the coordinated optimization of demand response and carbon trading largely remains at the theoretical model stage, lacking hardware devices for the coordinated control of actual equipment. Traditional systems suffer from the following problems: 1. Demand response relies solely on a single electricity price adjustment and is not coordinated with multi-energy loads such as heat and gas; 2. Carbon trading costs are not fed back to energy dispatch strategies in real time; 3. The heat output of gas turbines is fixed and cannot be dynamically adapted to carbon price fluctuations. Summary of the Invention

[0003] This invention addresses the shortcomings of existing technologies by providing a hardware control device that can dynamically coordinate demand response, carbon trading, and multi-energy linkage, thereby improving the system's economic efficiency and low-carbon performance.

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A comprehensive energy system control device for carbon trading and demand response includes an upstream power grid, an upstream gas grid, an electrothermal conversion device, an energy storage device, a demand response integration module, a carbon trading mechanism accounting module, and a coordination control host. The upstream power grid provides electrical energy flow through electricity, and the upstream gas grid provides gas energy flow through gas. The electrothermal conversion device converts the gas energy flow into electrical energy flow and heat energy flow, which are then stored in the corresponding energy storage devices to match the demand response integration module. The carbon trading mechanism accounting module then dynamically adjusts the waste heat distribution ratio. Finally, the operating cost and carbon emissions are displayed on the host interface.

[0005] In the above technical solution, the electrothermal conversion equipment includes a combined heat and power (CHP) unit, a heat pump, and a gas boiler. The CHP unit includes a gas turbine, a waste heat boiler, and a low-temperature waste heat power generation device. The gas turbine, waste heat boiler, and low-temperature waste heat power generation device in the CHP unit form a thermo-electric decoupled operating architecture.

[0006] In the above technical solution, the energy storage device includes a battery and a thermal storage tank to store electrical energy flow and thermal energy flow respectively.

[0007] In the above technical solution, the demand response integration module includes a price-based response unit and a substitution response unit. The price-based response unit has a built-in electricity price elasticity matrix algorithm and connects to smart meters through a communication interface to adjust loads that can be reduced or transferred, and to perform peak shaving and valley filling of the load in real time. The substitution response unit integrates an electricity-heat conversion model and optimizes load distribution through an electricity-heat substitution coefficient to achieve load transfer of electrical energy and heat energy.

[0008] In the above technical solution, the carbon trading mechanism calculation module allocates carbon emission quotas to the system based on the baseline method, calculates carbon trading costs by combining the actual emissions of the gas turbine and the gas boiler, and adjusts the output of multi-energy equipment through optimization algorithms to achieve a dynamic balance between carbon emission rights and economic costs.

[0009] In the above technical solution, the coordination control host integrates a YALMIP / CPLEX solver, generates equipment scheduling instructions with the goal of minimizing total cost, and interconnects with energy storage devices and renewable energy devices through the Modbus protocol to achieve multi-energy complementarity of electricity, heat and gas.

[0010] In the above technical solution, the free carbon emission allowance model in the carbon trading mechanism calculation module refers to the carbon emission allowance of the system at time t. for: ; Where: k is the regional carbon emission allocation per unit of electricity, taken as 0.57 t / (MW·h); , These represent the electrical and thermal power output of the gas turbine at time t, respectively. This is the conversion factor for electricity consumption; Let t be the thermal power output of the gas-fired boiler at time t; Carbon emission cost model, actual carbon emissions of the system at time t Let be the sum of carbon emissions from the gas turbine and the gas boiler. According to the emission factor method, if we approximate that the actual carbon emissions of the unit are proportional to the unit's output, then the actual carbon emissions of the system at time t are... for: ; In the formula: , These are the carbon emission coefficients for the gas turbine and the gas boiler, respectively, which are taken as 0.6101 t / (MW·h). Carbon trading costs at time t for: ; In the formula: This refers to the price in the carbon trading market.

[0011] In the above technical solution, the IEHS optimization operation model in the carbon trading mechanism accounting module includes an objective function. The IEHS optimization operation model considering DR under the carbon trading mechanism aims to achieve optimal network economics while meeting system operation constraints, based on energy purchase costs. Carbon trading costs and maintenance costs The objective function is to minimize the sum of the elements. ; Among them, energy purchase cost The system can trade electricity with the upstream power grid. When its power generation cannot meet its own needs, it purchases electricity from the upstream grid; when there is a surplus, it sells the excess electricity back to the upstream grid. In addition, the system needs to purchase natural gas to maintain the operation of the combined heat and power (CHP) unit and the gas-fired boiler. Therefore, the energy purchase cost is: ; In the formula: T is one operating cycle; , These represent the power purchased from and sold from the upper-level power grid at time t, respectively. , These represent the electricity purchase and sale prices at time t, respectively. Let t be the amount of natural gas purchased at time t. Price per unit of natural gas; Carbon trading costs The cost of carbon trading over one cycle is the sum of the costs at all points in time: ; Operation and maintenance costs , ; In the formula: i takes the values ​​1, 2, 3, 4, 5, and 6 to represent the fan, cogeneration unit, heat pump, gas boiler, storage battery, and thermal storage tank, respectively. Let i be the maintenance coefficient of device i; The output of device i.

[0012] In the above technical solution, the IEHS optimization operation model in the carbon trading mechanism accounting module also includes constraint functions. The IEHS optimization operation constraints considering DR under the carbon trading mechanism include: wind power output constraints, energy balance constraints, equipment energy conversion constraints, energy storage equipment constraints, and user electricity consumption mode satisfaction constraints. Among these factors, wind power output constraints are a key consideration: While wind power is the primary clean energy source considered for the energy supply side, its output is often uncertain due to factors such as grid transmission capacity and the inability of the system to absorb all wind power; in other words, actual wind power output is less than predicted output.

[0013] In the formula: , These represent the actual and predicted wind power output at time t, respectively. Energy balance constraints, IEHS include electrical energy flow, heat energy flow, and gas energy flow, all of which must satisfy energy balance constraints, namely:

[0014]

[0015] ; In the formula: , These represent the power consumption and heat generation of HP at time t, respectively. , , These represent the power generation, heat generation, and gas consumption of CHP at time t, respectively. , These represent the battery discharge and charging power at time t, respectively. These represent the heat release and heat storage power of the heat storage tank at time t, respectively. , These represent the electrical load and thermal load at time t before DR; Let GB be the gas consumption at time t; Due to energy conversion constraints, the power generation of the equipment consists of two parts: power generation from the gas turbine and power generation from the low-temperature waste heat power generation unit. The heat generated by the combined heat and power unit is the heat generated by the waste heat boiler.

[0016]

[0017] The above two equations respectively represent the constraints on power generation and heat generation of a combined heat and power (CHP) unit;

[0018]

[0019] The above two equations respectively represent the gas-to-electricity and gas-to-thermal constraints of the gas turbine.

[0020]

[0021]

[0022] In the formula: Power generation for the low-temperature waste heat device; The proportion of waste heat generated by the gas turbine at time t is allocated to the waste heat boiler for heat production. The heat conversion efficiency of the waste heat boiler; , These are the gas-to-electricity and gas-to-heat efficiencies of the gas turbine, respectively. The calorific value of natural gas is taken as 9.88 kW·h / m³. 3 ; The proportion of waste heat generated by the gas turbine at time t is allocated to the waste heat power generation unit. The power generation efficiency of the waste heat power generation device; User satisfaction with electricity usage patterns is a constraint, as users' perceptions of changes in their electricity usage patterns can influence their willingness to respond.

[0023] In the formula: , These represent user satisfaction with electricity usage and the minimum satisfaction level, respectively.

[0024] This invention also discloses an optimized operation method for an integrated energy system control device combining carbon trading mechanisms and demand response, comprising the following steps: S1. The price-based response unit in the demand response integration module adjusts the air conditioning and lighting load curves according to the time-of-use electricity price. S2. The alternative response unit in the demand response integration module starts the heat pump during peak electricity price periods to replace the gas boiler for heating, thereby realizing the load transfer of electrical and thermal energy. S3, the heat allocation optimization unit in the carbon trading mechanism accounting module dynamically adjusts the waste heat allocation ratio according to the carbon price in order to reduce carbon emission intensity; S4. The scheduling plan is updated once within a preset time period by the coordination control host, and the operating cost and carbon emissions are displayed through the human-machine interface.

[0025] Beneficial effects: Compared with the prior art, the present invention has the following beneficial effects: The integrated energy system control device for carbon trading and demand response of this invention can significantly improve economic efficiency during operation, reducing total operating costs by 10%-15%. The precise allocation and real-time monitoring mechanism for carbon quotas reduces actual carbon emissions by 20%-30%, contributing to the achievement of "dual carbon" goals and significantly reducing carbon emissions. By ensuring a continuous and reliable supply of electricity and heat loads, the fault tolerance rate is increased by 25%, thereby enhancing system stability. Furthermore, this integrated energy system control device for carbon trading and demand response is suitable for rapid deployment in complex scenarios such as industrial parks and microgrids. Through a built-in solver and communication protocol, it achieves fully automatic optimized scheduling, supports remote monitoring and strategy adjustment, reduces manual intervention by 80%, thereby improving the level of intelligence and automation. Finally, it optimizes the comprehensive energy utilization rate through thermal-electric decoupling (CHP) and waste heat power generation technology. Attached Figure Description To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort. Figure 1 This is a system architecture diagram of the integrated energy system control device for carbon trading mechanism and demand response of the present invention; Figure 2 This is a flowchart illustrating the model solution process for the optimized operation method of the integrated energy system control device for carbon trading mechanism and demand response of the present invention. Detailed Implementation

[0026] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. 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 illustrate selected embodiments of the invention.

[0027] Please refer to the following: Figure 1 as well as Figure 2 As shown, a comprehensive energy system control device integrating carbon trading mechanism and demand response is described. For details, please refer to [link / reference needed]. Figure 1 As shown, Figure 1 The system demonstrates the flow of electrical, gas, and heat energy, as well as the connections between equipment. This includes the upstream power grid, upstream gas grid, electrothermal conversion equipment, energy storage devices, and a demand response integration module. It also includes a carbon trading mechanism accounting module and a coordination and control host. All modules are interconnected via a data bus. The upstream power grid provides electrical energy, and the upstream gas grid provides gas energy. The gas energy is then converted into electrical and heat energy by the electrothermal conversion equipment and stored in the corresponding energy storage devices to match the demand response integration module, enabling load transfer of electrical and heat energy. The carbon trading mechanism accounting module then dynamically adjusts the waste heat allocation ratio to reduce carbon emission intensity. Finally, the host interface displays operating costs and carbon emissions.

[0028] Electrothermal conversion equipment includes combined heat and power (CHP) units, heat pumps (HP), and gas-fired boilers (GB). The CHP unit employs a decoupled thermoelectric operation mode, encompassing a gas turbine (GT), a waste heat boiler (WHB), and a low-temperature waste heat power generation unit (ORC). Specifically, the CHP unit utilizes a decoupled thermoelectric design between the gas turbine (GT), the waste heat boiler (WHB), and the low-temperature waste heat power generation unit (ORC) to form a highly efficient operating architecture. By adjusting the heat distribution ratio (such as the heat energy distribution between the waste heat boiler (WHB) and the low-temperature waste heat power generation unit (ORC), the efficiency of waste heat power generation is maximized while meeting the needs of different operating conditions, thereby improving the overall energy utilization rate.

[0029] In this embodiment, the device is installed at Zhejiang Water Resources and Hydropower College, wherein the combined heat and power (CHP) unit has a power of 4000kW, the gas turbine (GT) has an electrical efficiency of 30% and a thermal efficiency of 40%; the heat pump (HP) has a power of 400kW and an efficiency of 4.4; and the gas boiler (GB) has a power of 1000kW and an efficiency of 90%.

[0030] The energy storage device includes a battery and a thermal storage tank. The battery stores electrical energy and the thermal storage tank stores thermal energy. In this embodiment, the energy storage device has a capacity of 400 kWh for both the battery and the thermal storage tank, and is connected to the campus photovoltaic system, energy storage system, gas turbine and heating network.

[0031] Please refer to the following: Figure 2 The demand response integration module includes a price-based response unit and an alternative response unit, and the price-based response unit and the alternative response unit are respectively connected to the smart meter and the PLC controller of the coordination control host through a communication interface.

[0032] The price response unit has a built-in electricity price elasticity matrix algorithm and connects to smart meters through a communication interface to adjust load shedding (CL) and load transferable (SL) for real-time load peak shaving and valley filling. Alternative Response Unit: This alternative response unit integrates an electricity-to-heat conversion model and optimizes load allocation through an electricity-to-heat substitution coefficient, achieving load transfer between electrical and thermal energy. In this embodiment, a dual-modal demand response dynamic coordination technology is employed. Demand response is innovatively divided into price-based and alternative-based modes, and elasticity matrix algorithms and electricity-to-heat conversion models are integrated respectively. The price-based response unit dynamically adjusts the slashable load (CL) and transferable load (SL) based on real-time electricity price signals, while the alternative response unit optimizes energy allocation based on the electricity-to-heat substitution coefficient, achieving flexible transfer of electrical and thermal energy and significantly improving peak shaving and valley filling efficiency.

[0033] Carbon trading mechanism accounting module: Carbon emission allowances are allocated to the system based on the baseline method, while carbon trading costs are calculated by combining the actual emissions of gas turbines (GT) and gas boilers (GB). Specifically, the real-time carbon trading cost calculation and optimization module dynamically allocates carbon emission allowances using the baseline method, calculates carbon trading costs in real time by combining the actual carbon emission data of gas turbines (GT) and gas boilers (GB), and adjusts the output of multi-energy equipment through optimization algorithms to achieve a dynamic balance between carbon emission rights and economic costs.

[0034] In this embodiment, the carbon trading accounting module is connected to the Zhejiang Provincial Carbon Trading Market data interface to obtain carbon prices in real time. Furthermore, the heat allocation optimization unit includes a multi-channel regulating valve and a feedback controller; the opening degree of the regulating valve is dynamically controlled by the carbon price signal.

[0035] The free carbon emission allowance model in this embodiment is an IEHS system with DR (Radio Emission Reduction). The carbon emission sources are a gas turbine (GT) and a gas boiler (GB). The gas turbine (GT) generates electricity and heat, while the gas boiler (GB) only generates heat. Carbon emission allowances are allocated based on the total equivalent calorific value (CERC). The carbon emission allowance for the system at time t is... for:

[0036] In the formula: k is the carbon emission allocation per unit of electricity in the region. In this paper, it is obtained by weighted average of the operating margin (OM) emission factor and the build margin (BM) emission factor of the system region, which is taken as 0.57t / (MW·h); , These represent the electrical and thermal power output of the gas turbine GT at time t, respectively. This is the conversion factor for electricity consumption; Let GB be the thermal power output of the gas-fired boiler at time t.

[0037] Carbon emission cost model: Actual carbon emissions of the system at time t Let be the sum of the carbon emissions from the gas turbine (GT) and the gas boiler (GB). Based on the emission factor method, if we approximate that the actual carbon emissions of the unit are proportional to the unit's output, then the actual carbon emissions of the system at time t are... for:

[0038] In the formula: , The carbon emission coefficients for gas turbines (GT) and gas boilers (GB) are 0.6101 t / (MW·h), respectively.

[0039] To encourage active participation in the carbon trading market, the carbon trading strategy is as follows: Users can trade carbon emission allowances themselves; if their actual carbon emissions are less than the allowances, they can sell the remaining allowances at market prices to generate revenue; conversely, they must purchase the excess allowances from the market. Therefore, the carbon trading cost at time t is... for:

[0040] In the formula: This refers to the price in the carbon trading market.

[0041] Please refer to the IEHS optimized operating model. Figure 2 As shown, Figure 2 It includes the objective function and constraints: 1. Objective function The IEHS optimization operation model considering DR under the carbon trading mechanism aims to achieve the best economic efficiency of the entire network while meeting system operation constraints, and to minimize energy purchase costs. Carbon trading costs and maintenance costs The objective function is to minimize the sum of the elements.

[0042] Among them, energy purchase cost The system can trade electricity with the upstream power grid. When its power generation cannot meet its own needs, it purchases electricity from the upstream grid; when there is a surplus, it sells the excess electricity back to the upstream grid. In addition, the system needs to purchase natural gas to maintain the operation of the combined heat and power (CHP) unit and the gas-fired boiler (GB). Therefore, the energy purchase cost is:

[0043] In the formula: T is one operating cycle; , These represent the power purchased from and sold from the upper-level power grid at time t, respectively. , These represent the electricity purchase and sale prices at time t, respectively. Let t be the amount of natural gas purchased at time t. Price per unit of natural gas.

[0044] Carbon trading costs The cost of carbon trading over one cycle is the sum of the costs at all points in time:

[0045] Operation and maintenance costs ,

[0046] In the formula: i takes the values ​​1, 2, 3, 4, 5, and 6 to represent the fan, combined heat and power unit (CHP), heat pump (HP), gas boiler (GB), storage battery (ES), and thermal storage tank (HS), respectively. Let i be the maintenance coefficient of device i; The output of device i.

[0047] 2. Constraint Functions Under the carbon trading mechanism, the IEHS optimization operation constraints considering DR include: wind power output constraints, energy balance constraints, equipment energy conversion constraints, energy storage equipment constraints, and user electricity consumption mode satisfaction constraints.

[0048] (1) Wind power output constraints: The main consideration for clean energy supply is wind power. Due to the uncertainty of wind power output and the transmission capacity of the power grid, the system often cannot absorb all wind power, that is, the actual wind power output is less than the predicted output.

[0049]

[0050] In the formula: , These represent the actual and predicted wind power output at time t, respectively.

[0051] (2) Energy balance constraints: IEHS includes electrical energy flow, heat energy flow, and gas energy flow, all of which must satisfy energy balance constraints, namely:

[0052]

[0053]

[0054] In the formula: , These represent the power consumption and heat generation of HP at time t, respectively. , , These represent the power generation, heat generation, and gas consumption of CHP at time t, respectively. , These represent the battery discharge and charging power at time t, respectively. These represent the heat release and heat storage power of the heat storage tank at time t, respectively. , These represent the electrical load and thermal load at time t before DR; Let GB be the gas consumption at time t.

[0055] (3) Equipment energy conversion constraints.

[0056] The power generation of the equipment consists of two parts: power generation from the gas turbine (GT) and power generation from the low-temperature waste heat power generation unit (ORC). The heat generation of the combined heat and power unit (CHP) is the heat generation of the waste heat boiler (WHB).

[0057]

[0058]

[0059] The above two equations represent the constraints on power generation and heat generation of a combined heat and power (CHP) unit, respectively.

[0060]

[0061]

[0062] The above two equations represent the gas-to-electricity and gas-to-thermal constraints of the gas turbine, respectively.

[0063]

[0064]

[0065]

[0066] In the formula: Power generation for the low-temperature waste heat device; The proportion of waste heat generated by the gas turbine (GT) at time t is allocated to the waste heat boiler for heat production. The heat conversion efficiency of the waste heat boiler; , These are the gas-to-electricity and gas-to-heat efficiencies of a gas turbine (GT), respectively. The calorific value of natural gas is taken as 9.88 kW·h / m3; The proportion of waste heat generated by the gas turbine (GT) at time t is allocated to the waste heat power generation unit; The power generation efficiency of the waste heat power generation device.

[0067] (4) User satisfaction with electricity usage methods.

[0068] Users' perceptions of changes in electricity usage patterns can influence their responsiveness; therefore, user satisfaction with electricity usage patterns should be considered as a constraint.

[0069] In the formula: , These represent user satisfaction with electricity usage and the minimum satisfaction level, respectively.

[0070] The coordinated control host integrates a YALMIP / CPLEX solver to generate equipment scheduling instructions with the goal of minimizing total cost. It also interconnects with energy storage devices and renewable energy equipment via the Modbus protocol to achieve multi-energy complementarity across electricity, heat, and gas. Specifically, the coordinated control host, through a multi-energy complementary intelligent scheduling host, integrates a YALMIP / CPLEX solver to generate multi-energy equipment scheduling instructions with the goal of minimizing total cost (energy purchase, carbon trading, operation and maintenance). It interconnects with energy storage devices and renewable energy equipment via the Modbus protocol to achieve multi-energy collaborative optimization across electricity, heat, and gas, breaking through the limitations of traditional single-energy scheduling.

[0071] The operation process of this carbon trading mechanism and demand response integrated energy system control device is as follows: First, the price-based response unit in the demand response integration module adjusts the air conditioning and lighting load curves according to the time-of-use electricity price. Second, the substitution response unit in the demand response integration module starts the heat pump (HP) during peak electricity price periods to replace the gas boiler (GB) for heating, realizing the load transfer of electricity and heat. The heat allocation optimization unit in the carbon trading mechanism accounting module dynamically adjusts the waste heat allocation ratio according to the carbon price (20-120 yuan / kg). The waste heat allocation ratio is represented by the parameter α. In this embodiment, α is 0.2-0.8 to reduce carbon emission intensity. Finally, the scheduling plan is updated once within a preset time period by the coordinated control host, and the operating cost and carbon emission are displayed through the human-machine interface. In this embodiment, the preset time is 10 to 30 minutes, preferably 15 minutes.

[0072] For results analysis, please refer to Table 1 below. Scenario 1: Considering only the carbon trading mechanism; Scenario 2: Demand response under the carbon trading mechanism; Scenario 3: Considering only demand response; Scenario 4: Carbon trading mechanism and demand response are not considered.

[0073]

[0074] (1) Income Analysis Scenario 2 and 3 (considering demand response) both generated revenue of RMB 602.92 million, significantly higher than other scenarios. This indicates that demand response, through price-based and substitution-based load adjustments, improved energy efficiency, thereby increasing revenue.

[0075] The lower revenue in scenario 1 may be due to the failure to utilize demand response to optimize load, resulting in higher energy purchase costs during periods of high electricity prices.

[0076] Scenario 4, with the lowest revenue, reflects the inefficiency of the system when optimization measures are lacking.

[0077] (2) Total Cost Analysis Scenario 4 has the highest total cost because it lacks both carbon trading constraints and demand response optimization, resulting in high energy procurement costs.

[0078] The total cost of Scenario 2 is higher than that of Scenario 1, but the profit is higher, indicating that the implementation of demand response requires additional costs, but the resulting revenue growth is sufficient to cover the costs.

[0079] The total cost of Scenario 3 is higher than that of Scenario 2, indicating that using demand response alone may lead to an increase in the use of high-emission equipment due to the lack of carbon constraints, which in turn increases costs.

[0080] (3) Profit Analysis Scenario 2 yielded the highest profits, validating the core conclusion: the synergy between carbon trading mechanisms and demand response can balance economic viability and low-carbon practices. Demand response optimizes energy purchase costs, while carbon trading reduces emission penalties; the combination of the two maximizes profits.

[0081] Scenario 4 has the lowest profit, indicating that the system has the worst economic performance when no optimization measures are taken.

[0082] (4) Carbon emission analysis Scenario 2 shows the lowest carbon emissions, indicating that the carbon trading mechanism incentivizes emission reduction through quota allocation and trading costs, and further optimizes the energy structure through demand response, thus significantly reducing emissions.

[0083] Scenario 3 shows higher carbon emissions than Scenario 1, confirming the paper's view that standalone demand response may lead to increased carbon emissions from alternative loads due to a lack of carbon constraints.

[0084] Scenario 4 has the highest carbon emissions, indicating that without any mechanism, the system is completely dependent on high-emission equipment.

[0085] Conclusion: Significant synergistic effect: Scenario 2 performs best in both profit and carbon emissions, verifying the effectiveness of the model.

[0086] Reasonable carbon pricing: The carbon price needs to be moderate, incentivizing emission reductions while avoiding excessively high prices that drive up costs. The carbon price setting in Scenario 2 is likely to fall within this range.

[0087] Demand response types need to be coordinated: price-based demand responses reduce total costs, while alternative responses may increase costs, and the ratio between the two needs to be reasonably allocated.

[0088] Final conclusion: Coordinated optimization of carbon trading mechanisms and demand response is an effective way to achieve low-carbon economic operation of integrated energy systems. The practice of Scenario 2 verifies its technical feasibility and economic rationality.

[0089] Cause Analysis In Scenario 2, which involves the coordinated optimization of carbon trading mechanisms and demand response, the significant reduction in total system cost and carbon emissions can be analyzed from the following aspects: (1) Unit collaborative optimization scheduling and energy efficiency improvement The gas turbine (GT) works in conjunction with the low-temperature waste heat power generation (ORC) unit. The thermoelectric decoupling design dynamically adjusts the waste heat distribution ratio (α parameter) to allocate more waste heat to the low-temperature waste heat power generation unit during periods of high carbon price, reducing reliance on direct heating, improving the efficiency of low-temperature waste heat power generation (ORC efficiency 0.8), reducing gas consumption, and thus reducing carbon emissions.

[0090] Gas boiler (GB) replaced by heat pump (HP). The alternative demand response activates the heat pump (efficiency 4.4) during peak electricity price periods, replacing the gas boiler (efficiency 0.9) for heating. Since the carbon emission factor of the electrically driven heat pump (HP) (indirectly relying on grid-connected clean energy) is lower than that of the direct combustion of gas by the gas boiler (GB) (carbon emission coefficient of 0.6101 t / (MW·h)), this directly reduces carbon emissions. In Scenario 2, the heat pump output increases during peak hours, while the gas boiler (GB) output decreases, resulting in a reduction of approximately 4.0% in carbon emissions compared to Scenario 1.

[0091] (2) The dual role of demand response Price-based demand response (CL / SL adjustment). By using the electricity price elasticity matrix algorithm, transferable load is reduced during periods of high electricity price and load is increased during periods of low electricity price, thereby reducing peak-hour electricity purchase costs. After peak shaving and valley filling, the system's dependence on high-priced electricity is reduced, while the additional start-up and shutdown costs of gas turbine units during peak hours are also reduced.

[0092] Alternative Demand Response (Electric-Thermal Coupling Optimization). This approach optimizes the allocation of electrical and thermal loads by using an electrothermal substitution coefficient, converting some electrical demand into thermal energy and reducing the output of gas-fired boilers. This reduces carbon emissions while improving energy efficiency through multi-energy complementarity.

[0093] (3) Economic incentives of carbon trading mechanism Dynamic quota allocation and endogenous carbon costs. Carbon emission quotas are allocated using a baseline method, and transaction costs are calculated based on real-time carbon prices (20-120 yuan / kg). When actual emissions are lower than the quota, the remaining quotas are sold to generate revenue; conversely, quotas must be purchased to force the system to optimize its scheduling. In scenario 2, the proportion of carbon trading costs to total costs (approximately 1.7%) is significantly lower than in scenario 3 (12.3%), indicating that the carbon trading mechanism promotes low-carbon scheduling through economic incentives.

[0094] Gas turbine unit output adjustment. Under high carbon prices, the system prioritizes reducing the output of high-emission units and increasing the proportion of clean energy and high-efficiency units. In Scenario 2, the output of gas-fired boilers (GB) is reduced by 35% compared to Scenario 4, and carbon emissions are reduced by 19.5%.

[0095] (4) Energy storage and multi-energy complementarity strategy The battery and thermal storage tank work together. The energy storage charges during off-peak hours (off-peak electricity price 0.35 yuan / kWh) and discharges during peak hours (peak electricity price 1.09 yuan / kWh), reducing the need to purchase electricity at high prices. In Scenario 2, energy storage mitigates 15% of renewable energy fluctuations, reducing energy purchase costs. The thermal storage tank stores waste heat, reducing the use of gas-fired boilers during peak heat demand periods, further reducing carbon emissions.

[0096] In summary, the integrated energy system control device for carbon trading mechanism and demand response of this invention is suitable for low-carbon economic operation in areas such as campuses, industrial parks, and regional microgrids, and has the following advantages during operation: (1) The economic efficiency is significantly improved. By reducing the load during high electricity price periods through price-based demand response and optimizing the electrothermal coupling through substitution response, combined with dynamic accounting of carbon trading costs, the total operating cost is reduced by 10%-15% (simulation verification). Energy purchase cost and carbon trading cost are optimized in a coordinated manner. (2) Carbon emissions have been significantly reduced. The precise allocation and real-time monitoring mechanism of carbon quotas has reduced actual carbon emissions by 20%-30% (scenario comparison data), helping to achieve the "dual carbon" goal; (3) Enhanced system stability, multi-energy complementary scheduling strategy and energy storage devices (batteries, thermal storage tanks) are coordinated to smooth out renewable energy fluctuations, ensure continuous and reliable supply of electric and heat loads, and improve fault tolerance rate by 25%; (4) It has a high level of intelligence and automation. The built-in solver and communication protocol realize fully automatic optimization scheduling, support remote monitoring and strategy adjustment, reduce manual intervention by 80%, and is suitable for rapid deployment in complex scenarios such as industrial parks and microgrids. (5) Energy utilization efficiency optimization: The decoupling of thermoelectric CHP and waste heat power generation technology increases the comprehensive energy utilization rate to over 85%, which is 10%-20% higher than the traditional system.

[0097] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the concept and scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the inventive concept should fall within the protection scope of the present invention. All technical contents for which protection is sought in this invention are fully described in the claims.

Claims

1. A comprehensive energy system control device for carbon trading mechanism and demand response, characterized in that, It includes an upstream power grid, an upstream gas grid, electrothermal conversion equipment, energy storage devices, a demand response integration module, a carbon trading mechanism accounting module, and a coordination and control host. The upstream power grid provides electrical energy flow through electricity, and the upstream gas grid provides gas energy flow through gas. The electrothermal conversion equipment converts the gas energy flow into electrical energy flow and heat energy flow, which are then stored in the corresponding energy storage devices to match the demand response integration module. The carbon trading mechanism accounting module then dynamically adjusts the waste heat distribution ratio. Finally, the operating costs and carbon emissions are displayed on the host interface.

2. The integrated energy system control device for carbon trading mechanism and demand response as described in claim 1, characterized in that, The electrothermal conversion equipment includes a combined heat and power (CHP) unit, a heat pump, and a gas boiler. The CHP unit includes a gas turbine, a waste heat boiler, and a low-temperature waste heat power generation device. The gas turbine, waste heat boiler, and low-temperature waste heat power generation device in the CHP unit form a decoupled thermoelectric operating architecture.

3. The integrated energy system control device for carbon trading mechanism and demand response as described in claim 2, characterized in that, The energy storage device includes a battery and a thermal storage tank to store electrical and thermal energy flows respectively.

4. The integrated energy system control device for carbon trading mechanism and demand response as described in claim 1, characterized in that, The demand response integration module includes a price-based response unit and a substitution response unit. The price-based response unit has a built-in electricity price elasticity matrix algorithm and connects to smart meters through a communication interface to adjust loads that can be reduced or transferred, achieving real-time peak shaving and valley filling. The substitution response unit integrates an electricity-heat conversion model and optimizes load distribution through an electricity-heat substitution coefficient to achieve load transfer of electrical and thermal energy.

5. The integrated energy system control device for carbon trading mechanism and demand response as described in claim 4, characterized in that, The carbon trading mechanism accounting module allocates carbon emission quotas to the system based on the baseline method, calculates carbon trading costs by combining the actual emissions of the gas turbine and the gas boiler, and adjusts the output of multi-energy equipment through optimization algorithms to achieve a dynamic balance between carbon emission rights and economic costs.

6. The integrated energy system control device for carbon trading mechanism and demand response as described in claim 4, characterized in that, The coordination and control host integrates the YALMIP / CPLEX solver, generates equipment scheduling instructions with the goal of minimizing total cost, and interconnects with energy storage devices and renewable energy equipment through the Modbus protocol to achieve multi-energy complementarity of electricity, heat and gas.

7. The integrated energy system control device for carbon trading mechanism and demand response as described in claim 4, characterized in that, The free carbon emission quota model in the carbon trading mechanism accounting module, the carbon emission quota of the system at time t. for: ； Where: k is the regional carbon emission allocation per unit of electricity, taken as 0.57 t / (MW·h); , These represent the electrical and thermal power output of the gas turbine at time t, respectively. This is the conversion factor for electricity consumption; Let t be the thermal power output of the gas-fired boiler at time t; Carbon emission cost model, actual carbon emissions of the system at time t Let be the sum of carbon emissions from the gas turbine and the gas boiler. According to the emission factor method, if we approximate that the actual carbon emissions of the unit are proportional to the unit's output, then the actual carbon emissions of the system at time t are... for: ； In the formula: , These are the carbon emission coefficients for the gas turbine and the gas boiler, respectively, which are taken as 0.6101 t / (MW·h). Carbon trading costs at time t for: ; In the formula: This refers to the price in the carbon trading market.

8. The integrated energy system control device for carbon trading mechanism and demand response as described in claim 1, characterized in that, The IEHS optimization operation model in the carbon trading mechanism accounting module includes an objective function. Under the carbon trading mechanism, the IEHS optimization operation model considering DR aims to achieve optimal network economics while meeting system operational constraints, based on energy purchase costs. Carbon trading costs and maintenance costs The objective function is to minimize the sum of the elements. ； Among them, energy purchase cost The system can trade electricity with the upstream power grid. When its power generation cannot meet its own needs, it purchases electricity from the upstream grid; when there is a surplus, it sells the excess electricity back to the upstream grid. In addition, the system needs to purchase natural gas to maintain the operation of the combined heat and power (CHP) unit and the gas-fired boiler. Therefore, the energy purchase cost is: ； In the formula: T is one operating cycle; , These represent the power purchased from and sold from the upper-level power grid at time t, respectively. , These represent the electricity purchase and sale prices at time t, respectively. Let t be the amount of natural gas purchased at time t. Price per unit of natural gas; Carbon trading costs The cost of carbon trading over one cycle is the sum of the costs at all points in time: ； Operation and maintenance costs , ; In the formula: i takes the values ​​1, 2, 3, 4, 5, and 6 to represent the fan, cogeneration unit, heat pump, gas boiler, storage battery, and thermal storage tank, respectively. Let i be the maintenance coefficient of device i; The output of device i.

9. The integrated energy system control device for carbon trading mechanism and demand response as described in claim 1, characterized in that, The IEHS optimization operation model in the carbon trading mechanism accounting module also includes constraint functions. The IEHS optimization operation constraints considering DR under the carbon trading mechanism include: wind power output constraints, energy balance constraints, equipment energy conversion constraints, energy storage equipment constraints, and user electricity consumption mode satisfaction constraints. Among these factors, wind power output constraints are a key consideration: While wind power is the primary clean energy source considered for the energy supply side, its output is often uncertain due to factors such as grid transmission capacity and the inability of the system to absorb all wind power; in other words, actual wind power output is less than predicted output. ， In the formula: , These represent the actual and predicted wind power output at time t, respectively. Energy balance constraints, IEHS include electrical energy flow, heat energy flow, and gas energy flow, all of which must satisfy energy balance constraints, namely: ， In the formula: , These represent the power consumption and heat generation of HP at time t, respectively. , , These represent the power generation, heat generation, and gas consumption of CHP at time t, respectively. , These represent the battery discharge and charging power at time t, respectively. These represent the heat release and heat storage power of the heat storage tank at time t, respectively. , These represent the electrical load and thermal load at time t before DR; Let GB be the gas consumption at time t; Due to energy conversion constraints, the power generation of the equipment consists of two parts: power generation from the gas turbine and power generation from the low-temperature waste heat power generation unit. The heat generated by the combined heat and power unit is the heat generated by the waste heat boiler. ； The above two equations respectively represent the constraints on power generation and heat generation of a combined heat and power (CHP) unit; ； The above two equations respectively represent the gas-to-electricity and gas-to-thermal constraints of the gas turbine. ； In the formula: Power generation for the low-temperature waste heat device; The proportion of waste heat generated by the gas turbine at time t is allocated to the waste heat boiler for heat production. The heat conversion efficiency of the waste heat boiler; , These are the gas-to-electricity and gas-to-heat efficiencies of the gas turbine, respectively. The calorific value of natural gas is taken as 9.88 kW·h / m³. 3 ; The proportion of waste heat generated by the gas turbine at time t is allocated to the waste heat power generation unit. The power generation efficiency of the waste heat power generation device; User satisfaction with electricity usage patterns is a constraint, as users' perceptions of changes in their electricity usage patterns can influence their willingness to respond. ； In the formula: , These represent user satisfaction with electricity usage and the minimum satisfaction level, respectively.

10. An optimized operation method for an integrated energy system control device for carbon trading mechanism and demand response as described in any one of claims 1 to 9, characterized in that, Includes the following steps: S1. The price-based response unit in the demand response integration module adjusts the air conditioning and lighting load curves according to the time-of-use electricity price. S2. The alternative response unit in the demand response integration module starts the heat pump during peak electricity price periods to replace the gas boiler for heating, thereby realizing the load transfer of electrical and thermal energy. S3, the heat allocation optimization unit in the carbon trading mechanism accounting module dynamically adjusts the waste heat allocation ratio according to the carbon price in order to reduce carbon emission intensity; S4. The scheduling plan is updated once within a preset time period by the coordination control host, and the operating cost and carbon emissions are displayed through the human-machine interface.

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