Virtual power plant and power distribution network collaborative optimization operation model in electricity-carbon coupling market

Abstract

The invention relates to the field of power system dispatching optimization, and discloses a virtual power plant and power distribution network collaborative optimization operation model in an electricity-carbon coupling market, which comprises the following steps of: integrating multi-element energy resources and conversion equipment through a VPP operation framework, and constructing a multi-energy flow collaborative energy supply system for matching of electric heating loads and low-carbon operation; establishing an upper VPP decision-making layer for distributed resource scheduling and matching a market mechanism by taking comprehensive benefit maximization of an electricity market and a carbon trading market as a target; and after the upper VPP decision-making layer is established, on the basis of undertaking the upper VPP decision-making layer, constructing a lower market clearing layer by taking the power distribution network as a core. A double-layer optimization model in the electricity-carbon coupling market is constructed, the lower layer achieves collaborative optimization of electricity-carbon market income and system cost with the purpose of minimizing the total operation cost of the power distribution network, economical efficiency and low-carbon performance are both considered, and support is provided for system efficient operation under high-proportion renewable energy access.

Description

Technical Field

[0001] This invention relates to the field of power system dispatch optimization, specifically to a collaborative optimization operation model of virtual power plants and distribution networks under an electric-carbon coupled market. Background Technology

[0002] Against the backdrop of advancing "dual carbon" goals and the continuous increase in renewable energy penetration, the power system is facing the dual challenges of deep decarbonization and the integration of a high proportion of clean energy. On the one hand, traditional power system dispatching models are difficult to adapt to the intermittent and fluctuating characteristics of renewable energy output, resulting in low efficiency of distributed energy consumption and restricting the overall economic efficiency of the system. On the other hand, the gradual improvement of the carbon trading market is promoting the deep coupling of the electricity market and the carbon market, forming a new form of electricity-carbon coupled market, which places higher demands on the coordinated operation of the main bodies of the power system.

[0003] Virtual power plants (VPPs) serve as important carriers for the aggregated management of new energy units such as wind power and photovoltaics, traditional power sources such as coal and gas, as well as equipment for carbon capture, power-to-gas conversion, and energy storage. They are key entities connecting distributed resources and distribution networks in the electricity-carbon coupling market, and their coordinated and optimized operation with the distribution network is of great significance for improving the system's economy and low-carbon efficiency.

[0004] Some studies focus on single electricity market scenarios, improving power supply reliability and economic benefits solely by optimizing the output of distributed energy resources within virtual power plants. They fail to adequately consider the impact of the carbon market on operational strategies and cannot adapt to the collaborative operation requirements of a coupled electricity-carbon market. Even those studies attempting to construct collaborative models often employ traditional two-layer optimization or iterative solution methods, facing problems such as high computational complexity and slow convergence speed due to high-dimensional nonlinear constraints. Furthermore, the interaction of data among multiple stakeholders can easily lead to privacy leaks, making it difficult to meet the real-time scheduling needs of large-scale systems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a collaborative optimization operation model for virtual power plants and distribution networks in an electricity-carbon coupling market, comprising the following steps: By integrating diverse energy resources and conversion equipment through the VPP operating framework, a multi-energy flow coordinated energy supply system is constructed for matching electric and thermal loads and low-carbon operation. After constructing a multi-energy flow coordinated energy supply system, an upper-level VPP decision-making layer is established. This layer aims to maximize the combined benefits of the electricity market and carbon trading market, and is used for distributed resource scheduling and matching market mechanisms. After establishing the upper-level VPP decision-making layer, a lower-level market clearing layer is constructed based on the distribution network, taking over the decision-making of the upper-level VPP.

[0006] Preferably, the integrated VPP operation framework is connected to the power grid as the main body for basic power supply replenishment and interaction. The low-carbon operation includes operation from the dimensions of energy input, conversion, storage, and load distribution; Among them, when connecting to the power grid, clean electricity is injected in coordination with photovoltaic power and wind power, and coal-fired units are included as base load output to form an energy complementary input pattern.

[0007] Preferably, the coal-fired unit is equipped with a carbon capture system; The carbon capture system includes a carbon reduction and component electrical energy conversion loop; The carbon reduction directly captures CO2 using the carbon capture system supporting the coal-fired unit; The component electrical part uses a CO2 input power-to-gas device to couple and convert excess electricity into natural gas, constructing an electrical energy conversion loop for consuming natural gas, and outputting electricity and heat energy through a combined heat and power generation or pure power generation mode.

[0008] Preferably, the constraint conditions of the decision-making objective function of the upper-layer VPP decision-making layer include electric power balance, thermal power balance, resource operation limits, electric carbon trading rules, and energy storage state constraints.

[0009] Preferably, the lower-layer market clearing layer aims to minimize the total system operation cost for the electricity energy procurement and carbon quota acquisition strategy; Among them, in the lower-layer market clearing layer, the constraint conditions include electric and thermal power balance, equipment operation limits, energy storage state, and carbon trading bid constraints for system operation safety and market compatibility.

[0010] Preferably, taking the maximization of the comprehensive benefit of the upper-layer VPP decision-making layer as the goal, its model is as follows: 9 In the formula, is the comprehensive benefit of VPP market operation; is the total operation time; is the clearing electricity price at time is the winning bid electricity quantity of VPP at time is the carbon trading price at time is the winning bid quantity of carbon trading at time

[0011] Preferably, the VPP decision-making layer further includes a new energy system, and the new energy system includes wind power and photovoltaic power, and its mathematical model is as follows: 1 2 In the formula, For wind power in Efforts made at all times; For optoelectronics Efforts made at all times; These represent the minimum and maximum generating capacity of wind power. The minimum and maximum power output of photovoltaics.

[0012] Preferably, the carbon capture system separates CO2 from flue gas to reduce emissions for energy management and low-carbon emissions, and its mathematical model is as follows: 3 4 In the formula, for Power consumption of the carbon capture system at all times; Carbon-to-electricity ratio; for Real-time carbon capture system Absorption and regeneration rates; For carbon capture efficiency; for Carbon capture system carbon storage device Storage capacity; for Carbon capture system carbon storage device Storage capacity; This represents the maximum power consumption of the carbon capture system. This represents the maximum CO2 regeneration capacity of the carbon capture system. The minimum and maximum storage capacities of the carbon storage device in a carbon capture system.

[0013] Preferably, surplus electrical energy is converted into hydrogen or methane gas fuel for deep coupling between the power system and the natural gas system. The mathematical model is as follows: 5 In the formula, for The gas output of the electro-gas converter at any given time; For conversion efficiency; for The power consumption of the electro-pneumatic converter at any given time; This represents the maximum power consumption of the electro-gas conversion device.

[0014] Preferably, when constructing the upper VPP decision layer, the power balance constraints are as follows: 10 In the formula, for Total electrical load of the system at any given time; For wind power in Power at any given moment; For optoelectronics Power at any given moment; for Power consumption of the carbon capture system at all times; for The power consumption of the electro-pneumatic converter at any given time; for Net power of the energy storage device at any time; for The electrical power output of the coal-fired power unit at all times; for The electrical power output of the gas turbine unit at all times.

[0015] It has the following beneficial effects: 1. This invention constructs a two-layer optimization model under the electric carbon coupling market. The upper layer aims to maximize the comprehensive benefits of VPP electric carbon, while the lower layer aims to minimize the total operating cost of the distribution network. This achieves synergistic optimization of electric carbon market revenue and system cost, taking into account both economic efficiency and low carbon emissions, and provides support for the efficient operation of the system under a high proportion of renewable energy access.

[0016] 2. This invention transforms a high-dimensional nonlinear collaborative optimization problem into an equivalent low-dimensional projection model based on the upper mirror graph theory. It achieves a non-iterative solution through an asymptotic vertex enumeration method, significantly reducing computational complexity and solving the problems of slow convergence and high computational burden associated with traditional iterative methods. Furthermore, the solution process requires only one information exchange between the VPP and the distribution network, without exposing the internal equipment parameters and scheduling strategies of each entity, effectively protecting the privacy and security of both the VPP and the distribution network operator.

[0017] 3. This invention, through the constructed VPP multi-energy flow collaborative operation framework, can flexibly integrate diverse distributed energy sources such as wind power, photovoltaics, carbon capture, and electricity-to-gas conversion, adapting to the access requirements of different types of distributed resources; at the same time, the equivalent projection modeling and non-iterative solution mechanism can be easily extended to multi-VPP and distribution network collaborative scenarios without significant adjustments to the model structure, providing a scalable technical solution for system collaborative optimization in the large-scale electricity-carbon coupling market. Attached Figure Description

[0018] Figure 1 This is a diagram of the VPP operating framework of the present invention; Figure 2 This is a schematic diagram of the IEEE 33-node network topology of the present invention; Figure 3 This is a schematic diagram illustrating the VPP load and day-ahead forecast of wind and solar power output according to the present invention; Figure 4 This is a schematic diagram illustrating the electricity price and carbon quota price prediction results of the present invention; Figure 5Schematic diagram of the electric power dispatch result for Scenario 1 of the present invention; Figure 6 Schematic diagram of the electric power dispatch result for Scenario 2 of the present invention; Figure 7 Schematic diagram of the heat power dispatch result for Scenario 2 of the present invention; Figure 8 Schematic diagram of the equivalent projection slice of the feasible region for the typical time period of electric power in Scenario 3 of the present invention; Figure 9 Schematic diagram of the equivalent projection slice of the feasible region for the typical time period of heat power in Scenario 3 of the present invention; Figure 10 Schematic diagram of the electric power dispatch result for Scenario 3 of the present invention; Figure 11 Schematic diagram of the heat power dispatch result for Scenario 3 of the present invention. Detailed implementation manners

[0019] Next, in combination with the accompanying drawings of the present invention, the technical solutions of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Embodiment

[0020] Please refer to the attached Figure 1 - attached Figure 11 , the embodiment of the present invention provides a collaborative optimization operation model for a virtual power plant and a distribution network under an electric-carbon coupling market, including the following steps: Integrate multiple energy resources and conversion devices through the VPP operation framework to construct an energy supply system with coordinated multi-energy flows for the matching and low-carbon operation of electric and heat loads; The low-carbon operation is carried out from the dimensions of energy input, conversion, storage, and load distribution. Connect to the power grid as the basic electric energy supplement and interaction entity, coordinate the injection of clean electric energy from photovoltaic and wind power, and incorporate coal-fired units to ensure stable baseload output, so as to form an energy complementary input pattern.

[0021] After the integration of the VPP operation framework, connect to the power grid as the basic electric energy supplement and interaction entity; The low-carbon operation includes operation from the dimensions of energy input, conversion, storage, and load distribution; Among them, when connecting to the power grid, coordinate the injection of clean electric energy from photovoltaic and wind power, and incorporate coal-fired units as baseload output to form an energy complementary input pattern.

[0022] The carbon capture system includes carbon reduction and the construction of an electrical energy conversion link; Carbon reduction is directly captured by a carbon capture system supporting a coal-fired unit; The electrical components utilize an input power-to-gas conversion device to couple excess electrical energy into natural gas, constructing an electrical energy conversion link to utilize the natural gas. The resulting energy is then output as electricity and heat via combined heat and power (CHP) or pure power generation. Specifically, the new energy system includes wind power and solar power, whose output has significant intermittent and fluctuating characteristics. Its mathematical model is as follows: (1) (2) In the formula, For wind power in Efforts made at all times; For optoelectronics Efforts made at all times; These represent the minimum and maximum generating capacity of wind power. The minimum and maximum power output of photovoltaics.

[0023] Carbon capture systems are designed to efficiently separate carbon from flue gas. Reducing emissions is key to achieving efficient energy management and low carbon emissions, and its mathematical model is as follows: (3) (4) In the formula, for Power consumption of the carbon capture system at all times; Carbon-to-electricity ratio; for Real-time carbon capture system Absorption and regeneration rates; For carbon capture efficiency; for Carbon capture system carbon storage device Storage capacity; for Carbon capture system carbon storage device Storage capacity; This represents the maximum power consumption of the carbon capture system. This represents the maximum CO2 regeneration capacity of the carbon capture system. The minimum and maximum storage capacities of the carbon storage device in a carbon capture system.

[0024] Electricity-to-gas (EPG) technology, as an energy conversion and storage method, converts surplus electrical energy into gaseous fuels such as hydrogen or methane for deep coupling of power and natural gas systems. Its mathematical model is as follows: (5) In the formula, for The gas output of the electro-gas converter at any given time; For conversion efficiency; for The power consumption of the electro-pneumatic converter at any given time; This represents the maximum power consumption of the electro-gas conversion device.

[0025] Specifically, energy storage devices, as key equipment for balancing power system supply and demand and smoothing out fluctuations in new energy sources, involve the storage, conversion, and release of energy during their charging and discharging processes. Their mathematical model is as follows: (6) In the formula, for Capacity of real-time energy storage device; for Capacity of real-time energy storage device; These represent charging efficiency and discharging efficiency, respectively. They represent The charging and discharging power of the energy storage device at any time; Unit of time; These represent the minimum and maximum capacities of the energy storage device.

[0026] Carbon quota models need to comprehensively consider the carbon emission characteristics of different energy facilities. Coal-fired units are key targets for control due to the large amount of carbon dioxide released from coal combustion. Gas-fired units have lower carbon emission intensity than coal-fired units but still need to be included in the measurement scope. As a component of distributed energy systems, the carbon emissions of gas-fired boilers cannot be ignored. The carbon quota model in this paper is as follows: (7) In the formula, The total carbon quota for the system; Total running time; for The coal-fired power unit outputs electrical and thermal power at all times; for The constant output heat power of the gas-fired boiler; for The gas turbine unit outputs electrical and thermal power at all times; Carbon quota allocation coefficients for the output electrical power of coal-fired units, the output thermal power of coal-fired units, the output thermal power of gas-fired boilers, the output electrical power of gas-fired units, and the output electrical power of gas-fired units.

[0027] Similar to the carbon quota model, which also includes coal-fired units, gas-fired units, and gas-fired boilers, the mathematical model for system carbon emissions is as follows: (8) In the formula, This represents the system's actual total carbon emissions. Total running time; for The coal-fired power unit outputs electrical and thermal power at all times; for The constant output heat power of the gas-fired boiler; for The gas turbine unit outputs electrical and thermal power at all times; The actual carbon emission coefficients for the output electrical power of coal-fired units, the output thermal power of coal-fired units, the output thermal power of gas-fired boilers, the output electrical power of gas-fired units, and the output electrical power of gas-fired units.

[0028] After constructing a multi-energy flow coordinated energy supply system, an upper-level VPP decision-making layer is established. This layer aims to maximize the combined benefits of the electricity market and carbon trading market, and is used for distributed resource scheduling and matching market mechanisms. Specifically, the decision objective function constraints of the upper-level VPP decision-making layer cover power balance, thermal power balance, resource operation limits, electricity carbon trading rules, and energy storage state constraints.

[0029] The model for establishing the overall benefit maximization of the upper-level VPP decision-making layer is as follows: (9) In the formula, For the overall benefits of VPP market operations; Total running time; for Electricity prices are cleared out at all times; for The amount of electricity won by VPP at any given time; for Carbon trading price at any time; for The amount of carbon trading contracts won at any given time.

[0030] When constructing the upper-level VPP decision layer, the power balance constraints are as follows: (10) In the formula, for Total electrical load of the system at any given time; For wind power in Power at any given moment; For optoelectronics Power at any given moment; for Power consumption of the carbon capture system at all times; for The power consumption of the electro-pneumatic converter at any given time; for Net power of the energy storage device at any time; for The electrical power output of the coal-fired power unit at all times; for The electrical power output of the gas turbine unit at all times.

[0031] Specifically, the thermal power balance constraints are as follows: (11) In the formula, for Total system heat load at any given time; for The thermal power output of the coal-fired unit at all times; for The constant output heat power of the gas-fired boiler; for The output thermal power of the gas turbine unit at all times; The constraints for electricity market bidding are as follows: (12) In the formula, for Total electrical load of the system at any given time; for The amount of electricity won by VPP at any given time; The carbon market bidding constraints are as follows: (13) In the formula, Total running time; for The amount of carbon trading contracts won at any given time; This represents the system's actual total carbon emissions. The total carbon quota for the system; The carbon trading period decomposition factor is defined by the decision objective function constraints of the upper VPP decision-making layer, which cover equations (1) to (8).

[0032] After establishing the upper-level VPP decision-making layer, a lower-level market clearing layer is constructed based on the distribution network, taking over the decision-making of the upper-level VPP.

[0033] The lower market clearing layer aims to minimize the total operating cost of the system and is used to optimize the strategies for electricity procurement and carbon allowance acquisition. In the lower market clearing layer, constraints include those covering power balance, equipment operating limits, energy storage status, and carbon trading bidding constraints, which are used to ensure system operation safety and market compliance, forming a closed-loop optimization framework.

[0034] Specifically, the objective function is to minimize the total operating cost of the system, and its model is as follows: (14) In the formula, Total operating costs for the VPP market; Cost of wind turbine units; Cost of the optoelectronic unit; For the operating costs of the carbon capture system; For the operating cost of the power-to-gas generator unit; For the operating cost of energy storage devices; Operating costs of coal-fired power units; Operating costs of gas-fired boilers; For the operating cost of the gas turbine unit; for Carbon trading price at any time; This represents the total carbon quota for the system.

[0035] The power generation costs of new energy units are as follows: (15) (16) In the formula, For wind power in Power at any given moment; For optoelectronics Power at any given moment; This represents the operating cost coefficient for wind power and solar power units.

[0036] The operating costs of a carbon capture system are as follows: (17) In the formula, For the operating costs of the carbon capture system; Its operating cost coefficient; for Power consumption of the carbon capture system at all times.

[0037] The operating costs of the power-to-gas generator unit are as follows: (18) In the formula, For the operating cost of the power-to-gas generator unit; Its operating cost coefficient; for The power consumption of the electro-gas conversion device at all times.

[0038] The operating costs of energy storage devices are as follows: (19) In the formula, For the operating cost of energy storage devices; Its operating cost coefficient; They represent The charging and discharging power of the energy storage device at all times.

[0039] The operating costs of coal-fired power units are as follows: (20) In the formula, Operating costs of coal-fired power units; Its operating cost coefficient; for The coal-fired power unit outputs electrical and thermal power at all times.

[0040] The operating costs of a gas-fired boiler are as follows: (twenty one) In the formula, Operating costs of gas-fired boilers; Its operating cost coefficient; for Real-time thermal power of gas-fired boiler.

[0041] The operating costs of the gas turbine unit are as follows: (twenty two) In the formula, For the operating cost of the gas turbine unit; Its operating cost coefficient; for The gas turbine unit outputs electrical and thermal power at all times. In the lower market clearing layer, the constraints include equations (1)-(8), (10)-(13), and (15)-(22).

[0042] A non-iterative distributed solution is achieved based on the upper view theory. The total operating cost function of VPP is shown in equation (14). Equation (14) can be transformed using the upper mirror diagram theory: (twenty three) In the formula, Total operating cost of VPP; Cost of wind turbine units; Cost of the optoelectronic unit; For the operating costs of the carbon capture system; For the operating cost of the power-to-gas generator unit; For the operating cost of energy storage devices; Operating costs of coal-fired power units; Operating costs of gas-fired boilers; For the operating cost of the gas turbine unit; For carbon trading costs; This is a set of variables for each unit within a VPP, including electrical power, thermal power, and carbon emissions.

[0043] At the same time, auxiliary variables are introduced. This represents the upper limit of the total cost of the VPP, transforming the cost minimization objective into a constraint: (twenty four) In the formula, Total operating cost of VPP; It is an auxiliary variable.

[0044] At the same time, in order to avoid The value is unbounded, so a reasonable upper limit needs to be set. The estimate is based on the equipment's maximum output and the highest market price: (25) (26) In the formula, As an auxiliary variable; The upper limit of the auxiliary variable; for Maximum energy cost at any given moment; for The maximum cost of carbon trading at any given time.

[0045] Thus, the reconstructed objective function is obtained—minimizing the auxiliary ratio variable. The cost function is included in the constraints: (27) In the formula, As an auxiliary variable; Total operating cost of VPP; This is the upper limit of the auxiliary variable.

[0046] The energy-carbon coupling constraints of VPPs encompass two main categories: energy technology constraints and carbon trading constraints, including: electrical power balance constraints, thermal power balance constraints, equipment operation limits constraints, and carbon emission and carbon quota constraints. Integrating these constraints with the cost constraints derived from the above diagram reveals the complete feasible domain of VPPs. Defined as: (28) In the formula, For a complete feasible domain; Internal variables, including the output of each unit; carbon trading For coordination variables; cost As an auxiliary variable; This is the upper limit of the cost. For a moment .

[0047] Equivalent projection model of VPP Defined as a high-dimensional feasible region In the coordinate variable space The projection on, that is: (29) In the formula, For equivalent projection model; This is the feasible region; For internal variables; As a coordinating variable; It is an auxiliary variable.

[0048] For any There exists at least one set of internal variables. This ensures that VPPs, while meeting power balance, equipment limits, and carbon emission constraints, operate within a limit not exceeding [the specified limits]. Cost of achieving carbon trading volume .

[0049] Due to The constraints are all linear equality / inequality, and their projections For a convex polyhedron, it can be represented by a hyperplane as follows: (30) In the formula, For equivalent projection model; The coefficient matrix, To constrain the quantity; For internal variables; As a coordinating variable; As an auxiliary variable; A constant vector; each For each face of a convex polyhedron, define the feasible range of the coordination variables.

[0050] Because of internal variables With high dimensionality, it can be directly derived using elimination. The constraints will generate a large number of redundant inequalities, so the asymptotic vertex enumeration method is used to calculate the projection. The projection model is constructed by identifying the vertices of the convex polyhedron. The steps are as follows: Step 1: Initial Vertex Identification Along the coordination variables Solve the extremum problem along each axis to obtain the initial vertex: Maximize , minimize , Maximize / Minimize and Similarly, the initial set of vertices is denoted as... ,in , For the initial vertex set and the first vertex in the set... One vertex.

[0051] Step 2: Convex Hull Construction and Iterative Optimization Construct the convex hull based on the current vertex set. Constructing a convex polyhedron And represented as a hyperplane: .

[0052] Search for new vertices, along Solving the extremum problem by finding the normal vector directions of each hyperplane, and searching for the location located at... But not in New vertices within the VPP. "Iterative optimization" refers to the internal iterations during the VPP's local computation of the equivalent projection model, i.e., computing each vertex.

[0053] Step 3 Termination Condition: Hausdorff Distance Control: The current convex hull is measured using the Hausdorff distance. Compared to real projection Error: (31) when When the time comes, the algorithm terminates and outputs... As Approximate; For internal use The error of the next iteration; The preset error threshold is used; These represent two points in the vertex set.

[0054] Based on the above, and in conjunction with the accompanying drawings, the following explanation is provided: Simulation studies were conducted based on the IEEE 33-node distribution network, and a VPP (Variable Power Provider) participating in the electricity carbon market coupling was centrally deployed at node 18. Figure 2 As shown, the VPP includes a 4MW wind power and a 3MW photovoltaic new energy unit, a 2MW coal-fired unit, a 1.2MW carbon capture system (capture efficiency 85%), a 1MW gas-fired unit, and is also equipped with a 0.8MW power-to-gas conversion device (conversion efficiency 65%), a 1MW energy storage device (charge and discharge efficiency 92%), and a 1.2MW gas-fired boiler. The system is designed with a peak electrical load of 9.5MW and a thermal load of 3.5MW. The spot price of electricity fluctuates between 0.3 and 0.8 yuan / kWh, and the carbon quota price ranges from 200 to 450 yuan / t. To compare and analyze the performance differences between different market participation models and solution methods, three simulation scenarios were set up: Scenario 1: As a baseline scenario, the VPP only participates in the electricity market and does not consider carbon trading constraints. It minimizes its own operating costs by optimizing the output of internal energy equipment. This scenario is used to verify the impact of carbon market participation on the VPP's operating strategy and economics. Scenario 2: As a control scenario, VPP participates in both the electricity market and the carbon trading market. It is necessary to optimize the electricity-carbon coordinated operation strategy while meeting carbon quota constraints. The method uses KKT conditional transformation to solve the problem. Scenario 3: A scenario to verify the proposed method. The VPP also participates in the electricity market and carbon trading market. The solution is obtained using an optimization method based on the upper mirror diagram theory.

[0055] Figure 3 The data shows the day-ahead forecasts for VPP load and wind and solar power output. The 4MW wind turbine's output is affected by diurnal wind speed variations, with output ranging from 0.8-1.5MW between 2:00 AM and 6:00 AM, peaking at 2.5-3.8MW between 2:00 PM and 6:00 PM, and remaining stable at 1.2-2.2MW during other times. The 3MW solar turbine's output strictly follows changes in solar irradiance, with effective power generation from 7:00 AM to 5:00 PM, output maintained at 1.8-2.8MW from 9:00 AM to 3:00 PM, reaching its maximum output of 2.5-2.8MW between 12:00 PM and 2:00 PM, and zero output during the night and early morning hours. Peak electricity load occurs during the peak residential electricity consumption period from 19:00 to 21:00, when electricity demand is concentrated and load fluctuations are relatively small. Secondary peaks occur from 8:00 to 10:00 and from 12:00 to 14:00, with electricity loads of 7.2-8.0MW and 6.8-7.5MW respectively. The electricity load drops to its lowest point of 3.5-4.2MW from 3:00 to 6:00 in the morning. As for heat load, peak heating demand occurs from 6:00 to 8:00 in the morning and from 20:00 to 22:00 in the evening, with corresponding loads of 2.8-3.5MW. The heat load remains at 1.5-2.2MW from 10:00 to 18:00 during the day, and drops to 1.0-1.5MW in the early morning.

[0056] Figure 4 The daily forecast results for spot electricity prices and carbon allowance prices are presented. The price difference between peak and off-peak hours is significant, with a maximum difference of 0.5 yuan / kWh. This fluctuation characteristic is consistent with the basic principle of "demand-driven pricing" in the electricity market and provides price incentives for VPPs to formulate peak-valley arbitrage strategies. The price during peak heat load hours is 250 yuan / ton higher than during the early morning off-peak hours, reflecting the cost constraint effect of the carbon market on high-emission behavior and also creating space for VPPs to optimize the operation of carbon capture systems.

[0057] Figure 5 The results of power dispatch in Scenario 1 are shown. When the VPP only participates in the electricity market, wind power is the main source of power output, contributing continuously during multiple periods; coal-fired units and gas-fired units are flexibly adjusted to ensure stable power supply; auxiliary systems such as carbon capture and power-to-gas conversion work together to optimize energy utilization; energy storage is charged and discharged during certain periods to play a role in peak shaving and valley filling. All aspects work together to meet electricity demand.

[0058] Figure 6The results of power dispatch in Scenario 2 are shown. Wind and solar power outputs power during certain periods, coal-fired power output is relatively stable, energy storage has both positive and negative power outputs, and power fluctuations in equipment such as power-to-gas conversion are small. Overall, this reflects the synergy of multiple energy sources, with wind and solar power generation, energy storage regulation, and traditional coal-fired power units working together to achieve dynamic power balance and supply.

[0059] Figure 7 The results of thermal power scheduling in Scenario 2 are presented. Coal-fired units account for a large proportion of thermal power during most periods, while gas-fired boilers show significant power output at times such as 6:00 AM and 8:00 PM, and the power output of gas-fired units remains relatively stable. Overall, this demonstrates the time-specific differences in thermal power supply among different energy equipment, with coal-fired units serving as the primary source of thermal power and gas-fired boilers supplementing it during specific periods.

[0060] Table 1 Comparison of VPP operational benefits in basic scenarios Scene Electricity market benefits / yuan Carbon market benefits / yuan Total operating cost / yuan Total VPP Benefits / Yuan Carbon emissions / t 1 44726.12 0 7037.55 37688.57 18.915 2 46680.63 5388.83 8707.62 43361.84 18.552 Table 1 compares the operational benefits of VPPs under the basic scenarios. Scenario 1 serves as the baseline scenario, where the VPP only participates in the electricity market, without considering carbon trading constraints, and minimizes its operating costs by optimizing the output of its internal energy equipment. In this scenario, the electricity market benefit is 44,726.12 yuan, the total operating cost is 7,037.55 yuan, the total VPP benefit is 37,688.57 yuan, and the carbon emissions are 18.915 tons.

[0061] Comparing the two scenarios, the total operating cost of Scenario 2 is 8707.62 yuan, an increase of 23.73% compared to Scenario 1. This cost increase is likely due to adjustments made to the scheduling and operation of energy equipment to meet carbon quota constraints, resulting in greater investment in electricity-carbon synergy. The total VPP benefit reaches 43361.84 yuan, an increase of 15.05% compared to Scenario 1. This indicates that despite the increase in total operating costs, the increased benefits from the electricity market and carbon market significantly improve overall efficiency. Simultaneously, carbon emissions are reduced to 18.552 tons, a decrease of 1.92% compared to Scenario 1. This demonstrates that participation in the carbon trading market encourages VPPs to optimize energy use and reduce carbon emissions, achieving better results in both economic and environmental benefits.

[0062] Figure 8 The equivalent projection slice of the feasible region for typical power output during Scenario 3 is shown. During the peak daytime electricity consumption period of 9:00–13:00, power output remains at 4–7MW, and electricity revenue rapidly climbs to a high level of nearly 5,000 yuan. This indicates that significant market benefits can be obtained by increasing power output during this period, reflecting the strong positive correlation between electricity market prices and output levels. It also demonstrates the potential for VPPs to obtain economic returns in the electricity market by optimizing output.

[0063] Figure 9The equivalent projection slice of the feasible region during typical periods of thermal power in Scenario 3 is shown. From 13:00 to 21:00, as the thermal power gradually increases from 1MW to 3MW, the carbon revenue correspondingly increases from approximately 200 yuan to approximately 400-600 yuan. Especially during the period of higher thermal power (17:00-21:00), the carbon revenue stabilizes at around 400 yuan. This demonstrates that during periods of high heating demand, reasonable control of thermal power output not only meets the heat load demand but also effectively generates carbon market revenue, showcasing the synergistic effect of low-carbon technologies such as electricity-to-gas conversion and carbon capture in the heat supply process, promoting both carbon emission reduction and economic benefits.

[0064] Figure 10 The results of power dispatch in Scenario 3 were presented. Electricity and photovoltaic outputs are intermittent, energy storage has a significant regulating effect in some periods, carbon capture and power-to-gas equipment account for a relatively small proportion of output, while coal-fired and gas-fired units play an important role in power support. Overall, this reflects the synergy and complementarity of multiple energy sources at the power level.

[0065] Figure 11 The results of thermal power scheduling in Scenario 3 are presented. The output of the three types of equipment varies significantly at different times. Around 6:00, the total output is close to 3MW. The output of coal-fired units is relatively stable at certain times, while the output of gas-fired units fluctuates regularly over time, reflecting the dynamic adjustment of the three in thermal power supply according to demand and operating strategies.

[0066] Table 2 Comparison of VPP operational benefits in different scenarios Scene Electricity market benefits / yuan Carbon market benefits / yuan Total operating cost / yuan Total VPP Benefits / Yuan Carbon emissions / t 2 46680.63 5388.83 8707.62 43361.84 18.552 3 46901.68 5324.02 7049.16 45176.54 18.445 As can be seen from the comparison of VPP operating benefits between Scenario 2 and Scenario 3 using the proposed method in Table 2, Scenario 3, which uses the mirror image optimization method, demonstrates superior overall performance across all key indicators compared to Scenario 2, which only employs the conventional KKT method. Electricity market benefits slightly increase from RMB 46,680.63 to RMB 46,901.68; although the increase is limited, it indicates that the optimization method helps to further tap the revenue potential of the electricity market. Carbon market benefits decrease slightly, from RMB 5,388.83 to RMB 5,324.02. Most significantly, the total operating cost of Scenario 3 is greatly reduced, from RMB 8,707.62 to RMB 7,049.16, a reduction of approximately 19.05%, directly driving a significant increase in total VPP benefits, from RMB 43,361.84 to RMB 45,176.54, an increase of 4.18%. This fully verifies the effectiveness of the proposed method in reducing system operating costs and improving overall economic efficiency. Meanwhile, carbon emissions in Scenario 3 were also slightly optimized, decreasing from 18.552 tons to 18.445 tons. Although the change in value is not significant, the continuous improvement in low-carbon benefits has important cumulative effects and environmental significance in the context of high-proportion renewable energy grid connection and multi-energy coordinated dispatch.

[0067] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A collaborative optimization operation model for virtual power plants and distribution networks under an electricity-carbon coupling market, characterized in that, It includes the following steps: Integrate multiple energy resources and conversion devices through the VPP operation framework to build an energy supply system with coordinated multi-energy flows for the matching and low-carbon operation of electric and thermal loads; After building the energy supply system with coordinated multi-energy flows, establish the upper-layer VPP decision-making layer, aiming at maximizing the comprehensive benefits of the electricity market and the carbon trading market, for distributed resource scheduling and matching market mechanisms; After establishing the upper-layer VPP decision-making layer, on the basis of承接上层VPP决策层(这里原文可能有误,假设是“承接上层VPP决策层的决策结果”之类意思,可根据正确原文修改), build the lower-layer market clearing layer with the distribution network as the core.

2. The collaborative optimization operation model of virtual power plants and distribution networks under the electric carbon coupling market as described in claim 1, characterized in that: After the integration of the VPP operation framework, it is connected to the power grid as the basic electric energy supplement and interaction entity; The low-carbon operation includes operation from the dimensions of energy input, conversion, storage and load distribution; Among them, when connecting to the power grid, clean electric energy is injected in coordination with photovoltaic and wind power, and coal-fired units are included as the base load output to form an energy complementary input pattern.

3. The collaborative optimization operation model of virtual power plants and distribution networks under an electric carbon coupling market as described in claim 2, characterized in that: The coal-fired units are equipped with a carbon capture system; The coal-fired units are equipped with a carbon capture system; The carbon capture system includes carbon reduction and the construction of an electrical energy conversion link; The carbon reduction uses the carbon capture system equipped with coal-fired units to directly capture CO2; 4. The collaborative optimization operation model of virtual power plants and distribution networks under an electric carbon coupling market as described in claim 1, characterized in that: The construction of electricity uses the CO2 input to the power-to-gas device to be coupled with the excess electric energy and converted into natural gas, and constructs an electrical energy conversion link for consuming natural gas and outputting electricity and heat energy through the combined heat and power or pure power generation mode.

5. The collaborative optimization operation model of virtual power plants and distribution networks under an electric carbon coupling market as described in claim 1, characterized in that: The decision objective function constraint conditions of the upper-layer VPP decision-making layer include electric power balance, thermal power balance, resource operation limits, electricity-carbon trading rules and energy storage state constraints. The lower-layer market clearing layer aims at minimizing the total system operation cost for the electricity energy procurement and carbon quota acquisition strategy; 6. The collaborative optimization operation model of virtual power plants and distribution networks under an electric carbon coupling market as described in claim 1, characterized in that: Among them, in the lower-layer market clearing layer, the constraint conditions include electric and thermal power balance, equipment operation limits, energy storage state and carbon trading bid constraints for system operation safety and market compatibility. (9) In the formula, For the overall benefits of VPP market operations; Total running time; for Electricity prices are cleared out at all times; for The amount of electricity won by VPP at any given time; for Carbon trading price at any time; for The amount of carbon trading contracts won at any given time.

7. The collaborative optimization operation model of virtual power plants and distribution networks under an electric carbon coupling market as described in claim 1, characterized in that: Aiming at maximizing the comprehensive benefits of establishing the upper-layer VPP decision-making layer, its model is as follows: (1) (2) In the formula, For wind power in Efforts made at all times; For optoelectronics Efforts made at all times; These represent the minimum and maximum generating capacity of wind power. The minimum and maximum power output of photovoltaics.

8. The collaborative optimization operation model of virtual power plants and distribution networks under an electric carbon coupling market as described in claim 3, characterized in that: The VPP decision-making layer also includes a new energy system, and the new energy system includes wind power and photovoltaic power, and its mathematical model is as follows: (3) (4) In the formula, for Power consumption of the carbon capture system at all times; Carbon-to-electricity ratio; for Real-time carbon capture system Absorption and regeneration rates; For carbon capture efficiency; for Carbon capture system carbon storage device Storage capacity; for Carbon capture system carbon storage device Storage capacity; This represents the maximum power consumption of the carbon capture system. This represents the maximum CO2 regeneration capacity of the carbon capture system. The minimum and maximum storage capacities of the carbon storage device in a carbon capture system.

9. The collaborative optimization operation model of virtual power plants and distribution networks under an electric carbon coupling market as described in claim 3, characterized in that: The carbon capture system separates CO2 in the flue gas to reduce emissions for energy management and low-carbon emissions, and its mathematical model is as follows: (5) In the formula, for The gas output of the electro-gas converter at any given time; For conversion efficiency; for The power consumption of the electro-pneumatic converter at any given time; This represents the maximum power consumption of the electro-gas conversion device.

10. The collaborative optimization operation model of virtual power plants and distribution networks under an electric carbon coupling market as described in claim 1, characterized in that: By converting the surplus electric energy into hydrogen or methane gas fuel for the deep coupling of the power system and the natural gas system, its mathematical model is as follows: When constructing the upper-layer VPP decision-making layer, the electric power balance constraint is as follows: (10) In the formula, for Total electrical load of the system at any given time; For wind power in Power at any given moment; For optoelectronics Power at any given moment; for Power consumption of the carbon capture system at all times; for The power consumption of the electro-pneumatic converter at any given time; for Net power of the energy storage device at any time; for The electrical power output of the coal-fired power unit at all times; for The electrical power output of the gas turbine unit at all times.

Images