A virtual power plant low-carbon operation method based on dynamic carbon potential feedback and price self-adaptive correction

Abstract

This invention proposes a low-carbon operation method for virtual power plants based on dynamic carbon potential feedback and adaptive price correction. It is applicable to scenarios where the dynamic carbon emission factor fluctuates relative to the system's average carbon emission factor from the previous day, enabling low-carbon scheduling of virtual power plants containing distributed photovoltaic, wind power, energy storage, and flexible loads. First, based on carbon flow tracking theory, a model of the dynamic carbon emission factor and the real-time initial carbon emission intensity of each node is established. Second, a mathematical model for the low-carbon operation of virtual power plants based on dynamic carbon potential feedback and adaptive price correction is constructed. Then, for the problem of carbon-electricity synergy in virtual power plants after the integration of distributed energy, resource contribution weights are used for solution, effectively improving the local consumption rate of distributed energy. Finally, simulations verify the effectiveness of the proposed method. The results show that the low-carbon operation method for virtual power plants based on dynamic carbon potential feedback and adaptive price correction can significantly improve the system's adaptability to energy fluctuations, achieving a win-win situation of collaborative emission reduction between virtual power plants and users.

Description

Technical Field

[0001] This invention belongs to the field of virtual power plant operation optimization and carbon trading technology, specifically involving a low-carbon operation method for virtual power plants based on dynamic carbon potential feedback and adaptive price correction. Background Technology

[0002] Against the backdrop of the steady advancement of "dual carbon" goals and the new power system, virtual power plants, with their powerful resource aggregation capabilities, have become an important technical means for absorbing new energy sources such as photovoltaics and wind power, and for energy storage to participate in the grid in coordination with loads. As the carbon market matures, the dispatching strategies of virtual power plants no longer solely aim to minimize operating costs, but rather pursue low-carbon and economic benefits. However, in actual operation, existing dispatching methods face the problem of signal mismatch.

[0003] Most current time-of-use electricity prices are set based on the supply and demand balance of the power grid. Prices tend to rise during peak electricity consumption periods. However, if this coincides with the peak of renewable energy generation, the actual carbon emission intensity of the system will be low. This will result in a price-carbon price discrepancy, causing users to reduce their electricity consumption when carbon intensity is low and green energy is high due to high electricity prices. This severely inhibits the utilization of renewable energy and causes economic benefits to deviate from environmental goals.

[0004] Currently, there is a lack of unified standards and feedback mechanisms for carbon emission reduction accounting in virtual power plants, making it difficult to clarify the allocation of carbon credits and their economic incentive effects. The inability to accurately quantify carbon contributions reduces the participation of entities in low-carbon responses and makes it difficult for virtual power plants to mitigate market risk fluctuations through refined carbon management.

[0005] Therefore, a low-carbon strategy is needed that can adapt to changes in electricity carbon prices and guide users to adjust their electricity consumption behavior to achieve a reasonable allocation of resources, in order to solve the carbon contribution accounting problem and ensure the low-carbon and efficient operation of virtual power plants. Summary of the Invention

[0006] The core objective of this invention is to provide a low-carbon operation method for virtual power plants based on dynamic carbon potential feedback and adaptive price correction. By constructing an optimization scheduling model with the optimal virtual power plant operating revenue, total system carbon emissions, and user satisfaction as objective functions, this method addresses the electricity-carbon synergy problem of virtual power plants containing distributed photovoltaic, wind power, energy storage, and flexible loads after the integration of distributed energy sources. It achieves electricity-carbon coupled scheduling of virtual power plants, ensuring the synergy between economic benefits and carbon emission reduction targets.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] The present invention discloses a low-carbon operation method for a virtual power plant based on dynamic carbon potential feedback and adaptive price correction, comprising the following steps:

[0009] Step 1: Analyze the characteristics of carbon emission flows from the virtual power plant and establish a mathematical model of dynamic carbon emission factors based on distributed energy and load;

[0010] Step 2: Analyze the characteristics of distributed energy, energy storage and load within the virtual power plant, and establish a real-time initial carbon emission intensity model for each node based on carbon flow tracing theory.

[0011] Step 3: Based on the original dynamic carbon emission factor, a resource contribution weight is introduced, and the virtual carbon emission intensity of each node is adjusted in real time according to the overall carbon reduction benefit of the virtual power plant. When the dynamic carbon emission factor deviates from the average carbon emission factor of the previous day, the system automatically generates a carbon compensation price based on the direction of deviation.

[0012] Step 4: Based on users' sensitivity to changes in carbon emission intensity, construct a comprehensive utility function that includes the cost of insufficient electricity consumption and the sense of gain from carbon emission reduction.

[0013] Step 5: With the objectives of maximizing virtual power plant revenue, minimizing total system carbon emissions, and maximizing overall user satisfaction, a multi-objective optimization scheduling model based on power balance, energy storage state of charge, and distribution network security constraints is constructed.

[0014] Step 6: Solve the objective function that optimizes the revenue of the virtual power plant, the total carbon emissions of the system, and the overall user satisfaction to achieve low-carbon operation of the virtual power plant.

[0015] In step 1, considering the characteristics of carbon emission flows from virtual power plants, a mathematical model of dynamic carbon emission factors for virtual power plants is established, and the virtual power plant is defined. Instantaneous total carbon emissions at any given moment for:

[0016] (1)

[0017] In the formula, Is Time of the first Output power of distributed energy sources (photovoltaics, wind power, and gas turbines); Is Time of the first Carbon emission intensity of distributed energy sources; It refers to the quantity of distributed energy resources; It is a collection of distributed energy resources; It is the first in the distributed energy collection One distributed energy source; Is Purchase power from the main grid at all times; Is The carbon emission intensity of electricity purchased from the main grid at all times.

[0018] Defining virtual power plants in The ratio of total carbon emissions to total net power output at any given time is the dynamic carbon emission factor. :

[0019] (2)

[0020] In the formula, Is The instantaneous total carbon emissions of a virtual power plant at any given moment; Is The charging power of the instantaneous energy storage device; Is The discharge power of the energy storage device at any given time. This scheme is based on the traditional carbon emission factor calculation method, and provides a dynamic carbon emission factor formula and calculation steps, providing a theoretical basis for subsequent steps to calculate the carbon offset price.

[0021] In step 2, by analyzing the operational characteristics of distributed energy sources and energy storage, as well as loads, composed of photovoltaic, wind power, and gas turbines within the virtual power plant, and based on the principle of node power conservation, an initial mathematical model of carbon emission intensity is constructed, and nodes are defined. The real-time initial carbon emission intensity is at node The weighted average of all incoming carbon streams, expressed mathematically as:

[0022] (3)

[0023] In the formula, Is Time Node Real-time initial carbon emission intensity; Is Time of the first Distributed energy flow nodes The active power; Is Time of the first The carbon emission intensity of distributed energy output; Is Mainnet nodes Flow to Node The power; Is Real-time carbon emission factor of the main network; For nodes A collection of interconnected distributed energy resources; For nodes A set of interconnected main grid nodes. Traditional methods for calculating carbon emission intensity only consider the carbon emission intensity of distributed energy sources and the power grid, while this step also considers the carbon emission intensity on the user side. This results in a more accurate node carbon emission intensity, leading to more precise calculations and better regulation effects in subsequent steps.

[0024] In step 3, a resource contribution weight is introduced based on the real-time initial carbon emission intensity. The carbon emission intensity of each node is adjusted to reflect the contribution of different resources to the overall carbon reduction benefits:

[0025] (4)

[0026] In the formula, It is a node exist Real-time adjusted virtual carbon intensity; It is a node exist Real-time initial carbon emission intensity at any given moment; Is Time Node The carbon reduction contribution weight of each resource reflects the magnitude of its contribution to reducing the total carbon emissions of the system. For virtual power plants The overall carbon reduction benefit coefficient at any given time

[0027] In the intraday scheduling of virtual power plants, when the dynamic carbon emission factor fluctuates between the average carbon emission factor of the previous day and the actual value of the previous day, the system automatically generates a corresponding carbon compensation price based on the direction of the deviation, in order to guide users to adjust their behavior precisely.

[0028] (5)

[0029] In the formula, yes Automatically generated carbon offset price in real time; It is a price sensitivity coefficient used to adjust the level of carbon offsetting. yes Dynamic carbon emission factors of a virtual power plant at all times; It is the average carbon emission factor of the virtual power plant the day before; This is a sign function used to determine the direction of deviation of the dynamic carbon emission factor from the average carbon emission factor of the previous day; when When this occurs, it indicates that the dynamic carbon emission factor deviates from the average carbon emission factor of the previous day, triggering the sign function. Automatically generate carbon offset price .

[0030] Electricity price adjustments based on dynamic carbon emission factors aim to achieve the effect of reducing electricity consumption when the carbon emission factor is high and the output of new energy sources is low, and increasing electricity consumption when the carbon emission factor is low and the output of new energy sources is high, thereby actively absorbing new energy sources.

[0031] In step 4, a comprehensive utility function is constructed based on the user's sensitivity to changes in carbon emission intensity. This is to achieve both the convenience of electricity use and environmental protection requirements.

[0032] Step 4-1, Comprehensive Utility Function Model:

[0033] The user's total utility consists of three parts: electricity utility, cost of insufficient electricity usage, and perceived benefit from carbon emission reduction.

[0034] (6)

[0035] In the formula, This represents the total overall utility of the user within the scheduling period; It is the scheduling cycle; For users Basic electricity efficiency at all times; yes User power consumption at any given moment; Costs related to user dissatisfaction with electricity usage; for The amount of load adjustment at any given time; To gain a sense of achievement from carbon emission reduction,

[0036] Step 4-2, Cost Model for Incomplete Electricity Usage:

[0037] A quadratic function is typically used to characterize user dissatisfaction with load changes; as the adjustment amount increases, dissatisfaction tends to rise at an accelerated rate.

[0038] (7)

[0039] In the formula, for Load adjustment amount at any time ; yes User power consumption at any given moment; yes The user's basic electricity consumption at any given time; and This represents the user's electricity preference coefficient; a higher value indicates that the user is more sensitive to changes in their electricity consumption habits.

[0040] Step 4-3, Sense of Gain from Carbon Emission Reduction:

[0041] The sensitivity of users to changes in carbon emission intensity is determined by carbon offset prices and actual carbon reductions:

[0042] (8)

[0043] In the formula, For users The carbon sensitivity coefficient; for The carbon offset price at any given time; Is Time Node Initial carbon emission intensity; Is Time Node The revised virtual carbon intensity. Users' electricity consumption habits have changed accordingly based on the price of electricity-carbon co-location: 1. The adjusted electricity price better reflects the actual carbon intensity in the current system; 2. Users' electricity consumption patterns have changed, and compensation is provided for the discomfort caused by the change in electricity consumption habits; 3. Rewards are given for users' actual behavior in participating in the "dual carbon" goals to incentivize users to actively participate in carbon reduction.

[0044] In step 5, to find the optimal equilibrium point for the virtual power plant's operation, multiple mutually constraining objective functions are set to achieve low-carbon operation while satisfying the physical constraints of the power system.

[0045] Step 5-1: Construction of multi-objective function:

[0046] 5-1.1 Objective 1: Maximize the economic benefits of the virtual power plant ( )

[0047] (9)

[0048] In the formula, It is the scheduling cycle; for The original time-of-use electricity price at any given moment; for Adaptive carbon offset price at any given time; Is Time Node The actual power; Is Revenue from selling electricity to the main grid at all times; yes The operating cost of a virtual power plant at any given time.

[0049] 5-1.2 Objective 2: Minimize total carbon emissions of the system ( )

[0050] (10)

[0051] In the formula, Is Time of the first The output power of a distributed energy source; Is Time of the first Carbon emission intensity of distributed energy sources; The quantity of distributed energy resources; Is Purchase power from the main grid at all times; Is The carbon emission intensity of electricity purchased from the main grid at all times. It is the scheduling time step.

[0052] 5-1.3 Objective 3: To achieve the highest overall user satisfaction ( )

[0053] (11)

[0054] in, For the comprehensive utility function,

[0055] Step 5-2, System Constraints

[0056] To ensure the model's feasibility at the physical and security levels, constraints need to be set:

[0057] 5-2.1 Power balance constraints:

[0058] (12)

[0059] In the formula, Power for charging energy storage devices; This refers to the discharge power of the energy storage device. Is Time Node The actual power It is the number of nodes.

[0060] 5-2.2 Energy Storage State of Charge ( Constraints:

[0061] (13) In the formula, It is the lower limit of the state of charge of the energy storage device. This represents the upper limit of the state of charge of the energy storage device. Is Real-time state of charge of the energy storage device ensures that the energy storage system operates within a safe charging and discharging range.

[0062] 5-2.3 Line safety constraints:

[0063] (14)

[0064] In the formula, This represents the minimum line power. This represents the maximum line power. Is The line power value at any given time is used to limit the power flow of each branch to not exceed the line thermal stability limit. This step comprehensively considers power balance constraints, energy storage state of charge constraints, and line safety constraints, setting three mutually restrictive objective functions to find the equilibrium point of the virtual power plant operation under the physical constraints of the power system.

[0065] Step 6 specifically involves solving the objective function using the previously given formula, and the calculation steps are as follows:

[0066] Step 6-1: Input the distributed energy output forecasts for each node of the virtual power plant, the real-time carbon emission factor of the main grid, the time-of-use electricity price, and the initial energy storage data. Status and user utility parameters;

[0067] Step 6-2: Calculate the real-time initial carbon emission intensity of each node according to the formula in Step 2. ;

[0068] Step 6-3: Based on Step 3, determine the direction of deviation between the dynamic carbon emission factor and the average carbon emission factor of the previous day, and automatically calculate the carbon offset price for each time period. ;

[0069] Step 6-4: Set decision variables, including distributed energy output in each time period. Energy storage charging and discharging power and With load adjustment ;

[0070] Step 6-5: Objective function evaluation and constraint check:

[0071] Calculate the current virtual power plant revenue, total system carbon emissions, and overall user satisfaction.

[0072] Check for violations of power balance, energy storage state of charge, and line safety constraints;

[0073] Step 6-6: By continuously updating the decision variables until the convergence condition is met, the final scheduling scheme is selected from the solution set using the resource contribution weight, the power control instructions of each unit are issued, and the dynamic carbon potential of each node is updated.

[0074] Compared to existing technologies, the advantages of this invention are as follows: Through the above steps, this invention reduces the total carbon emissions of virtual power plants by 8.55%, increases revenue by 9.68%, reduces user electricity costs by 4.91%, and improves user satisfaction by 5.03%. The method proposed in this invention can increase the flexibility and enthusiasm of virtual power plants and users to participate in the carbon market, and enable the load side to benefit from low-carbon demand response. It also increases the revenue of virtual power plants and promotes proactive carbon reduction by the load side. Furthermore, the strategy proposed in this invention can improve the local consumption rate of distributed energy, providing reliable technical support for virtual power plants to participate in the carbon trading market. Attached Figure Description

[0075] Figure 1 is a flowchart of a specific implementation method of the present invention;

[0076] Figure 2 shows the carbon emission intensity comparison curves;

[0077] Figure 3 shows the comparison curves of user-side demand response;

[0078] Figure 4 shows a comparison curve of electricity prices at different time periods. Detailed Implementation

[0079] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0080] Example: The low-carbon operation method of a virtual power plant based on dynamic carbon potential feedback and adaptive price correction according to the present invention includes the following steps:

[0081] Step 1: Analyze the characteristics of carbon emission flows from the virtual power plant and establish a mathematical model of dynamic carbon emission factors based on distributed energy and load;

[0082] Step 2: Analyze the characteristics of distributed energy, energy storage and load within the virtual power plant, and establish a real-time initial carbon emission intensity model for each node based on carbon flow tracing theory.

[0083] Step 3: Based on the original dynamic carbon emission factor, a resource contribution weight is introduced, and the virtual carbon emission intensity of each node is adjusted in real time according to the overall carbon reduction benefit of the virtual power plant. When the dynamic carbon emission factor deviates from the average carbon emission factor of the previous day, the system automatically generates a carbon compensation price based on the direction of deviation.

[0084] Step 4: Based on users' sensitivity to changes in carbon emission intensity, construct a comprehensive utility function that includes the cost of insufficient electricity consumption and the sense of gain from carbon emission reduction.

[0085] Step 5: With the objectives of maximizing virtual power plant revenue, minimizing total system carbon emissions, and maximizing overall user satisfaction, a multi-objective optimization scheduling model based on power balance, energy storage state of charge, and distribution network security constraints is constructed.

[0086] Step 6: Solve the objective function that optimizes the revenue of the virtual power plant, the total carbon emissions of the system, and the overall user satisfaction to achieve low-carbon operation of the virtual power plant.

[0087] In step 1, considering the characteristics of carbon emission flows from the virtual power plant, a mathematical model of the dynamic carbon emission factor of the virtual power plant is established. The virtual power plant is defined. Instantaneous total carbon emissions at any given moment for:

[0088] (1)

[0089] In the formula, Is Time of the first Output power of distributed energy sources (photovoltaics, wind power, and gas turbines);

[0090] Is Time of the first Carbon emission intensity of distributed energy sources; The quantity of distributed energy resources; Is Purchase power from the main grid at all times; Is The carbon emission intensity of electricity purchased from the main grid at all times.

[0091] Furthermore, define virtual power plants The ratio of total carbon emissions to total net power output at any given time is the dynamic carbon emission factor. :

[0092] (2)

[0093] In the formula, It is a virtual power plant Total instantaneous carbon emissions at any given moment; Is Time of the first Output power of distributed energy sources (photovoltaics, wind power, and gas turbines); Is Purchase power from the main grid at all times;

[0094] The quantity of distributed energy resources; Is The charging power of the instantaneous energy storage device; Is time

[0095] The discharge power of the energy storage device.

[0096] In step 2, the operational characteristics of the distributed energy resources, energy storage, and loads within the virtual power plant, consisting of photovoltaic, wind power, and gas turbines, are analyzed. Based on the principle of nodal power conservation, a mathematical model of the initial carbon emission intensity of each node is constructed, and the nodes are defined. The real-time initial carbon emission intensity is at node The weighted average of all incoming carbon streams, expressed mathematically as:

[0097] (3)

[0098] In the formula, It is a node exist Real-time initial carbon emission intensity at any given moment; In order to be in Time of the first Distributed energy flow nodes The active power; Is Time of the first The carbon emission intensity of distributed energy output; Is Mainnet nodes Flow to Node The power; Is Real-time carbon emission factor of the main network; For nodes A collection of interconnected distributed energy resources; It is with nodes A set of connected main network nodes.

[0099] In step 3, a resource contribution weight is introduced based on the real-time initial carbon emission intensity. Adjusting the carbon emission intensity at each node to reflect the contribution of different resources to the overall carbon reduction benefits:

[0100] (4)

[0101] In the formula, It is a node exist Virtual carbon intensity adjusted over time; It is a node exist Real-time initial carbon emission intensity at any given moment; Is Time Node The carbon reduction contribution weight of each resource reflects the magnitude of its contribution to reducing the total carbon emissions of the system. For virtual power plants The overall carbon reduction benefit coefficient at any given time.

[0102] In the intraday scheduling of virtual power plants, when the dynamic carbon emission factor fluctuates between the average carbon emission factor of the previous day and the actual value of the previous day, the system automatically generates a corresponding carbon compensation price based on the direction of the deviation, in order to guide users to adjust their behavior precisely.

[0103] (5)

[0104] In the formula, for Automatically generated carbon offset price in real time; This is a price sensitivity coefficient used to adjust the level of carbon offsetting. for Dynamic carbon emission factors of a virtual power plant at all times; This represents the average carbon emission factor of the virtual power plant from the previous day. It is a sign function used to determine the direction of deviation of the dynamic carbon emission factor from the average carbon emission factor of the previous day; when When this occurs, it indicates that the dynamic carbon emission factor deviates from the average carbon emission factor of the previous day, triggering the sign function. Automatically generate carbon offset price .

[0105] In step 4, a comprehensive utility function is constructed based on the user's sensitivity to changes in carbon emission intensity. This is to achieve both convenient electricity use and environmental protection requirements.

[0106] Step 4-1, Comprehensive Utility Function Model:

[0107] The user's total utility consists of three parts: electricity utility, cost of insufficient electricity usage, and perceived benefit from carbon emission reduction.

[0108] (6)

[0109] In the formula, This represents the total overall utility of the user within the scheduling period; It is the scheduling cycle; For users Basic electricity efficiency at all times; yes User power consumption at any given moment; Costs related to user dissatisfaction with electricity usage; for The amount of load adjustment at any given time; To gain a sense of achievement from carbon emission reduction.

[0110] Step 4-2, Cost Model for Incomplete Electricity Usage:

[0111] A quadratic function is typically used to characterize user dissatisfaction with load changes; as the adjustment amount increases, dissatisfaction tends to rise at an accelerated rate.

[0112] (7)

[0113] In the formula, for Load adjustment amount at any time ; yes User power consumption at any given moment; yes The user's basic electricity consumption at any given time; and This is the user's electricity preference coefficient; the higher the value, the more sensitive the user is to changes in their electricity usage habits.

[0114] Step 4-3, Sense of Gain from Carbon Emission Reduction:

[0115] The sensitivity of users to changes in carbon emission intensity is determined by carbon offset prices and actual carbon reductions:

[0116] (8)

[0117] In the formula, For users The carbon sensitivity coefficient; for The carbon offset price at any given time; Is Time Node Initial carbon emission intensity; Is Time Node The revised virtual carbon intensity.

[0118] In step 5, in order to find the optimal balance point for the operation of the virtual power plant, multiple mutually constraining objective functions are set to achieve low-carbon operation under the premise of satisfying the physical constraints of the power system.

[0119] Step 5-1: Construction of multi-objective function:

[0120] 5-1.1 Objective 1: Maximize the economic benefits of the virtual power plant ( )

[0121] (9)

[0122] In the formula, It is the scheduling cycle; for The original time-of-use electricity price at any given moment; for Adaptive carbon offset price at any given time; Is Time Node The actual power; Is Revenue from selling electricity to the main grid at all times; yes The operating cost of a virtual power plant at any given moment.

[0123] 5-1.2 Objective 2: Minimize total carbon emissions of the system ( )

[0124] (10)

[0125] In the formula, It is the scheduling cycle; Is Time of the first Output power of distributed energy sources (photovoltaics, wind power, and gas turbines); Is Time of the first Carbon emission intensity of distributed energy sources; The quantity of distributed energy resources; Is Purchase power from the main grid at all times; Is The carbon emission intensity of electricity purchased from the main grid at all times. It is the scheduling time step.

[0126] 5-1.3 Objective 3: To achieve the highest overall user satisfaction ( )

[0127] (11)

[0128] in, This is a comprehensive utility function.

[0129] Step 5-2, System Constraints

[0130] To ensure the model's feasibility at the physical and security levels, the following agreements need to be set.

[0131] 5-2.1 Power balance constraints:

[0132] (12)

[0133] In the formula, Is Time of the first Output power of distributed energy sources (photovoltaics, wind power, and gas turbines); The quantity of distributed energy resources; Is Purchase power from the main grid at all times; Power for charging energy storage devices; This refers to the discharge power of the energy storage device. Is Time Node The actual power It represents the number of nodes.

[0134] 5-2.2 Energy Storage State of Charge ( Constraints:

[0135] (13) In the formula, It is the lower limit of the state of charge of the energy storage device. This represents the upper limit of the state of charge of the energy storage device. Is Real-time state of charge of energy storage devices ensures that the energy storage system operates within a safe charging and discharging range.

[0136] 5-2.3 Line safety constraints:

[0137] (14)

[0138] In the formula, This represents the minimum line power. This represents the maximum line power. Is The line power value at any given time limits the power flow of each branch to ensure it does not exceed the line thermal stability limit.

[0139] Step 6 specifically involves solving the objective function based on the previously given formula, and the calculation steps are as follows:

[0140] Step 6-1: Input the distributed energy output forecast of each node in the virtual power plant, the real-time carbon factor of the main grid, the time-of-use electricity price, and the initial energy storage data. Status and user utility parameters;

[0141] Step 6-2: Calculate the real-time initial carbon emission intensity of each node according to the formula in Step 2. ;

[0142] Step 6-3: Based on Step 3, determine the direction of deviation between the dynamic carbon emission factor and the average carbon emission factor of the previous day, and automatically calculate the carbon offset price for each time period. ;

[0143] Step 6-4: Set decision variables, including distributed energy output in each time period. Energy storage charging and discharging power and With load adjustment ;

[0144] Step 6-5: Objective function evaluation and constraint check:

[0145] Calculate the current virtual power plant revenue, total system carbon emissions, and overall user satisfaction.

[0146] Check for violations of power balance, energy storage state of charge, and line safety constraints;

[0147] Step 6-6: Continuously update the decision variables until the convergence condition is met, and then utilize the resource contribution weights.

[0148] The final scheduling scheme is selected from the solution set, power control commands are issued to each unit, and the dynamic carbon potential of each node is updated.

[0149] Based on a low-carbon operation model for virtual power plants with dynamic carbon potential feedback and adaptive price correction, this invention sets up two scenarios and analyzes and verifies the proposed strategy in a 10-node virtual power plant system:

[0150] Scenario 1: Considering the net revenue and carbon emissions of a virtual power plant, but not the synergistic effect of electricity and carbon emissions;

[0151] Scenario 2: Net revenue and carbon emissions of a virtual power plant based on the synergistic effect of electricity and carbon.

[0152] To verify the effectiveness of the proposed low-carbon operation method for virtual power plants based on dynamic carbon potential feedback and adaptive price correction, simulation and verification were conducted in a 10-node virtual power plant system. The virtual power plant is connected to the main grid via node 1; photovoltaic power is located at node 3 with an installed capacity of 2MW; wind turbines are located at node 7 with an installed capacity of 1.4MW; energy storage devices are located at node 5 with a maximum charge / discharge power of 0.8MW and a charging efficiency of 0.95% and a discharging efficiency of 0.95%; a gas turbine with a rated power of 0.6MW is located at node 2; the corresponding cost coefficients are a=0.05, b=20, c=100; the operating cycle in the simulation is set to 24 hours per day. The time-of-use electricity pricing adopted is shown in Table 1.

[0153] Table 1 Time-of-use electricity prices

[0154]

[0155] By comparing the results of Scenario 1 and Scenario 2, the effectiveness of the virtual power plant low-carbon operation strategy based on dynamic carbon potential feedback and adaptive price correction was verified. Figure 2 shows the operational results under the same settings, illustrating the daily carbon emission intensity trends for both scenarios. As can be seen from Figure 2, the proposed low-carbon operation mechanism of the virtual power plant based on dynamic carbon potential feedback and adaptive price correction can more flexibly adjust node carbon emission intensity and, combined with electricity price signals, effectively guide load shifting. Scenario 2 reduces carbon emission intensity during low-carbon periods, prompting user load to concentrate during these periods, while appropriately increasing electricity prices during high-carbon periods to suppress demand and reduce the impact of the time-of-use price difference with carbon signals. This adjustment can promote the carbon reduction benefits of the virtual power plant and reduce users' electricity costs, as shown in Figure 3. Scenario 2 not only reduced the total carbon emissions of the virtual power plant by 8.55% and increased revenue by 9.68%, but also reduced user electricity costs by 4.91% and increased user satisfaction by 5.03%.

[0156] Under the same settings, the electricity price trend is shown in Figure 4. Compared with Scenario 1, Scenario 2's dynamic electricity pricing achieves real-time mapping of high carbon intensity with high prices and low carbon intensity with low prices. It provides reasonable pricing for basic services during zero-carbon periods, maintains price incentives during high-carbon periods, and exhibits continuous and smooth adjustment capabilities that Scenario 1 lacks during transitional periods of rapid carbon intensity changes. This pricing mechanism retains the guiding role of peak-valley electricity pricing in demand response while using carbon price signals to encourage users to shift their electricity load to clean energy periods, ensuring a better synergy between economic benefits and low-carbon goals.

[0157] The method proposed in this invention achieves accurate calculation of user-side carbon emissions, improves the system's adaptability to energy fluctuations and load changes, and forms a win-win situation for collaborative emission reduction between virtual power plants and users. This invention compares the low-carbon demand response in scenarios 1 and 2, and the specific results are shown in Table 2. The scheduling strategy proposed in this invention effectively coordinates and optimizes low-carbon goals and economic benefits, and more fully taps the emission reduction potential of virtual power plants.

[0158] Table 2 Comparison of Low-Carbon Demand Response in Different Scenarios

[0159]

[0160] This invention explores methods for electricity-carbon synergy after distributed energy resources are integrated into virtual power plants (VPS). It proposes a low-carbon operation method for VPS based on dynamic carbon potential feedback and adaptive price correction. Through carbon emission intensity feedback and adaptive price correction mechanisms, the method reduces the scissors difference between electricity price and carbon signals and its impact on demand response. Case studies validate the proposed strategy. Simulation results show that the proposed method can increase the flexibility and enthusiasm of VPS and users in participating in the carbon market, enabling the load side to benefit from low-carbon demand response. It also increases the revenue of VPS and promotes proactive carbon reduction by the load side. Furthermore, the proposed strategy can improve the local consumption rate of distributed energy resources.

[0161] The above description is merely a preferred embodiment of the present invention and is not intended to further limit the present invention. All equivalent changes made based on the description and drawings of the present invention are within the protection scope of the present invention.

Claims

1. A low-carbon operation method for a virtual power plant based on dynamic carbon potential feedback and adaptive price correction, characterized in that, Includes the following steps: Step 1: Analyze the characteristics of carbon emission flows from the virtual power plant and establish a dynamic carbon emission factor mathematical model based on distributed energy resources and loads. Step 2: Analyze the characteristics of distributed energy sources, energy storage devices, and loads within the virtual power plant. Based on carbon flow tracing theory, establish a real-time initial carbon emission intensity model for each node. Step 3: Based on the original dynamic carbon emission factor, a resource contribution weight is introduced. The virtual carbon emission intensity of each node is adjusted in real time according to the overall carbon reduction effect of the virtual power plant. When the dynamic carbon emission factor deviates from the average carbon emission factor of the previous day, the system automatically generates a corresponding carbon compensation price based on the direction of the deviation. Step 4: Based on users' sensitivity to changes in carbon emission intensity, construct a comprehensive utility function that includes the cost of insufficient electricity supply and the perceived benefits of carbon emission reduction. Step 5: With the objectives of maximizing virtual power plant revenue, minimizing total system carbon emissions, and maximizing overall user satisfaction, a multi-objective optimization scheduling model based on power balance, energy storage state of charge, and distribution network security constraints is constructed. Step 6: Solve the objective function that optimizes the revenue of the virtual power plant, the total carbon emissions of the system, and the overall user satisfaction to achieve low-carbon operation of the virtual power plant.

2. The low-carbon operation method of a virtual power plant based on dynamic carbon potential feedback and adaptive price correction according to claim 1, characterized in that, In step 1, considering the characteristics of carbon emission flows from the virtual power plant, a mathematical model of the dynamic carbon emission factor of the virtual power plant is established, and the virtual power plant is defined. Instantaneous total carbon emissions at any given moment for: (1) In the formula, Is Time of the first The output power of a distributed energy source; Is Time of the first Carbon emission intensity of distributed energy sources; It refers to the quantity of distributed energy resources; It is a collection of distributed energy resources; It is the first in the distributed energy collection One distributed energy source; Is Purchase power from the main grid at all times; Is The carbon emission intensity of electricity purchased from the main grid at all times. Defining virtual power plants in The ratio of total carbon emissions to total net power output at any given time is the dynamic carbon emission factor. : (2) In the formula, Is The instantaneous total carbon emissions of a virtual power plant at any given moment; Is The charging power of the instantaneous energy storage device; Is The discharge power of the energy storage device at any given time.

3. The low-carbon operation method of a virtual power plant based on dynamic carbon potential feedback and adaptive price correction according to claim 2, characterized in that, In step 2, by analyzing the operational characteristics of distributed energy sources and energy storage, as well as loads, composed of photovoltaic, wind power, and gas turbines within the virtual power plant, and based on the principle of nodal power conservation, an initial mathematical model of carbon emission intensity is constructed, and nodes are defined. The real-time initial carbon emission intensity is at node The weighted average of all incoming carbon streams, expressed mathematically as: (3) In the formula, Is Time Node Real-time initial carbon emission intensity; Is Time of the first Distributed energy flow nodes The active power; Is Time of the first The carbon emission intensity of distributed energy output; Is Mainnet nodes Flow to Node The power; Is Real-time carbon emission factor of the main network; For nodes A collection of interconnected distributed energy resources; For nodes A set of connected main network nodes.

4. The low-carbon operation method of a virtual power plant based on dynamic carbon potential feedback and adaptive price correction according to claim 3, characterized in that, In step 3, a resource contribution weight is introduced based on the real-time initial carbon emission intensity. The carbon emission intensity of each node is adjusted to reflect the contribution of different resources to the overall carbon reduction benefits: (4) In the formula, It is a node exist Real-time adjusted virtual carbon intensity; It is a node exist Real-time initial carbon emission intensity at any given moment; Is Time Node The carbon reduction contribution weight of each resource reflects the magnitude of its contribution to reducing the total carbon emissions of the system. For virtual power plants The overall carbon reduction benefit coefficient at any given time In the intraday scheduling of virtual power plants, when the dynamic carbon emission factor fluctuates between the current day's dynamic carbon emission factor and the average carbon emission factor of the previous day, the system automatically generates a corresponding carbon compensation price based on the direction of the deviation, in order to guide users to adjust their behavior precisely. (5) In the formula, Is Automatically generated carbon offset price in real time; It is a price sensitivity coefficient used to adjust the level of carbon offsetting. yes Dynamic carbon emission factors of a virtual power plant at all times; It is the average carbon emission factor of the virtual power plant the day before; This is a sign function used to determine the direction of deviation of the dynamic carbon emission factor from the average carbon emission factor of the previous day; when When this occurs, it indicates that the dynamic carbon emission factor deviates from the average carbon emission factor of the previous day, triggering the sign function. Automatically generate carbon offset price .

5. A low-carbon operation method for a virtual power plant based on dynamic carbon potential feedback and adaptive price correction according to claim 4, characterized in that, In step 4, a comprehensive utility function is constructed based on the user's sensitivity to changes in carbon emission intensity. To achieve both convenient electricity use and environmental protection requirements, Step 4-1, Comprehensive Utility Function Model: The user's total utility consists of three parts: electricity utility, cost of insufficient electricity usage, and perceived benefit from carbon emission reduction. (6) In the formula, This represents the total overall utility of the user within the scheduling period; It is the scheduling cycle; For users Basic electricity efficiency at all times; yes User power consumption at any given moment; To account for the costs of unsatisfactory electricity usage, and to quantify the inconvenience that load adjustments bring to users' lives; for The amount of load adjustment at any given time; To gain a sense of achievement from carbon emission reduction, Step 4-2, Cost Model for Incomplete Electricity Usage: A quadratic function is typically used to characterize user dissatisfaction with load changes; as the adjustment amount increases, dissatisfaction tends to rise at an accelerated rate. (7) In the formula, for Load adjustment amount at any time ; yes User power consumption at any given moment; yes The user's basic electricity consumption at any given time; and This represents the user's electricity preference coefficient; a higher value indicates that the user is more sensitive to changes in their electricity consumption habits. Step 4-3, Sense of Gain from Carbon Emission Reduction: The sensitivity of users to changes in carbon emission intensity is determined by carbon offset prices and actual carbon reductions: (8) In the formula, For users The carbon sensitivity coefficient; for The carbon offset price at any given time; Is Time Node Initial carbon emission intensity; Is Time Node The revised virtual carbon intensity.

6. The low-carbon operation method of a virtual power plant based on dynamic carbon potential feedback and adaptive price correction according to claim 5, characterized in that, In step 5, to find the optimal equilibrium point for the virtual power plant's operation, multiple mutually constraining objective functions are set to achieve low-carbon operation while satisfying the physical constraints of the power system. Step 5-1: Construction of multi-objective function: 5-1.1 Objective 1: Maximize the economic benefits of the virtual power plant (9) In the formula, It is the scheduling cycle; for The original time-of-use electricity price at any given moment; for Adaptive carbon offset price at any given time; Is Time Node The actual power; Is Revenue from selling electricity to the main grid at all times; yes The operating cost of a virtual power plant at any given time. 5-1.2 Objective 2: Minimize total carbon emissions of the system ( ) (10) In the formula, Is Time of the first The output power of a distributed energy source; Is Time of the first Carbon emission intensity of distributed energy sources; The quantity of distributed energy resources; Is Purchase power from the main grid at all times; Is The carbon emission intensity of electricity purchased from the main grid at all times. It is the scheduling time step. 5-1.3 Objective 3: To achieve the highest overall user satisfaction ( ) (11) in, For the comprehensive utility function, Step 5-2, System Constraints To ensure the model's feasibility at the physical and security levels, constraints need to be set: 5-2.1 Power balance constraints: (12) In the formula, Power for charging energy storage devices; This refers to the discharge power of the energy storage device. Is Time Node The actual power It is the number of nodes. 5-2.2 Energy Storage State of Charge ( Constraints: (13), In the formula, It is the lower limit of the state of charge of the energy storage device. This represents the upper limit of the state of charge of the energy storage device. Is Real-time state of charge of the energy storage device ensures that the energy storage system operates within a safe charging and discharging range. 5-2.3 Line safety constraints: (14) In the formula, This represents the minimum line power. This represents the maximum line power. Is The line power value at any given time limits the power flow of each branch to ensure it does not exceed the line thermal stability limit.

7. A low-carbon operation method for a virtual power plant based on dynamic carbon potential feedback and adaptive price correction according to claim 6, characterized in that, Step 6 specifically involves solving the objective function based on the previously given formula, and the calculation steps are as follows: Step 6-1: Input the distributed energy output forecast of each node in the virtual power plant, the real-time carbon emission factor of the main grid, the time-of-use electricity price, and the initial energy storage data. Status and user utility parameters; Step 6-2: Calculate the real-time initial carbon emission intensity of each node according to the formula in Step 2. ; Step 6-3: Based on Step 3, determine the direction of deviation between the dynamic carbon emission factor and the average carbon emission factor of the previous day, and automatically calculate the carbon offset price for each time period. ; Step 6-4: Set decision variables, including distributed energy output in each time period. Energy storage charging and discharging power and With load adjustment ; Step 6-5: Objective function evaluation and constraint check: Calculate the current virtual power plant revenue, total system carbon emissions, and overall user satisfaction. Check for violations of power balance, energy storage state of charge, and line safety constraints; Step 6-6: By continuously updating the decision variables until the convergence condition is met, the final scheduling scheme is selected from the solution set using the resource contribution weight, the power control instructions of each unit are issued, and the dynamic carbon potential of each node is updated.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the low-carbon operation method of the virtual power plant based on dynamic carbon potential feedback and price adaptive correction as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, they implement the low-carbon operation method of the virtual power plant based on dynamic carbon potential feedback and price adaptive correction as described in any one of claims 1-7.

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