Carbon energy collaborative response optimization method and device of integrated energy system

Abstract

The invention discloses a carbon energy collaborative response optimization method and device of an integrated energy system, and belongs to the field of power system dispatching operation. The method comprises the steps that a system carbon emission flow model used for calculating the load node carbon potential of the integrated energy system is established according to operation data of the integrated energy system; establishing an upper-layer carbon price optimization model according to purchase and sale transaction data and load node carbon potential of the integrated energy system; establishing a lower-layer load optimization model according to the user use data and the load node carbon potential of the energy load side; and respectively solving the upper-layer carbon price strategy model and the lower-layer load optimization model according to a preset optimization algorithm, and carrying out iterative game optimization on a calculation result to obtain an optimal transaction carbon price and a user response which meet preset requirements. The operation cost of the comprehensive energy system can be reduced, and low-carbon economical operation is achieved.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching and operation technology, and in particular to a method and apparatus for optimizing the carbon energy synergistic response of an integrated energy system. Background Technology

[0002] As a key market participant, integrated energy systems can fully tap the carbon reduction potential at each stage of production, conversion, and storage, aligning with my country's sustainable development philosophy of energy conservation and emission reduction. Carbon trading mechanisms, as a market-based system and policy guide for energy conservation and emission reduction, are crucial for enhancing the carbon emission reduction of integrated energy systems. Therefore, the low-carbon economic operation of integrated energy systems requires not only formulating carbon trading strategies based on electricity trading results but also considering the impact of carbon trading results on electricity trading.

[0003] In related technologies, integrated energy system operators (IESOs) and load aggregators (LAs) typically use fixed carbon prices to simplify the carbon trading process. This lack of fixed carbon prices incentivizes load-side responses in integrated energy systems and ignores the load-side's participation in carbon reduction within the integrated energy system, thereby increasing the system's carbon emissions and carbon emission costs.

[0004] Therefore, there is an urgent need for a method and device for optimizing the carbon energy synergistic response of integrated energy systems to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention provides a method and apparatus for optimizing the carbon energy synergistic response of an integrated energy system, which can reduce the operating costs of the integrated energy system and achieve low-carbon economic operation. The technical solution is as follows: On the one hand, a method for optimizing the carbon energy synergistic response of an integrated energy system is provided, the method comprising: Based on the operational data of the integrated energy system, a system carbon emission flow model is established to calculate the carbon potential of the load nodes of the integrated energy system; wherein, the integrated energy system includes an electric subsystem, a thermal subsystem, a natural gas subsystem, energy coupling equipment, and energy storage equipment; Based on the purchase and sale transaction data of the integrated energy system and the carbon potential of the load nodes, an upper-level carbon price optimization model is established with the objective function of maximizing system revenue and the constraint of equipment operation balance. Based on user usage data on the energy load side and the carbon potential of the load nodes, a lower-level load optimization model is established with the objective function of minimizing user costs and the constraint of demand response balance. The upper-level carbon price strategy model and the lower-level load optimization model are solved according to the preset optimization algorithm, and the calculation results are iteratively optimized through game theory to obtain the optimal trading carbon price and user response that meet the preset requirements.

[0006] On the other hand, a carbon energy synergistic response optimization device for an integrated energy system is provided, the device comprising: The first modeling module is used to establish a system carbon emission flow model for calculating the carbon potential of the load nodes of the integrated energy system based on the operating data of the integrated energy system; wherein, the integrated energy system includes an electric subsystem, a thermal subsystem, a natural gas subsystem, energy coupling equipment, and energy storage equipment; The second modeling module is used to establish an upper-level carbon price optimization model based on the purchase and sale transaction data of the integrated energy system and the carbon potential of the load nodes, with the objective function of maximizing system revenue and the constraint of equipment operation balance. The third modeling module is used to establish a lower-level load optimization model based on user usage data on the energy load side and the carbon potential of the load nodes, with the objective function of minimizing user costs and the constraint of demand response balance. The optimization module is used to solve the upper-level carbon price strategy model and the lower-level load optimization model according to the preset optimization algorithm, and to perform iterative game optimization on the calculation results to obtain the optimal trading carbon price and user response that meet the preset requirements.

[0007] On the other hand, a computer device is provided, the computer device including a memory and a processor, the memory for storing computer programs, and the processor for executing the computer programs stored in the memory to implement the steps of the carbon energy synergistic response optimization method of the integrated energy system described above.

[0008] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, the steps of the carbon energy synergistic response optimization method of the integrated energy system described above are implemented.

[0009] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the carbon energy synergistic response optimization method for the integrated energy system described above.

[0010] The technical solution provided by this invention can bring at least the following beneficial effects: First, it accurately quantifies the node carbon potential through a system carbon emission flow model of the load node carbon potential of the integrated energy system, transforming the abstract carbon footprint into calculable data, providing a basis for variable carbon prices, and solving the problem that fixed carbon prices cannot reflect spatiotemporal differences. Then, it establishes an upper-level carbon price optimization model for the integrated energy system, linking carbon potential to prices through a dynamic load carbon price strategy, reducing carbon purchase costs by optimizing equipment output, and maximizing benefits. Next, it constructs a lower-level load optimization model, enabling users to flexibly adjust their energy consumption behavior through demand response and cooperative game theory, thereby reducing total costs. Finally, it uses a preset optimization algorithm to quickly converge and solve the two-level model, overcoming the bottleneck of slow convergence in traditional algorithms, ensuring the real-time nature of game equilibrium, and ultimately achieving stable system benefits and reduced user costs, which has practical value in promoting low-carbon economic synergy between source and load sides. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a carbon energy synergistic response optimization method for an integrated energy system provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the integrated energy system operation framework provided in an embodiment of the present invention; Figure 3 This is a carbon emission flow diagram of an integrated energy system provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the basic data curve provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the IESO scheduling results under scenario 2 provided in an embodiment of the present invention; Figure 6 This is a schematic diagram comparing the carbon potential of LA under scenario 2 and scenario 3 provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the variable load carbon price curve for scenario 3 provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the step carbon potential-carbon valence optimization process under different algorithms provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of the electricity trading volume between different LAs in different scenarios provided by an embodiment of the present invention; Figure 10This is a schematic diagram of the curve of LA purchasing electrical power from IESO according to an embodiment of the present invention. Detailed Implementation

[0013] 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 some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] As mentioned earlier, the carbon reduction process of existing integrated energy systems lacks the incentive effect of variable load carbon prices on the load-side response of integrated energy systems, and ignores the participation of the load side in the system's carbon reduction.

[0015] Based on this, the concept of the present invention is to help promote carbon emission reduction in the integrated energy system from both the source and load sides by transferring the responsibility for carbon emission reduction from the source side to the load side.

[0016] The following describes the specific implementation of the above concept.

[0017] Please refer to Figure 1 The present invention provides a method for optimizing the carbon energy synergistic response of an integrated energy system, the method comprising: Step 100: Based on the operating data of the integrated energy system, establish a system carbon emission flow model for calculating the carbon potential of the load nodes of the integrated energy system; wherein, the integrated energy system includes an electric subsystem, a thermal subsystem, a natural gas subsystem, energy coupling equipment, and energy storage equipment; Step 102: Based on the purchase and sale transaction data of the integrated energy system and the carbon potential of the load nodes, establish an upper-level carbon price optimization model with the objective function of maximizing system revenue and the constraint of equipment operation balance. Step 104: Based on the user usage data on the energy load side and the carbon potential of the load nodes, establish a lower-level load optimization model with the objective function of minimizing user costs and the constraint of demand response balance. Step 106: Solve the upper-level carbon price strategy model and the lower-level load optimization model according to the preset optimization algorithm, and perform iterative game optimization on the calculation results to obtain the optimal trading carbon price and user response that meet the preset requirements.

[0018] In this embodiment of the invention, the carbon potential of nodes is first accurately quantified using a system carbon emission flow model of the load nodes in an integrated energy system. This transforms the abstract carbon footprint into computable data, providing a basis for variable carbon prices and solving the problem that fixed carbon prices cannot reflect spatiotemporal differences. Subsequently, an upper-level carbon price optimization model for the integrated energy system is established. Through a dynamic load carbon price strategy, carbon potential is linked to prices, and carbon purchase costs are reduced by optimizing equipment output, maximizing revenue. Next, a lower-level load optimization model is constructed. Through demand response and cooperative game theory, users can flexibly adjust their energy consumption behavior, reducing total costs. Finally, a pre-set optimization algorithm is used to quickly converge and solve the two-level model, overcoming the bottleneck of slow convergence in traditional algorithms, ensuring the real-time nature of game equilibrium, and ultimately achieving stable system revenue and reduced user costs. This has practical value in promoting low-carbon economic synergy between source and load sides.

[0019] The following description Figure 1 The execution method of each step is shown.

[0020] First, for step 100, based on the operating data of the integrated energy system, a system carbon emission flow model is established to calculate the carbon potential of the load nodes of the integrated energy system.

[0021] Considering that the load side is the primary bearer of carbon emissions, this invention introduces carbon flow theory to accurately measure load-side carbon emissions. Then, based on load carbon potential, a load carbon pricing strategy is proposed, allowing users to intuitively perceive differences in load carbon potential across different time periods and achieve low-carbon energy use through demand response. To ensure individual users bear carbon emission responsibility and participate in the low-carbon economic operation of the integrated energy system, the concepts of Integrated Energy System Operator (IESO) and Load Aggregator (LA) are introduced. Against this backdrop, this embodiment proposes the following... Figure 2 The integrated energy system operation framework is shown.

[0022] The decisions of both the IESO and the LA will affect each other's costs and benefits. Based on grid electricity prices and natural gas prices, the IESO provides electricity and heat to the LA through energy conversion, energy storage, and photovoltaic equipment. While meeting the diverse load demands of the LA, it can also participate in the carbon trading market, using market mechanisms to achieve integrated energy system emission reduction targets and reduce carbon emission costs. Passing on the responsibility for carbon emissions at the source to the LA helps it adopt low-carbon energy consumption habits. Therefore, the IESO, as the leader, calculates the carbon potential of each energy system node, with the optimization objective of maximizing total revenue, and uses the RIME algorithm to solve for energy prices and load carbon prices, thus allocating carbon emission costs to the LA.

[0023] As a follower, the LA responds to demand based on the energy prices and load carbon prices provided by the IESO. Besides purchasing energy from the IESO, LA users can also enhance their energy exchange potential through shared energy storage leasing. When a user's own energy demand is high, they can purchase energy from other users and sell it back when their demand is low. To protect the privacy of information among users, the only information exchanged is the volume and price of energy transactions. Users adjust their own energy demand, the volume and price of energy transactions between users at different times through cooperative game theory, which satisfies both the goal of minimizing LA's total cost and the reasonable distribution of cooperative profits among users. This process iterates until the transactions among all users in LA reach Nash equilibrium, and LA's energy purchase demand is transmitted to the IESO. Then, the IESO adjusts its energy price and load carbon price strategies based on LA's energy purchase demand. This process iterates repeatedly until a master-slave game equilibrium is found between LA and the IESO.

[0024] The above analysis shows that the master-slave game between LA and IESO, and the cooperative game among users within LA, constitute the integrated energy system operation framework in this embodiment. By transferring the responsibility for carbon emission reduction from the source side to the load side, it helps to jointly promote carbon emission reduction of the integrated energy system from both the source and load sides.

[0025] In this embodiment of the invention, the carbon emission flow model is established through the following steps.

[0026] Specifically, based on the concept of "electricity-carbon coupling", carbon emission intensity is attributed to the load side, and carbon flow rate (CEFR), nodal carbon potential (NCI), branch carbon potential (BCI), source carbon potential (GCI), and port carbon potential (PCI) are used to characterize the average carbon emissions per unit of energy, with units of ton / kWh, ton / h, ton / h, and ton / h, respectively.

[0027] like Figure 3 As shown, the integrated energy system's source side consists of photovoltaic power, the power grid, and natural gas. Source-side carbon emissions originate from carbon emissions during power generation by coal-fired units in the power grid and carbon emissions generated during natural gas combustion. The carbon potential of the power grid and the gas source at time t is respectively determined by… and The carbon potential at the ports of the natural gas flowing into the gas boiler (GB) and gas turbine (GT) is represented by [symbols]. and Indicated. GB output port carbon potential used. The combined heat and power (CHP) unit, consisting of a gas turbine (GT), a waste heat boiler (WHB), and an organic Rankine cycle (ORC), can flexibly adjust its output electrical and thermal power. The carbon potentials at the output ports of GT, WHB, and ORC are denoted as... , , and Furthermore, electrical and thermal energy storage can achieve bidirectional flow of energy and its carbon emissions, and the carbon potential at time t can be expressed as follows: and Represented. The load side includes electrical load and thermal load, and the load node carbon potential at time t is represented by... and express.

[0028] Based on this, the grid node carbon potential, which characterizes the sum of carbon flow rates injected into the power lines, is calculated according to the power flow distribution and power source carbon potential of the power subsystem.

[0029] Specifically, the carbon emission flow model for the power subsystem is shown in the following formula: In the formula: Let NCI be the NCI of power subsystem node i at time t; Let i be the set of power lines ending at node i. Let i be the set of power sources located at node i; Let be the line power flowing from node j to node i at time t; The BCI of line ji at time t; Let ps be the power supply power at time t; The GCI of the power supply ps at time t.

[0030] The BCI of each line is equal to the NCI of the inflow node, as shown in the following formula: Combining the above formulas, the NCI of each node and the BCI of each branch in the power subsystem can be calculated based on the power flow distribution and the source GCI: In the formula: Let NCI be the NCI of power system node j at time t.

[0031] Furthermore, the carbon potential of the heat network nodes is calculated based on the pipeline flow rate of the thermal subsystem and the carbon flow rate of the heat network; wherein, the carbon flow rate of the heat network is determined by the specific heat capacity of the medium and the temperature difference.

[0032] Specifically, the heat power of the heat source can be transported to the load side through heating pipelines, and the carbon emission flow model of the thermal subsystem is shown in the following formula: In the formula: Let NCI be the heating node i at time t; Let i be the set of heating pipes ending at node i. BCI of heating pipe ji at time t; Specific heat capacity of the liquid in the heating pipeline; This refers to the flow rate of the heating pipeline. and These are the inlet and outlet temperatures of the heating pipe at time t, respectively.

[0033] Furthermore, the carbon potential of the natural gas network nodes is calculated based on the carbon potential of the gas source and the carbon flow rate of the pipeline.

[0034] Specifically, similar to the power subsystem, the carbon emission flow model for the natural gas subsystem is shown in the following formula: In the formula: Let NCI be the natural gas system node i at time t; Let i be the set of gas pipelines ending at node i. Let i be the set of gas sources located at node i; Let be the carbon flow rate of the gas pipeline ji at time t; Let be the gas flow rate from node j to node i at time t; Let gs be the gas flow rate of the gas source at time t; The GCI of the gas source gs at time t is taken as 2.09 ton / km. 3 ; The calorific value of natural gas is taken as 10.45 MWh / km. 3 .

[0035] In this embodiment of the invention, the coupling relationship between carbon potential and power at the input and output ports of the energy coupling device is determined based on the principle of carbon potential conservation.

[0036] Specifically Figure 3 The energy coupling devices shown include gas turbines, waste heat boilers, gas-fired boilers, and organic Rankine cycles. This section classifies energy coupling devices into single-input multiple-output (SIMO) devices, such as GT, and single-input single-output (SISO) devices, such as WHB, GB, and ORC, analyzes their energy flow-carbon emission flow mapping relationship, and establishes carbon emission flow models for energy coupling devices.

[0037] For SIMO equipment, according to the principle of carbon emission conservation, the CEFR injected into the SIMO equipment should be equal to the CEFR flowing out of the equipment. Taking GT as an example, its PCI is as follows: Assuming that the PCI of the electrical output port and the thermal output port are inversely proportional to their conversion efficiency, as shown below: The PCI relationship between the input and output terminals of a SIMO device can then be rewritten as follows: In the formula: Let GT be the PCI of the natural gas inflow port at time t; and These are the PCI ports for the GT electrical output port and thermal output port at time t, respectively. Let be the flow rate of natural gas flowing into GT at time t; and Let GT be the electric power and thermal power at time t, respectively. and The values ​​are 0.29 and 0.51, representing the power generation efficiency and heating efficiency of GT, respectively.

[0038] SISO equipment should also adhere to the principle of carbon emission conservation, and its PCI is shown in the following formula: In the formula: and These are the PCI values ​​for the input and output ports of SISO device i at time t, respectively. and Let be the input power and output power of SISO device i at time t, respectively; The energy conversion efficiency of the SISO device i.

[0039] Taking gas-fired boilers, waste heat boilers, and organic Rankine cycle equipment as examples, the conversion relationship between their input and output ports (PCI) is shown in the following formula: In the formula: , , PCI for gas-fired boilers, waste heat boilers, and organic Rankine cycle equipment at time t, respectively. PCI of the natural gas inflow port of GB at time t; GB heating efficiency; and , respectively, are the distribution coefficients of the GT heat power at time t; and The values ​​are 0.76 for the heating efficiency of the waste heat boiler and 0.8 for the power generation efficiency of the organic Rankine cycle equipment.

[0040] In this embodiment, the energy storage carbon potential used to react to changes in carbon stock is calculated based on the state of charge.

[0041] Specifically, energy storage devices are an important means of addressing the uncertainties of renewable energy generation and enhancing the flexibility of energy systems. Combining the calculation method for state of charge (SOC), the GCI of electrical and thermal energy storage at time t is shown in the following formula: In the formula: , GCI of electrical and thermal energy storage at time t, respectively; , These represent the remaining electrical and thermal energy storage capacities at time t, respectively. and These represent the charging and discharging power of the energy storage at time t; and These represent the thermal energy storage power and the heat release power at time t, respectively. , These are the NCIs of the nodes where the electrical and thermal energy storage is located at time t, respectively.

[0042] For step 102, based on the purchase and sale transaction data of the integrated energy system and the carbon potential of the load nodes, an upper-level carbon price optimization model is established with the objective function of maximizing system revenue and the constraint of equipment operation balance.

[0043] In this embodiment of the invention, the system revenue is determined based on the energy sales revenue, carbon emission revenue, energy purchase cost, and carbon trading cost of the integrated energy system.

[0044] Based on the LA electric heating load demand and its carbon potential, IESO can formulate a load carbon pricing strategy by optimizing the output of each device and pass it to the lower-level model. IESO establishes the following equation with revenue maximization as the optimization objective: In the formula: This represents the total revenue of IESO; This is revenue from the sale of energy by IESO; Carbon revenue for IEO, which is the carbon emission fee paid by LA to IEO; The cost of energy that IESO purchases from the grid and gas sources; This represents the carbon trading cost for IEO.

[0045] Specifically, revenue from energy sales is calculated using the following formula: In the formula: T is the total number of time periods within the scheduling cycle; , These represent the ISO electricity price at time t and the electricity power purchased by LA from the ISO, respectively. , The values ​​represent the ISO heat price at time t and the heat power purchased by LA from ISO, respectively.

[0046] Carbon emission revenue is calculated using the following formula: In the formula: , These are the LA electrical load carbon potential at time t and the IESO-defined electrical load carbon valence for LA, respectively. , These represent the LA thermal load carbon potential at time t and the IESO-defined thermal load carbon valence for LA, respectively.

[0047] Energy purchase cost is calculated using the following formula: In the formula: , These are the grid electricity price and natural gas price at time t, respectively; This refers to the amount of electricity that IEO purchases from the grid; This represents the amount of gas that ISO purchases from its gas source.

[0048] Carbon trading costs are calculated using the following formula: Based on the carbon trading market mechanism, the IESO carbon trading cost is calculated using a tiered carbon trading model, as shown in the following formula: In the formula: Carbon emission allowances that IESO needs to purchase; The base carbon price is set at 0.275 yuan / kg; The interval step size is set to 2.5 tons. The price increase is set at 25%. , These represent ISO's actual carbon emissions and carbon emission allowances, respectively.

[0049] The actual carbon emissions of the IESO can be calculated based on the GCI of the power grid and gas source, as shown below: According to China's principles for the determination and allocation of carbon allowances, the carbon emission allowances held by IESO are shown in the following formula: In the formula: , These are the GCIs for municipal power grids and gas sources, respectively. The carbon allowance coefficient for the power grid is 0.7 kg / kWh. The carbon quota factor for natural gas is 0.185 kg / kWh.

[0050] Furthermore, the constraints are determined based on the upper and lower limits of energy equipment output and ramping limits in the integrated energy system, as well as the system power balance.

[0051] Specifically, by Figure 3 It is known that a combined heat and power (CHP) unit consisting of a gas turbine (GT), a waste heat boiler (WHB), and an organic Rankine cycle (ORC) can flexibly adjust its output thermal and electrical power. Specifically, the ORC utilizes waste heat to generate electricity, while the WHB uses waste heat to supply heat to the LA (Lower Ambient Air).

[0052] In the formula: The upper limit of GT's electric power is set at 1.2 MW; The maximum ramp power for GT is set at 0.2 MW. Let WHB be the thermal power at time t; The upper limit of WHB thermal power is set at 0.65 MW; Let be the ORC electric power at time t; The upper limit of ORC power is set at 0.63 MW; The upper limit of ORC ramp power is set at 0.13 MW.

[0053] When the WHB (heat boiler) cannot meet the LA (heat load) demand, the gas-fired boiler can be activated for auxiliary heating. The constraint equations for the gas-fired boiler are shown below: In the formula: Let GB be the thermal power at time t; Let be the flow rate of natural gas flowing into GB at time t; The upper limit of GB thermal power is taken as 0.65 MW; The upper limit of the GB ramp power is set at 0.12 MW.

[0054] Other equipment constraints mainly include photovoltaic power output constraints and energy storage equipment constraints, as shown in the following formula: In the formula: Let be the photovoltaic power at time t; This is the upper limit of photovoltaic power output; , The charging and discharging efficiency of the energy storage device is taken as 0.96. The upper limit of the charging and discharging power of the electric energy storage is set at 0.22 MW; , The upper and lower limits of the remaining capacity of the energy storage are set at 0.54 MW and 0.054 MW, respectively. , These are the remaining capacities of the energy storage devices at the start and end of the scheduling cycle, respectively. , The heat storage and heat release efficiencies of thermal energy storage are respectively taken as 0.92; The upper limit for thermal energy storage or heat release is set at 0.2 MW. , The upper and lower limits of the remaining thermal energy storage capacity are set at 0.48 MW and 0.048 MW, respectively. , These represent the remaining capacity of the thermal energy storage equipment at the start and end of the scheduling cycle, respectively.

[0055] In addition, IESO must also meet the energy supply and demand balance constraint: In the formula: This refers to the total number of users included in LA. , These are the electrical load and thermal load of the nth user within LA, respectively.

[0056] Finally, based on the carbon potential of the load nodes, a load carbon price model for the upper-level carbon price optimization model is established.

[0057] Specifically, to effectively guide LA (Laminated Load) demand response in accordance with carbon potential, this section establishes a load carbon price model based on the consumption characteristics of GT (Gross Load) units, as shown in the following formula: In the formula, , These are the carbon prices for electrical load and thermal load, respectively. , The load node carbon potentials are the electrical load and thermal load at time t, respectively.

[0058] Taking the carbon price of electricity load as an example, this section explains the calculation method of the load carbon price model coefficients. First, the LA (electric load) electricity load carbon potential is divided into interval levels, as shown in the following formula: The stepped boundary carbon valence is shown in the following formula: In the formula: , , The carbon potential range boundaries for the electrical load nodes are taken as 0.32 kg / kWh, 0.55 kg / kWh, and 0.78 kg / kWh. Similarly, , , The carbon potential range boundaries for the heat load nodes are taken as 0.3 kg / kWh, 0.4 kg / kWh, and 0.6 kg / kWh; , , The carbon valence intervals corresponding to the carbon potential levels of electrical load nodes are defined as 0.384 yuan / kg, 0.66 yuan / kg, and 0.936 yuan / kg. Similarly, , , The carbon valence intervals corresponding to the carbon potential levels at the heat load nodes are set at 0.42 yuan / kg, 0.576 yuan / kg, and 0.732 yuan / kg.

[0059] Define the y-axis as carbon price and the x-axis as carbon potential. Based on the range of LA carbon potential and carbon price, the coefficients of the electricity load carbon price model can be calculated according to the principle of constant average price.

[0060] by Let's take an example to illustrate. Assume there is a straight line. and The areas enclosed are equal. The calculation method for the coefficients of the electricity load carbon price model is shown in the following formula: In the formula: Let y be the slope of the line y.

[0061] The above analysis shows that after introducing the carbon emission flow model, the load carbon price and load carbon potential are positively correlated, which can better transmit carbon emission responsibility to LA and incentivize them to adopt low-carbon energy use patterns.

[0062] Then, for step 104, based on the user usage data on the energy load side and the carbon potential of the load nodes, a lower-level load optimization model is established with the minimum user cost as the objective function and demand response balance as the constraint.

[0063] In this embodiment of the invention, the user cost is first determined based on the user's energy purchase cost, the user's carbon emission cost, the user's shared energy storage leasing cost, the user's electricity trading revenue, and the user's revenue from demand response.

[0064] In LA, each user optimizes with the goal of minimizing total cost, as shown in the following formula: In the formula: This represents the total cost for the nth user. The energy purchase cost for the nth user; The carbon emission cost paid for the nth user; The shared energy storage leasing cost for the nth user; The electricity trading revenue for the nth user; Benefits from responding to the needs of each user.

[0065] Existing research indicates that both electrical and thermal loads can be categorized into stationary loads, shiftable loads (SL), and interruptible loads (IL). Each user can adjust their load demand in a spatiotemporal dimension through cooperative game theory, based on energy prices and load carbon prices set by the IESO. Then, the LA (Load Allocation) system aggregates the load demands of all users and transmits them to the upper-level model.

[0066] Energy purchase cost is calculated using the following formula: The cost of carbon emissions is calculated using the following formula: The cost of shared energy storage leasing is calculated using the following formula: In the formula: , These are the leasing cost coefficients for shared energy storage capacity and power, respectively. The capacity leased by the nth user from the shared energy storage; , These represent the charging and discharging power leased by the nth user to the shared energy storage service.

[0067] Electricity trading revenue is calculated using the following formula: In the formula: Let t be the price of electricity traded between the nth user and the i-th user; This is the power transfer direction coefficient, which is used when user n transfers power to user i. When user i transfers power to user n, ; Let t be the power exchange between the nth user and the ith user.

[0068] Demand response benefits are calculated using the following formula: In the formula: , These are the compensation coefficients for the interruptible power load and the transferable power load of the nth user, respectively, and are set to 0.7 yuan / kWh and 0.37 yuan / kWh. , These represent the load reduction and load transfer amounts for the nth user, respectively. , These are the compensation coefficients for the interruptible heat load and the shiftable heat load of the nth user, respectively, and are set to 0.55 yuan / kWh and 0.24 yuan / kWh. , These represent the heat load reduction and heat load transfer for the nth user, respectively.

[0069] Furthermore, in this embodiment, the constraints are determined based on the user's shared energy storage operation constraints, the power exchange constraints between users, and the load demand response constraints.

[0070] Specifically, when each user uses shared energy storage, the remaining capacity and charging / discharging power constraints are as shown in the following formula: In the formula: Let n be the remaining capacity for the nth user at time t; , These are the charging and discharging efficiencies of shared energy storage, respectively. , These represent the charging and discharging power of the nth user at time t.

[0071] The power exchange constraints between users in LA are shown in the following formula: In the formula, The upper limit for power exchange between users is set at 0.2 MW.

[0072] The load demand response constraints are illustrated using electrical load as an example to explain the calculation process for interruptible and shiftable loads. The calculation for thermal load is similar and will not be repeated here.

[0073] The interruptible load (IL) model is shown in the following formula: In the formula: , Let be the interruptible electrical load before and after the nth user demand response at time t; Let t be the load reduction amount for the nth user. Let t be the maximum load reduction for the nth user at time t.

[0074] The total amount of transferable load needs to remain constant within a scheduling cycle. Therefore, the transferable load (SL) model is shown in the following formula: In the formula: , Let be the transferable electrical load before and after the nth user demand response at time t; Let be the load transfer amount for the nth user at time t; , These represent the input and output of the electrical load of the nth user at time t, respectively. Let t be the maximum load transfer amount for the nth user at time t.

[0075] The load model before and after responding to the nth user demand is shown in the following formula: In the formula: , These represent the electrical loads before and after the nth user demand response at time t; Let n be the fixed electrical load of the nth user at time t; , These represent the electrical loads before and after the nth user demand response at time t; Let t be the fixed electrical load of the nth user at time t.

[0076] Finally, this embodiment establishes a cooperative game model for the lower-level load optimization model based on the minimum user cost.

[0077] Specifically, in order to achieve a fair distribution of cooperative benefits by characterizing the cooperation mechanism among users in LA while minimizing the total cost of LA, this embodiment constructs a Nash negotiation cooperative game model for LA, as shown in the following formula: In the formula, This represents the total cost for the nth user. The negotiation breakdown point for the nth user; N LA This represents the total number of users.

[0078] For step 106, the upper-level carbon price strategy model and the lower-level load optimization model are solved according to the preset optimization algorithm, and the calculation results are iteratively optimized by game theory to obtain the optimal trading carbon price and user response that meet the preset requirements.

[0079] In this embodiment of the invention, solving the upper-level carbon price strategy model and the lower-level load optimization model includes the following steps: First, the energy price and load carbon price determined by the upper-level carbon price strategy model are initialized.

[0080] Specifically, the IESO first sets initial energy prices and load carbon prices for LA. The IESO strategy set can be represented as follows: LA (Independent Energy Provider) uses IEO (Independent Energy Provider) energy prices and load carbon prices for demand response to determine the amount of electricity and heat it should purchase from IEO. LA's strategy set can be represented as... Based on the updated LA energy purchase demand, IESO optimizes equipment power output and the amount of electricity and gas purchased from the grid and gas sources. Based on the concept of "electricity-carbon coupling," energy flow and carbon flow are calculated. The above steps are repeated until neither side can obtain a better solution by changing their own decisions, i.e., Stackelberg equilibrium is achieved.

[0081] Then, the initialization results are explored globally based on the dynamically changing adaptive inertia weights, and a local fine search is performed on the global exploration results to obtain the updated energy price and load carbon price.

[0082] Specifically, this embodiment uses the RIME algorithm to solve the upper-level model. Its step-by-step search strategy helps to escape local optima and find the global optimum. A global exploration is performed through a soft frost search phase, followed by a local fine-tuning search through a hard frost intersection phase, thus ensuring both the accuracy and speed of solving the upper-level model, as shown in the following formula: In the formula: For particles The new location; The j-th particle is the optimal individual in the population. A random number between (-1, 1); Indicates environmental factors; represents the direction of particle motion; h represents the adhesion degree, which is a random number between (0, 1); and represents the upper and lower bounds of the j-dimensional space; M is the attachment coefficient; It is a random number between (0, 1).

[0083] In the formula: A random number between (-1, 1); Let be the normalized fitness value of individual i.

[0084] As can be seen from the above equations, the balance between global and local optimization in RIME is affected by... , The impact is significant, with optimization efficiency in the early stages of iteration being relatively low. Weight-related. Therefore, this section defines adaptive inertia weights, which are reduced in the early stages of iteration. Weights are used to improve global optimization capabilities. In the later stages of iteration, The weights are decreased non-linearly to balance global and local optimization capabilities. Furthermore, using a uniformly distributed method to generate random numbers may lead to… Uneven value distribution affects the global optimization capability. and The same problem exists. To enhance its randomness and ergodicity, this section uses Piecewise mapping to generate chaotic sequences as an improvement. , and The improved calculation method reduces the probability of RIME getting trapped in local optima. In summary, the improved update model for the search and crossover phases is shown in the following formula: In the formula: For adaptive inertia weights; and α and β are weighting factors; α and β are adjustment parameters; z and These represent the current iteration number and the maximum iteration number in the master-slave game, respectively.

[0085] Furthermore, the lower-level load optimization model is decomposed into subproblems to obtain a parallel cost-minimum model and a cooperative benefit allocation model.

[0086] Specifically, the Nash negotiation cooperation game model of LA is essentially a non-convex nonlinear optimization problem, which can be decomposed into the subproblem of minimizing the total cost of LA and the subproblem of allocating cooperation benefits.

[0087] Subproblem 1: Minimize the total cost of LA, as shown in the following formula: Sub-problem 2: Distribution of cooperative benefits, as shown in the following formula: In the formula: , , , , These are the optimal power interaction among users, the optimal energy purchase cost for each user, the carbon emission cost, the shared energy storage leasing cost, and the demand response revenue obtained from subproblem 1.

[0088] Finally, based on the independently optimized decision variables of each user, the minimum cost model and the cooperative benefit distribution model are coordinated and optimized to obtain a demand response scheme that meets the requirements of global consistency.

[0089] Specifically, the multi-user cooperative game model in LA is a distributed optimization problem. Considering privacy protection and information transmission issues among users, an adaptive ADMM can be used to continuously update and effectively limit the randomness of the penalty parameters. This section uses Python to call the GUROBI solver to solve the lower-level model in the master-slave game problem. At the end of the cooperative game, the convergence accuracy constraints of the original residuals and the dual must be satisfied, as shown in the following formula: In the formula: This represents the original residual obtained in the v-th iteration; The dual residual obtained in the v-th iteration; This is the penalty parameter during the v-th iteration; , These represent the convergence accuracy of the original residual and the dual residual, respectively. and These represent the current iteration number and the maximum iteration number for the cooperative game, respectively.

[0090] After obtaining the above calculation results, iterative game optimization can be performed on the calculation results to obtain the optimal trading carbon price and user response that meet the preset requirements. This includes the following steps: updating the user's energy purchase demand according to the updated energy price and load carbon price of the system, and updating the energy price and load carbon price again according to the new energy purchase demand of the user.

[0091] Subsequently, based on the preset convergence conditions, it is determined whether the changes in energy prices and load carbon prices before and after the update are both less than the preset convergence accuracy. If so, it is determined that the integrated energy system and the user end have reached a game equilibrium, and the optimal trading carbon price and user response in the equilibrium state are output.

[0092] For example, the master-slave game can be considered converged when the changes in energy prices and load carbon prices are less than or equal to a given convergence precision, as shown in the following formula: In the formula, The preset electrical load convergence accuracy; The preset thermal load convergence accuracy.

[0093] The following example demonstrates the practicality of the above method.

[0094] The integrated energy system dispatch cycle in the demonstration zone is 24 hours, with a dispatch interval of 1 hour. Typical daily electricity and heat purchase demand, and photovoltaic power generation output are as follows: Figure 4 As shown in (a). The GCI of the power grid and gas source point is as follows. Figure 4As shown in (b). The price range for electricity purchased by IESO from the grid is [0.2, 1.2] yuan / kW, and the price of natural gas is 0.4 yuan / kW. Detailed parameters of the various devices within the integrated energy system can be found in previous chapters and will not be repeated here.

[0095] To verify the positive role of variable load carbon price and demand response in optimizing the operation of a low-carbon integrated energy system, this section presents three comparative scenarios. The IESO revenue and total LA cost for each scenario are shown in Table 1.

[0096] Scenario 1: IESO considers carbon emission costs, load carbon price is fixed, and LA does not participate in demand response.

[0097] Scenario 2: IESO considers carbon emission costs, load carbon price is fixed, and LA participates in demand response.

[0098] Scenario 3: The strategy model proposed in this invention, namely, IESO considers carbon emission costs, load carbon price is variable, and LA participates in demand response.

[0099] Table 1 Comparison of IESO and LA scheduling results under different schemes Compared to Scenario 1, Scenario 2, considering demand response, resulted in a 24.47% decrease in total IESO revenue. However, the total carbon emissions of the integrated energy system decreased by 11.583 tons, and the IESO's carbon purchase cost also decreased by 38.278%. Specifically, LA's energy purchase cost and carbon purchase cost decreased by 6.66% and 25.886%, respectively. Due to the decrease in LA's demand for energy from the IESO, the IESO's energy purchase cost also decreased by 2.952%. This demonstrates that passing on carbon emission responsibility to the load side can incentivize users to engage in demand response, contributing to the low-carbon operation of the integrated energy system.

[0100] Scenario 3 employs a variable load carbon price to deeply optimize load-side energy demand from a carbon potential perspective. Compared to Scenario 2, the total carbon emissions of the integrated energy system in Scenario 3 further decreased by 2.982 tons, and the IESO carbon purchase cost also decreased by 21.1%. Although the IESO carbon sales revenue decreased by 2.877%, the carbon sales profit (carbon sales revenue - carbon purchase cost) in Scenario 3 increased by 6.175%.

[0101] Therefore, by comprehensively considering the variable load carbon price and demand response, the master-slave game model can reduce the total cost of LA while ensuring the economic efficiency of IESO, so as to achieve low-carbon optimized operation of the integrated energy system.

[0102] In Scenario 3, the combined heat and power (CHP) unit, composed of GT, ORC, and WHB, can flexibly adjust its output electrical and thermal power. For example, between 01:00 and 06:00, both electrical and thermal load demands gradually increase. As the electrical load increases, the electrical power output of GT also increases. With a fixed GT heat ratio, the thermal power output of GT also increases. At this time, the thermal load demand during this period can be met by increasing the heat supplied by GT to WHB and decreasing the heat supplied by GT to ORC for power generation. Similarly, between 08:00 and 18:00, thermal load demand is lower, while electrical load demand is higher. At this time, the electrical load demand during this period can be met by decreasing the heat supplied by GT to WHB and increasing the heat supplied by GT to ORC for power generation. It is worth noting that although the IESO's gas purchase cost is higher than its electricity purchase cost between 08:00 and 10:00, the IESO still maintains GT's electrical power output and increases ORC power generation to meet the increasing electrical load demand. The main reason is that the carbon potential of gas sources is lower than that of power sources during this period. The increase in energy purchase costs can be offset by reducing carbon purchase costs, thereby reducing carbon emissions from the integrated energy system.

[0103] Therefore, by scheduling equipment in the energy conversion and energy storage stages, IESO's energy sales profits can be effectively increased while reducing the carbon emissions of the integrated energy system.

[0104] This section verifies the effectiveness of the variable load carbon price by comparing the integrated energy system dispatch results under scenarios 2 and 3. The electrical and thermal power of the IESO in scenario 2 is as follows: Figure 5 a and Figure 5 As shown in b. The energy purchase demand and load carbon potential after LA demand response in scenarios 2 and 3 are as follows: Figure 6 a and Figure 6 As shown in b.

[0105] Compared to Scenario 2, Scenario 3 features more frequent charging and discharging of energy storage and a more rational scheduling. From 1 PM to 2 PM, energy storage reduces the carbon potential of the LA load nodes by releasing its stored low-carbon-potential energy, helping to lower the carbon emission costs for LA. During this period, the LA load carbon potential decreases from 0.3 kg / kWh and 0.312 kg / kWh in Scenario 2 to 0.25 kg / kWh and 0.266 kg / kWh in Scenario 3. Therefore, the IESO does not need to purchase more high-carbon-potential energy from the grid during this period, increasing carbon sales profits by reducing carbon purchase costs. From 9:00 AM to 12:00 PM, the IESO purchases more low-carbon-potential energy from the grid and fully utilizes photovoltaic power generation to meet the electricity demand of LA. During this period, the IESO can increase carbon sales revenue by incentivizing LA electricity consumption, thus increasing carbon sales profits.

[0106] Furthermore, variable load carbon prices can guide more frequent heat storage and release in thermal energy storage through price signals. Compared to Scenario 2, the thermal energy storage operation plan in Scenario 3 is more rational. The main reason is that Scenario 2 uses a fixed load carbon price, which prevents the load side from actively adjusting its energy demand by sensing price signals, affecting the carbon reduction effect on the load side and leading to an increase in the total carbon emissions of the integrated energy system. On the other hand, variable load carbon prices help IESO to rationally formulate energy storage operation strategies, increasing carbon sales profits by reducing carbon purchase costs or increasing carbon sales revenue, demonstrating the effectiveness of the strategy of this invention.

[0107] The variable load carbon price curve for Scheme 3 is as follows: Figure 7 a and Figure 7 As shown in b, the variable load carbon price reflects the cleanliness of energy used by LAs. The less clean energy LAs use, the higher the carbon potential at the load node, and the higher the load carbon price during high carbon potential phases compared to low and medium carbon potential phases. These price signals are more incentivizing LAs to change their energy consumption patterns, shifting some energy demand from periods with higher load carbon prices to periods with lower load carbon prices, thus reducing users' carbon emission costs. Simultaneously, the optimal solution for IESO (Enhanced Environmental Societies) benefits can be found within each load carbon price range, achieving a win-win situation for multiple stakeholders.

[0108] In Scenario 3, the high carbon potential of electrical load nodes and peak electricity prices overlap between 6 PM and 8 PM. To reduce total costs, LA reduces some of its electricity load through demand response or shifts some load to nighttime periods when load nodes have low carbon potential and off-peak electricity prices. Furthermore, to further reduce total costs, LA also reduces some load during daytime periods with high carbon potential at load nodes to increase demand response revenue. Due to the higher IESO heat sales price at night, LA reduces heat load throughout the entire dispatch cycle.

[0109] The optimization process of algorithms such as RIME, AOA, JS, and WOA for the master-slave game between IESO and LA, such as Figure 8 a and Figure 8 As shown in b, as the iteration process progresses, IESO continuously optimizes energy prices and load carbon prices with the goal of increasing its own revenue. Simultaneously, LA continuously optimizes its energy purchase demand based on price information, gradually reducing its total cost. When neither party can further increase revenue or reduce costs through strategy changes, IESO and LA reach equilibrium at 26,120 yuan and 108,790 yuan, respectively. Compared with other algorithms, RIME improves the equilibrium of the inter-subject game outcome, which is conducive to achieving a win-win situation among multiple subjects.

[0110] To verify the positive effects of shared energy storage leasing and inter-user power exchange on reducing total energy storage (LA) costs, this section presents three comparative scenarios. Table 2 shows the IESO revenue and LA costs under different scenarios.

[0111] Scenario 3: The strategy model proposed in this invention involves LA leasing and sharing energy storage, allowing users to exchange electrical energy.

[0112] Scenario 4: LA does not lease shared energy storage, and users can exchange electrical energy.

[0113] Scenario 5: LA rental shared energy storage, with no power exchange between users.

[0114] Table 2 Comparison of IESO and LA scheduling results under different schemes Compared to scenario 4, the total cost of LA in scenario 3 is reduced by 2.465%. The power interaction curves between users in scenarios 3 and 4 are shown below. Figure 9 a and Figure 9 As shown in b. After leasing shared energy storage, the amount of electricity exchanged between users also increased. The power purchased by LA from the IESO in scenarios 3 and 4 is as follows: Figure 10 As shown in the diagram. Compared to Scenario 4, although the IESO profit in Scenario 3 decreased by 4.006%, the LA in Scenario 3 purchased less electricity from the IESO during peak electricity price periods. This not only helps alleviate the power supply pressure on the IESO, but also helps reduce the carbon emissions of the integrated energy system during periods of high carbon potential at load nodes.

[0115] Compared to scenario 5, scenario 3, by taking into account power interaction between users, can reduce LA's dependence on IEO for power supply, enabling LA to have a fairer trading right with IEO. This not only ensures a balance of interests among users but also promotes cooperation among them.

[0116] This invention provides a carbon energy synergistic response optimization device for an integrated energy system, the device comprising: The first modeling module is used to establish a system carbon emission flow model for calculating the carbon potential of the load nodes of the integrated energy system based on the operating data of the integrated energy system; wherein, the integrated energy system includes an electric subsystem, a thermal subsystem, a natural gas subsystem, energy coupling equipment, and energy storage equipment; The second modeling module is used to establish an upper-level carbon price optimization model based on the purchase and sale transaction data of the integrated energy system and the carbon potential of the load nodes, with the objective function of maximizing system revenue and the constraint of equipment operation balance. The third modeling module is used to establish a lower-level load optimization model based on user usage data on the energy load side and the carbon potential of the load nodes, with the objective function of minimizing user costs and the constraint of demand response balance. The optimization module is used to solve the upper-level carbon price strategy model and the lower-level load optimization model according to the preset optimization algorithm, and to perform iterative game optimization on the calculation results to obtain the optimal trading carbon price and user response that meet the preset requirements.

[0117] In this embodiment of the invention, establishing a system carbon emission flow model for calculating the carbon potential of load nodes in the integrated energy system based on the operating data of the integrated energy system includes: calculating the grid node carbon potential, which characterizes the sum of carbon flow rates injected into power lines, based on the power flow distribution and power source carbon potential of the power subsystem; calculating the heating network node carbon potential based on the pipeline flow rate and heating network carbon flow rate of the thermal subsystem; wherein the heating network carbon flow rate is determined by the specific heat capacity of the medium and the temperature difference; calculating the natural gas network node carbon potential based on the gas source carbon potential and pipeline carbon flow rate; determining the coupling relationship between the carbon potential and power at the input and output ports of the energy coupling equipment based on the principle of carbon potential transfer conservation; and calculating the energy storage carbon potential, which reflects changes in carbon stock, based on the state of charge.

[0118] In this embodiment of the invention, the step of establishing an upper-level carbon price optimization model based on the purchase and sale transaction data of the integrated energy system and the carbon potential of the load nodes, with the objective function of maximizing system revenue and the constraint of equipment operation balance, includes: determining the system revenue based on the energy sales revenue, carbon emission revenue, energy purchase cost, and carbon trading cost of the integrated energy system; determining the constraint based on the upper and lower limits of energy equipment output and ramp-up limits in the integrated energy system, as well as the system power balance; and establishing a load carbon price model of the upper-level carbon price optimization model based on the carbon potential of the load nodes. In the formula, , These are the carbon prices for electrical load and thermal load, respectively. , The load node carbon potentials are the electrical load and thermal load at time t, respectively.

[0119] In this embodiment of the invention, establishing a lower-level load optimization model based on user usage data on the energy load side and the carbon potential of the load nodes, with the objective function of minimizing user costs and the constraint of demand response balance, includes: determining user costs based on users' energy purchase costs, users' carbon emission costs, users' shared energy storage leasing costs, users' electricity trading revenue, and users' revenue from demand response; determining the constraints based on users' shared energy storage operation constraints, users' power exchange constraints, and load demand response constraints; and establishing a cooperative game model for the lower-level load optimization model based on the minimum user cost. In the formula, This represents the total cost for the nth user. The negotiation breakdown point for the nth user; N LA This represents the total number of users.

[0120] In this embodiment of the invention, solving the upper-level carbon price strategy model and the lower-level load optimization model according to a preset optimization algorithm includes: initializing the energy price and load carbon price determined by the upper-level carbon price strategy model; performing a global exploration of the initialization results based on dynamically changing adaptive inertia weights, and performing a local fine-grained search on the global exploration results to obtain updated energy prices and load carbon prices; decomposing the lower-level load optimization model into sub-problems to obtain a parallel minimum cost model and a cooperative benefit allocation model; and coordinating and optimizing the minimum cost model and the cooperative benefit allocation model based on the decision variables independently optimized by each user to obtain a demand response scheme that meets the requirements of global consistency.

[0121] In this embodiment of the invention, the calculation results are iteratively optimized using game theory to obtain the optimal trading carbon price and user response that meet preset requirements. This includes: updating the user's energy purchase demand based on the updated energy price and load carbon price of the system, and updating the energy price and load carbon price again based on the new energy purchase demand of the user; determining whether the changes in energy price and load carbon price before and after the update are both less than the preset convergence accuracy based on preset convergence conditions; if so, determining that the integrated energy system and the user have reached a game equilibrium, and outputting the optimal trading carbon price and user response in the equilibrium state.

[0122] The carbon energy synergistic response optimization device and the carbon energy synergistic response optimization method of the integrated energy system provided in the above embodiments belong to the same concept. The specific implementation process can be found in the method embodiments, which will not be repeated here.

[0123] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for optimizing the carbon energy synergistic response of an integrated energy system, characterized in that, The method includes: Based on the operational data of the integrated energy system, a system carbon emission flow model is established to calculate the carbon potential of the load nodes of the integrated energy system; wherein, the integrated energy system includes an electric subsystem, a thermal subsystem, a natural gas subsystem, energy coupling equipment, and energy storage equipment; Based on the purchase and sale transaction data of the integrated energy system and the carbon potential of the load nodes, an upper-level carbon price optimization model is established with the objective function of maximizing system revenue and the constraint of equipment operation balance. Based on user usage data on the energy load side and the carbon potential of the load nodes, a lower-level load optimization model is established with the objective function of minimizing user costs and the constraint of demand response balance. The upper-level carbon price strategy model and the lower-level load optimization model are solved according to the preset optimization algorithm, and the calculation results are iteratively optimized through game theory to obtain the optimal trading carbon price and user response that meet the preset requirements.

2. The method as described in claim 1, characterized in that, The step of establishing a system carbon emission flow model for calculating the load node carbon potential of the integrated energy system based on the operating data of the integrated energy system includes: Based on the power flow distribution and power source carbon potential of the power subsystem, the grid node carbon potential used to characterize the sum of carbon flow rates injected into the power lines is calculated. The carbon potential of the heating network nodes is calculated based on the pipeline flow rate of the thermal subsystem and the carbon flow rate of the heating network; the carbon flow rate of the heating network is determined by the specific heat capacity of the medium and the temperature difference. The carbon potential of the natural gas network nodes is calculated based on the carbon potential of the gas source and the carbon flow rate of the pipeline. Based on the principle of carbon potential conservation, the coupling relationship between carbon potential and power at the input and output ports of the energy coupling device is determined. The energy storage carbon potential used to react to changes in carbon stock is calculated based on the state of charge.

3. The method as described in claim 1, characterized in that, The step of establishing an upper-level carbon price optimization model based on the purchase and sale transaction data of the integrated energy system and the carbon potential of the load nodes, with the objective function of maximizing system revenue and the constraint of equipment operation balance, includes: The revenue of the integrated energy system is determined based on the energy sales revenue, carbon emission revenue, energy purchase cost, and carbon trading cost. The constraints are determined based on the upper and lower limits of the output of energy equipment and the ramping limit in the integrated energy system, as well as the system power balance. Based on the carbon potential of the load nodes, establish the load carbon price model of the upper-level carbon price optimization model: In the formula, , These are the carbon prices for electrical load and thermal load, respectively. , The load node carbon potentials are the electrical load and thermal load at time t, respectively.

4. The method as described in claim 1, characterized in that, The process of establishing a lower-level load optimization model based on user usage data on the energy load side and the carbon potential of the load nodes, with the objective function of minimizing user costs and the constraint of demand response balance, includes: User costs are determined based on the user's energy purchase costs, carbon emission costs paid by the user, energy storage leasing costs shared by the user, electricity trading revenue for the user, and revenue from demand response for the user. The constraints are determined based on the user's shared energy storage operation constraints, the power exchange constraints between users, and the load demand response constraints. A cooperative game model for the lower-level load optimization model is established based on the minimum user cost: In the formula, This represents the total cost for the nth user. The negotiation breakdown point for the nth user; N LA This represents the total number of users.

5. The method as described in claim 1, characterized in that, The step of solving the upper-level carbon price strategy model and the lower-level load optimization model according to the preset optimization algorithm includes: Initialize the energy price and load carbon price determined by the upper-level carbon price strategy model; The initialization results are explored globally based on dynamically changing adaptive inertia weights, and a local fine-search is performed on the global exploration results to obtain the updated energy price and load carbon price. The lower-level load optimization model is decomposed into sub-problems to obtain a parallel cost-minimum model and a cooperative benefit allocation model. Based on the independently optimized decision variables of each user, the minimum cost model and the cooperative benefit distribution model are coordinated and optimized to obtain a demand response scheme that meets the requirements of global consistency.

6. The method as described in claim 1, characterized in that, The calculation results are iteratively optimized using game theory to obtain the optimal trading carbon price and user response that meet preset requirements, including: The system updates the user's energy purchase demand based on the updated energy prices and load carbon prices, and then updates the energy prices and load carbon prices again based on the new energy purchase demand from the user. Based on the preset convergence conditions, determine whether the changes in energy prices and load carbon prices before and after the update are both less than the preset convergence accuracy. If so, determine that the integrated energy system and the user end have reached a game equilibrium, and output the optimal trading carbon price and user response in the equilibrium state.

7. A carbon energy synergistic response optimization device for an integrated energy system, characterized in that, The device includes: The first modeling module is used to establish a system carbon emission flow model for calculating the carbon potential of the load nodes of the integrated energy system based on the operating data of the integrated energy system; wherein, the integrated energy system includes an electric subsystem, a thermal subsystem, a natural gas subsystem, energy coupling equipment, and energy storage equipment; The second modeling module is used to establish an upper-level carbon price optimization model based on the purchase and sale transaction data of the integrated energy system and the carbon potential of the load nodes, with the objective function of maximizing system revenue and the constraint of equipment operation balance. The third modeling module is used to establish a lower-level load optimization model based on user usage data on the energy load side and the carbon potential of the load nodes, with the objective function of minimizing user costs and the constraint of demand response balance. The optimization module is used to solve the upper-level carbon price strategy model and the lower-level load optimization model according to the preset optimization algorithm, and to perform iterative game optimization on the calculation results to obtain the optimal trading carbon price and user response that meet the preset requirements.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

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