A virtual power plant electric carbon market day-ahead bidding method and device and a storage medium

Abstract

The application provides a virtual power plant electricity-carbon market day-ahead bidding method and device and a storage medium, relates to the technical field, and solves the problem that the prior art lacks robust consideration of uncertainty in the real-time operation stage, which leads to the fact that the virtual power plant is prone to causing assessment punishment and loss risk when a prediction deviation occurs. The method specifically comprises the following steps: obtaining operation parameters of each distributed energy source in the virtual power plant, load prediction data, and market electricity price signals and carbon price signals; determining a day-ahead bidding optimization model based on the operation parameters, the load prediction data, the market electricity price signals and the carbon price signals; and solving the day-ahead bidding optimization model by using a two-stage robust optimization algorithm to generate a day-ahead purchase and sale electricity power plan of the virtual power plant and real-time power adjustment strategies of each distributed energy source. The application is used in the virtual power plant electricity-carbon market day-ahead bidding.

Description

Technical Field

[0001] This application relates to the field of virtual power plant technology, and in particular to a virtual power plant carbon market day-ahead bidding method, apparatus and storage medium. Background Technology

[0002] Currently, in the joint dispatch of the electricity market and carbon market, virtual power plants typically formulate day-ahead power plans based on deterministic forecasts and manage carbon costs using a single benchmark quota or tiered carbon pricing mechanism. However, in actual operating scenarios where wind and solar power output has strong randomness, existing solutions fail to fully quantify the physical contribution of clean energy power generation to carbon quotas and lack robust mechanisms to cope with real-time power output fluctuations. This leads to significant deviations in the generated day-ahead power plans during real-time execution. Therefore, existing technologies lack robust consideration for uncertainties in real-time operation, making virtual power plants prone to performance penalties and loss risks when forecasting deviations occur. Summary of the Invention

[0003] This application provides a method, apparatus, and storage medium for bidding on the day-ahead carbon market for virtual power plants, which solves the problem that the existing technology lacks robust consideration of uncertainties in the real-time operation stage, which makes virtual power plants prone to performance penalties and loss risks when prediction deviations occur.

[0004] To achieve the above objectives, this application adopts the following technical solution: Firstly, a day-ahead bidding method for the carbon market of a virtual power plant is provided, comprising: acquiring the operating parameters, load forecast data, and market electricity price and carbon price signals of each distributed energy source within the virtual power plant; determining a day-ahead bidding optimization model based on the operating parameters, load forecast data, market electricity price signals, and carbon price signals, wherein the day-ahead bidding optimization model includes a composite carbon quota constraint based on a benchmark quota and clean energy generation incentives, and a box uncertainty set constraint constructed based on uncertainty adjustment parameters; and solving the day-ahead bidding optimization model using a two-stage robust optimization algorithm to generate the day-ahead power purchase and sale plan of the virtual power plant and the real-time power adjustment strategy of each distributed energy source.

[0005] The above scheme embeds composite carbon quota constraints and box uncertainty set constraints into the day-ahead bidding optimization model and uses a two-stage robust optimization algorithm to solve it. This makes the generated power plan reflect the physical and environmental value of clean energy and have the ability to resist real-time power output fluctuations, thereby improving scheduling safety and compliance while ensuring system power balance.

[0006] In conjunction with the first aspect mentioned above, one possible implementation method involves a composite carbon quota constraint based on a benchmark quota and clean energy power generation incentives, which includes: determining a benchmark carbon quota based on the power generation of gas turbines and the carbon quota benchmark coefficient; determining a carbon quota incentive based on the power generation of wind turbines and photovoltaic units and the incentive coefficient; determining carbon emissions based on the power generation of gas turbines and the carbon emission factor coefficient; and determining a composite carbon quota constraint based on the benchmark carbon quota, the carbon quota incentive, and the carbon emissions.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, the composite carbon quota constraint satisfies the following relationship:

[0008] in, , These represent the carbon allowance and carbon emissions of a virtual power plant during time period t under the electricity price scenario s. This serves as the baseline coefficient for carbon quotas for gas turbines. For the gas turbine's output during the day-ahead phase t period, This refers to the real-time output adjustment of the gas turbine during the day-ahead period (t). , These are the incentive coefficients for wind turbines and photovoltaic units, respectively. , These are the actual outputs of wind turbines and photovoltaic units, respectively. This refers to the carbon emission factor coefficient of the gas turbine. This refers to the number of time periods within a bidding cycle.

[0009] In conjunction with the first aspect mentioned above, in one possible implementation, the box-shaped uncertainty set constraint constructed based on the uncertainty adjustment parameter includes: obtaining the day-ahead predicted output, maximum fluctuation coefficient, and uncertainty adjustment parameter of the wind turbine and photovoltaic unit; determining the fluctuation boundary of the actual output in the real-time stage based on the day-ahead predicted output, maximum fluctuation coefficient, and uncertainty adjustment parameter; and generating the box-shaped uncertainty set constraint based on the fluctuation boundary.

[0010] In conjunction with the first aspect mentioned above, in one possible implementation, the box-type uncertain set constraint satisfies the following relationship:

[0011] in, This represents the set of uncertainties regarding the actual output of wind turbines and photovoltaic units. , , These are the actual power output and their combined output of wind turbines and photovoltaic units, respectively, considering uncertainties. , The day-ahead forecast power outputs for wind turbines and photovoltaic units are respectively. , These are the maximum output fluctuation coefficients for wind turbines and photovoltaic units, respectively. and For state variables, a value of 1 indicates that the output drops to the lower boundary during time period t. and These are the set uncertainty adjustment parameters for wind turbines and photovoltaic units, respectively.

[0012] In conjunction with the first aspect mentioned above, one possible implementation involves using a two-stage robust optimization algorithm to solve the day-ahead bidding optimization model, generating the day-ahead power purchase and sale plan for the virtual power plant and the real-time power adjustment strategy for each distributed energy source. This includes: decomposing the day-ahead bidding optimization model into a main problem and sub-problems; the main problem using the day-ahead power purchase and sale plan as the decision variable to solve for the upper bound of the objective function; and the sub-problems finding the worst-case scenario for wind and solar power under a given day-ahead decision and updating the lower bound of the objective function; updating the constraints corresponding to the worst-case scenario based on the iterative solution results of the main problem and sub-problems; and outputting the day-ahead power purchase and sale plan and the real-time power adjustment strategy in response to the upper and lower bounds of the objective function satisfying a preset convergence accuracy.

[0013] In conjunction with the first aspect mentioned above, in one possible implementation, the constraints corresponding to the worst-case landscape scenario are updated based on the iterative solution results of the main problem and sub-problems. This includes: initializing the initial landscape scenario as the day-ahead predicted power output scenario, and setting an initial upper bound, an initial lower bound, and a preset convergence precision; solving the main problem under the current worst-case scenario set to obtain the optimal day-ahead decision and the main problem objective value, and updating the upper bound of the objective function based on the main problem objective value; substituting the optimal day-ahead decision into the sub-problems to obtain new worst-case landscape scenarios and sub-problem objective values, and updating the lower bound of the objective function based on the sub-problem objective values; determining that the algorithm has converged in response to the difference between the upper bound and the lower bound of the objective function satisfying the preset convergence precision; otherwise, adding the constraints corresponding to the new worst-case landscape scenario to the main problem and returning to the steps of solving the main problem.

[0014] In conjunction with the first aspect mentioned above, in one possible implementation, the day-ahead bidding optimization model also includes day-ahead stage constraints and real-time stage constraints. The day-ahead stage constraints include: day-ahead power balance constraints, day-ahead market power purchase and sale plan upper and lower limit constraints, gas turbine day-ahead output constraints, energy storage charging and discharging power and state of charge day-ahead constraints, and demand response day-ahead constraints. The real-time stage constraints include: real-time power balance constraints, gas turbine real-time output adjustment constraints, energy storage charging and discharging power and state of charge real-time constraints, demand response real-time adjustment constraints, and real-time market power purchase and sale constraints.

[0015] Secondly, a day-ahead bidding device for a virtual power plant's carbon market is provided, comprising: a communication unit and a processing unit; the communication unit is used to acquire operating parameters, load forecast data, and market electricity price and carbon price signals of each distributed energy source within the virtual power plant; the processing unit is used to determine a day-ahead bidding optimization model based on the operating parameters, load forecast data, market electricity price signals, and carbon price signals. The day-ahead bidding optimization model includes a composite carbon quota constraint based on a benchmark quota and clean energy generation incentives, and a box-type uncertainty set constraint constructed based on uncertainty adjustment parameters; a two-stage robust optimization algorithm is used to solve the day-ahead bidding optimization model to generate the day-ahead power purchase and sale plan of the virtual power plant and the real-time power adjustment strategy of each distributed energy source.

[0016] Thirdly, this application provides a virtual power plant carbon market day-ahead bidding device, comprising: a processor and a storage medium; the storage medium includes instructions, and the processor is used to execute the instructions to implement the method as described in the first aspect and any possible implementation thereof. This virtual power plant carbon market day-ahead bidding device can be an electronic device or a chip within an electronic device.

[0017] Fourthly, this application provides a day-ahead bidding system for the carbon market of a virtual power plant, comprising: a parameter acquisition module and a day-ahead bidding device for the carbon market of a virtual power plant; wherein, the parameter acquisition module is used to acquire the operating parameters, load forecast data, and market electricity price and carbon price signals of each distributed energy source within the virtual power plant; the day-ahead bidding device is used to acquire the operating parameters, load forecast data, and market electricity price and carbon price signals of each distributed energy source within the virtual power plant; based on the operating parameters, load forecast data, market electricity price signals, and carbon price signals, a day-ahead bidding optimization model is determined, the day-ahead bidding optimization model including a composite carbon quota constraint based on the benchmark quota and clean energy power generation incentives, and a box uncertainty set constraint constructed based on uncertainty adjustment parameters; a two-stage robust optimization algorithm is used to solve the day-ahead bidding optimization model to generate the day-ahead power purchase and sale plan of the virtual power plant and the real-time power adjustment strategy of each distributed energy source.

[0018] Fifthly, this application provides a computer-readable storage medium storing instructions that, when executed on a virtual power plant carbon market day-ahead bidding device, cause the virtual power plant carbon market day-ahead bidding device to perform the method described in the first aspect and any possible implementation thereof.

[0019] In a sixth aspect, this application provides a computer program product containing instructions that, when the computer program product is run on a virtual power plant carbon market day-ahead bidding device, cause the virtual power plant carbon market day-ahead bidding device to perform the methods described in the first aspect and any possible implementation thereof.

[0020] This application constructs a box-type uncertainty set constraint based on uncertainty adjustment parameters to confine the random fluctuations of wind and solar power output within a physical boundary defined by the predicted value, the maximum fluctuation coefficient, and the conservatism parameter. Combined with the master-slave iterative solution mechanism of a two-stage robust optimization algorithm, the generated day-ahead power plan is mathematically guaranteed to be feasible for the worst-case scenario and physically ensures that the equipment adjustment capability covers real-time deviations, thereby reducing the risk of power default and assessment costs caused by inaccurate predictions.

[0021] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0022] Figure 1 A system architecture diagram of a virtual power plant carbon market day-ahead bidding system provided for embodiments of this application; Figure 2 A flowchart illustrating a day-ahead bidding method for a virtual power plant's carbon market, provided as an embodiment of this application. Figure 1 ; Figure 3 A flowchart illustrating a day-ahead bidding method for a virtual power plant's carbon market, provided as an embodiment of this application. Figure 2 ; Figure 4 A flowchart illustrating a day-ahead bidding method for a virtual power plant's carbon market, provided as an embodiment of this application. Figure 3 ; Figure 5 A flowchart illustrating a day-ahead bidding method for a virtual power plant's carbon market, provided as an embodiment of this application. Figure 4 ; Figure 6 A schematic diagram of a virtual power plant carbon market day-ahead bidding device provided for embodiments of this application; Figure 7 This is a schematic diagram of the hardware structure of a virtual power plant carbon market day-ahead bidding device provided in an embodiment of this application. Detailed Implementation

[0023] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0024] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0025] Example 1: The virtual power plant carbon market day-ahead bidding method provided in this application embodiment can be applied to, for example... Figure 1 The virtual power plant carbon market day-ahead bidding system shown includes: a parameter acquisition module 101 and a virtual power plant carbon market day-ahead bidding device 102.

[0026] The parameter acquisition module 101 is used to collect the operating parameters, load forecast data, market electricity price signals, and carbon price signals of each distributed energy source within the virtual power plant. The day-ahead bidding device 102 of the virtual power plant's carbon market is used to acquire the operating parameters, load forecast data, market electricity price signals, and carbon price signals of each distributed energy source within the virtual power plant. Based on the operating parameters, load forecast data, market electricity price signals, and carbon price signals, a day-ahead bidding optimization model is determined. The day-ahead bidding optimization model includes a composite carbon quota constraint based on the benchmark quota and clean energy power generation incentives, as well as a box-type uncertainty set constraint constructed based on uncertainty adjustment parameters. A two-stage robust optimization algorithm is used to solve the day-ahead bidding optimization model, generating the day-ahead power purchase and sale plan of the virtual power plant and the real-time power adjustment strategy of each distributed energy source.

[0027] For example, the parameter acquisition module is an API interface.

[0028] Example 2: To address the lack of robust consideration for uncertainties in real-time operation in existing technologies, which leads to performance penalties and losses for virtual power plants due to prediction deviations, this application provides a day-ahead bidding method for the virtual power plant's carbon market. This method includes: acquiring energy operating parameters and electricity and carbon price signals; determining a day-ahead bidding optimization model; solving the day-ahead bidding optimization model using a two-stage robust optimization algorithm; and generating a real-time power adjustment strategy for the virtual power plant. Based on this, by embedding composite carbon quota constraints and box uncertainty set constraints into the day-ahead bidding optimization model and solving it using a two-stage robust optimization algorithm, the generated power plan reflects both the physical and environmental value of clean energy and has the ability to withstand real-time power fluctuations, thereby improving dispatch safety and compliance while ensuring system power balance.

[0029] like Figure 2 As shown in the embodiments of this application, the virtual power plant carbon market day-ahead bidding method includes: S201. Obtain the operating parameters, load forecast data, market electricity price signals, and carbon price signals of each distributed energy source within the virtual power plant.

[0030] Among them, the operating parameters characterize the physical operating boundary and state characteristics of distributed energy, the load forecast data reflects the baseline of electricity demand of users aggregated by the virtual power plant, and the market electricity price signal and carbon price signal provide economic incentive guidance for power dispatch and carbon emissions from the external environment.

[0031] In this embodiment, a data acquisition module deployed at the edge of the virtual power plant or in the cloud reads the equipment ledgers and real-time measurement data of wind turbines, photovoltaic units, gas turbines, energy storage devices, and demand response loads in real time or at regular intervals, serving as the input source for operating parameters. Simultaneously, it connects with the information flow released by the meteorological forecast system and the power grid dispatch center to analyze and obtain the load forecast curves, time-of-use electricity price sequences, and carbon trading price signals for the next 24 hours at 96 points or higher resolution.

[0032] It should be noted that the data obtained should not only include deterministic values, but also statistical features that characterize uncertainty, such as the historical error distribution of wind and solar power output and the confidence interval of electricity price fluctuations. These implicit parameters are the basis for building subsequent robust optimization models, rather than just for deterministic prediction.

[0033] Based on the above steps, a digital mapping relationship between the virtual power plant physical entity and the external market environment was established, providing complete and practical boundary conditions and incentive signals for subsequent optimization models.

[0034] S202. Based on operating parameters, load forecast data, market electricity price signals, and carbon price signals, determine the day-ahead bidding optimization model.

[0035] Among them, the day-ahead bidding optimization model includes a composite carbon quota constraint based on the benchmark quota and clean energy power generation incentive, as well as a box-type uncertainty set constraint constructed based on uncertainty adjustment parameters.

[0036] In this embodiment of the application, based on the setting of virtual power plants participating in the electricity carbon market as price takers, a day-ahead bidding optimization model is constructed with the objective function of maximizing the net benefit including day-ahead market revenue, real-time market revenue, carbon market revenue and various costs. The model is structured to cover electricity price scenario clustering, day-ahead / real-time stage constraints, carbon market carbon quota trading and compliance constraints, and box uncertainty set constraints based on uncertainty adjustment parameters.

[0037] The objective function satisfies the following relationship:

[0038] in, Let S represent the net revenue of the virtual power plant, and let S be the set of electricity price scenarios. For electricity price scenarios The probability of occurrence; , , These are the day-ahead market revenue, real-time market revenue, and carbon market revenue under the electricity price scenario 's'. , , These represent the costs of gas turbine power generation, energy storage charging and discharging, and demand response under electricity price scenario s.

[0039] The day-ahead market return, real-time market return, and carbon market return satisfy the following relationship:

[0040] in, , These represent the day-ahead market power sales and purchase plans of the virtual power plant during time period t. , These are the day-ahead market sales and purchase prices for electricity during time period t. , These represent the real-time market sales and purchase power during time period t. , These represent the real-time market sales and purchase prices of electricity during time period t. For carbon price in time period t, , These represent the carbon allowances sold and bought during period t, respectively, where T is the number of periods in a bidding cycle.

[0041] The cost of gas turbine power generation, the cost of energy storage charging and discharging, and the cost of demand response satisfy the following relationship:

[0042] The cost of gas turbine power generation includes variable costs and fixed costs. , These are the variable cost coefficient and fixed cost coefficient for the gas turbine, respectively. , These represent the day-ahead power output of the gas turbine during time period t and its real-time power output adjustment. This is a variable representing the operating state of the gas turbine; a value of 1 indicates the turbine is running, and a value of 0 indicates it is shut down. The energy storage charging and discharging cost is calculated using the real-time charging and discharging power of the energy storage system. This is the energy storage charging and discharging cost coefficient. , These refer to the energy storage charging and discharging power during the real-time phase; demand response loads consider both reduceable and transferable loads. , These are the compensation unit prices for reduced or transferred-out load and transferred-in load, respectively. , These represent the load reduction amount that can be reduced during the daytime period t and its real-time adjustment amount. , These represent the amount of transferable load during the day-ahead period (time t) and its real-time adjustment. , These represent the amount of transferable load transferred in during the daytime period t and its real-time adjustment.

[0043] To prevent virtual power plants from purchasing large amounts of electricity in the day-ahead market and then selling it in large quantities in the real-time market, the fluctuation relationship between the real-time market purchase and sale prices and the day-ahead market prices is set to satisfy the following formula:

[0044] in, , These are the set lower and upper fluctuation coefficients, respectively.

[0045] To address the forecasting bias in day-ahead electricity prices, the forecasting error is assumed to follow a Gaussian mixture distribution. First, a large number of Monte Carlo random samples are generated based on this distribution. Second, the K-means algorithm is used to perform cluster analysis on the samples, extracting S typical electricity price scenarios. Finally, each cluster center is used as the representative electricity price for its corresponding scenario, and the proportion of samples within each cluster to the total number of samples is taken as the probability of that scenario occurring. .

[0046] Based on the above objective function and electricity price scenario, we further construct day-ahead stage constraints, real-time stage constraints, carbon market carbon quota trading and compliance constraints, and constraints for modeling the real-time stage wind and solar power output uncertainty using box uncertainty sets, which together form the day-ahead bidding optimization model.

[0047] The overall form of the aforementioned bid optimization model can be uniformly expressed as a two-stage robust optimization problem:

[0048] in, Let this be the vector of decision variables for the current stage. To adjust the variable vector in real time, For a given The feasible domain for real-time adjustment of variables. This is the set of actual power outputs of wind and solar power under uncertainties.

[0049] It should be noted that the unified form of the above objective function and the two-stage robust optimization problem enables the virtual power plant to quantify the coupling relationship between economic incentives and physical constraints under uncertain environments, providing a model basis for the iterative solution of subsequent column and constraint generation algorithms.

[0050] For example, the above-mentioned day-ahead bidding optimization model is deployed in a virtual power plant day-ahead bidding optimization decision-making device.

[0051] Based on the above steps, a complete day-ahead bidding optimization model was constructed by introducing electricity price scenario clustering to characterize the uncertainty of electricity price prediction and integrating composite carbon quota and box uncertainty set constraints. This model is able to cope with the dual uncertainties of electricity price and wind and solar power output, and provides structured input for the subsequent two-stage robust solution.

[0052] S203. A two-stage robust optimization algorithm is used to solve the day-ahead bidding optimization model, generating the day-ahead power purchase and sale plan of the virtual power plant and the real-time power adjustment strategy of each distributed energy source.

[0053] Among them, the two-stage robust optimization algorithm can ensure the feasibility of day-ahead decisions for the worst-case scenario under uncertain wind and solar power output by iteratively solving the main problem and sub-problems; the day-ahead power purchase and sale plan represents the power curve declared by the virtual power plant in the day-ahead market, while the real-time power adjustment strategy describes how each distributed energy source responds to the prediction deviation in the real-time stage.

[0054] In this embodiment, the Column and Constraint Generation (C&CG) algorithm is used to solve the above two-stage robust optimization problem. The model is decomposed into a main problem and sub-problems: the main problem solves the current-day decision variables and the upper bound of the objective function under a finite number of worst-case scenarios, and the sub-problems find the worst-case scenery scenario and update the lower bound of the objective function under a given current-day decision. The algorithm iterates until the upper and lower bounds converge to the preset accuracy.

[0055] Based on the above steps, the worst-case day-ahead decision is obtained by solving the master-sub-problem iteratively with minimal computational overhead, thereby improving the security and profitability robustness of the virtual power plant bidding scheme.

[0056] Based on the above technical solution, by embedding composite carbon quota constraints and box uncertainty set constraints into the day-ahead bidding optimization model and solving it with a two-stage robust optimization algorithm, the generated power plan not only reflects the physical and environmental value of clean energy, but also has the ability to resist real-time power output fluctuations, thereby improving scheduling safety and compliance while ensuring system power balance.

[0057] Example 3: In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 3 As shown, the composite carbon quota constraint based on the baseline quota and clean energy power generation incentive in S202 above can be specifically implemented through the following S301 to S304, which are explained in detail below: S301. Determine the benchmark carbon quota based on the power generation of the gas turbine and the carbon quota benchmark coefficient.

[0058] Among them, the benchmark carbon quota represents the initial carbon emission allowance obtained by the gas turbine according to the carbon emission benchmark. The carbon quota benchmark coefficient is usually issued based on the industry carbon emission benchmark and after being verified by the carbon emission authority.

[0059] In this embodiment of the application, the power output of the gas turbine during the day-ahead phase t time period is... Real-time output adjustment of gas turbine during the day-ahead period (t-period). and carbon quota benchmark coefficient The baseline carbon quota corresponding to the gas turbine was calculated. This benchmark carbon allowance reflects the initial carbon emission credits that gas turbines receive based on the carbon emission benchmark.

[0060] Optionally, the baseline carbon allowance satisfies the following formula:

[0061] in, As a benchmark carbon quota, This is the baseline coefficient for carbon quotas for gas turbines.

[0062] Based on the above steps, a quantitative mapping between the benchmark carbon quota for gas turbines and day-ahead output was established, providing a basic quota for the next step of constructing composite carbon quota constraints.

[0063] S302. Determine carbon allowance rewards based on the power generation and reward coefficient of wind turbines and photovoltaic units.

[0064] Among them, carbon allowance rewards represent the physical contribution of zero-carbon emission energy sources such as wind power and photovoltaics to carbon allowances, and the reward coefficient is determined by the carbon emission management department based on the environmental value of clean energy.

[0065] In this embodiment of the application, the actual output of the wind turbine is used. Actual output of photovoltaic units and their respective reward coefficients , Calculate the carbon allowance rewards for wind power and solar power separately, and sum the two to obtain the carbon allowance reward for clean energy power generation. .

[0066] Alternatively, the carbon allowance incentive can satisfy the following formula:

[0067] in, Carbon allowance rewards , V represents the incentive coefficients for wind power and solar power, respectively. , These represent the actual output of wind turbines and photovoltaic units, respectively.

[0068] It should be noted that wind power and solar power, as zero-carbon emission energy sources, can have their carbon quota rewards used to offset the actual carbon emissions generated by gas turbine power generation, thus quantifying the environmental value of clean energy.

[0069] Based on the above steps, the contribution of clean energy power generation to carbon quotas will be included in the calculation in the form of rewards, incentivizing virtual power plants to increase the proportion of wind and solar power output.

[0070] S303. Determine carbon emissions based on the power generation of the gas turbine and the carbon emission factor coefficient.

[0071] Among them, carbon emissions characterize the total amount of carbon dioxide produced during the gas turbine power generation process, while the carbon emission factor coefficient is usually determined based on the gas turbine fuel type and combustion efficiency, characterizing the carbon dioxide emission level corresponding to a unit of power generation.

[0072] In this embodiment of the application, the power output of the gas turbine during the day-ahead phase t time period is... Real-time output adjustment of gas turbine during the day-ahead period (t-period). With carbon emission factor coefficient The carbon emissions corresponding to the gas turbine were calculated. .

[0073] Alternatively, carbon emissions satisfy the following formula:

[0074] in, Carbon emissions from a virtual power plant during time period t under the electricity price scenario s. This represents the carbon emission factor coefficient for gas turbines.

[0075] Based on the above steps, the actual carbon emissions generated by gas turbine power generation were accurately quantified, providing an emission basis for determining compliance with composite carbon quota constraints.

[0076] S304. Determine composite carbon quota constraints based on benchmark carbon quotas, carbon quota rewards, and carbon emissions.

[0077] Among them, the composite carbon quota constraint represents the rules for virtual power plants to buy, sell and fulfill carbon quotas in the electricity carbon market, ensuring that the total amount of carbon quotas fully covers the actual carbon emissions at the end of the trading cycle.

[0078] In this embodiment, the total available carbon allowance for the virtual power plant in time period t is calculated based on the sum of the baseline carbon allowance and the carbon allowance bonus. The actual carbon emissions of the virtual power plant in time period t are calculated based on the carbon emission levels. And on this basis, a composite carbon quota constraint system that includes carbon quota trading constraints and compliance constraints will be established.

[0079] Optionally, the composite carbon quota constraint satisfies the following relationship:

[0080] Furthermore, the constraints on the quantity of carbon allowances purchased, the total amount sold, and the carbon compliance constraints for virtual power plants in each time period are as follows:

[0081] in, This refers to the carbon allowance for a virtual power plant during time period t under the electricity price scenario s. This is the set upper limit coefficient for the proportion of carbon quotas that can be purchased in a single time period.

[0082] It should be noted that carbon compliance constraints ensure that the total amount of carbon allowances held by virtual power plants at the end of the trading cycle can fully cover actual carbon emissions.

[0083] Based on the above steps, a complete carbon quota constraint system has been established that takes into account benchmark quotas, clean energy incentives, trading strategies, and compliance requirements, supporting compliant trading of virtual power plants in the combined electricity and carbon market.

[0084] Based on the above technical solution, by decomposing and modeling the benchmark carbon quota, clean energy carbon quota rewards and carbon emissions, and superimposing the upper limit of the purchase ratio and compliance constraints, a composite carbon quota constraint is formed, which enables virtual power plants to strictly control carbon emission compliance risks while taking into account the environmental value of clean energy.

[0085] Example 4: In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 4As shown, the box-shaped uncertainty set constraint constructed based on the uncertainty adjustment parameter in S202 above can be specifically implemented through the following S401 to S403: S401. Obtain the day-ahead forecast output, maximum fluctuation coefficient, and uncertainty adjustment parameters of wind turbines and photovoltaic units.

[0086] Among them, the day-ahead forecast output characterizes the power curve of wind and solar power under deterministic forecasting, the maximum fluctuation coefficient reflects the maximum relative deviation of wind and solar power output within a unit time period, and the uncertainty adjustment parameter is used to achieve a balance between conservatism and solution feasibility in the worst-case scenario.

[0087] In this embodiment of the application, the day-ahead forecast output of wind power for time period t is read from the meteorological forecasting system. Solar power output is predicted to be [missing information]. Read the maximum fluctuation coefficient of wind power output from the system parameter table. Maximum fluctuation coefficient of photovoltaic power output The uncertainty adjustment parameters are calculated inversely based on the confidence probabilities preset by the Lindeberg-Levy central limit causation. , These are used to control the level of conservatism for worst-case scenarios for wind power and solar power, respectively.

[0088] Based on the above steps, parameterized boundary conditions covering the random fluctuation range of wind power and photovoltaic output in real time were established.

[0089] S402. Based on the day-ahead predicted output, maximum fluctuation coefficient, and uncertainty adjustment parameters, determine the fluctuation boundary of the actual output in the real-time stage.

[0090] Among them, the fluctuation boundary is characterized by an analytical expression to describe the lower boundary value and time period constraint of the wind and solar power output in the uncertainty set.

[0091] In this embodiment, the day-ahead predicted output is adjusted downward according to the maximum fluctuation coefficient of wind power and photovoltaic power respectively, and the uncertainty adjustment parameter is used as the upper limit of the total number of time periods for taking the lower boundary value in time period t to determine the lower boundary set of wind power and photovoltaic power in real time.

[0092] Based on the above steps, the physical fluctuation range of wind and solar power output is expressed in the form of analytical boundaries, providing interval constraints for the construction of box-type uncertain sets.

[0093] S403. Generate box-type uncertainty set constraints based on wave boundary.

[0094] Among them, the box-type uncertainty set constraint embeds the fluctuation range of wind and solar power output as the feasible domain into the day-ahead bidding optimization model, ensuring that the day-ahead decision is still feasible when the actual wind and solar power output is within the given set in the real-time stage.

[0095] In this embodiment, the uncertainty set of actual wind and solar power output is jointly constructed according to the fluctuation boundary expressions of wind power and photovoltaic power, and is embedded as a constraint into the day-ahead bidding optimization model.

[0096] Optionally, the box-type uncertain set constraint satisfies the following formula:

[0097] in, This represents the set of uncertainties regarding the actual output of wind and solar power. , , These are the actual power output and their aggregates for wind power and solar power, respectively, considering uncertainties. , These are the maximum output fluctuation coefficients for wind turbines and photovoltaic units, respectively. and For state variables, a value of 1 indicates that the output drops to the lower boundary during time period t. and These are the set uncertainty adjustment parameters for wind power and photovoltaic power, respectively.

[0098] It should be noted that the uncertainty adjustment parameter and The value of is determined based on the confidence probability of the Lindeberg-Levy central limit reason, thereby achieving a balance between conservatism in the worst-case scenario and the feasibility of the solution.

[0099] Based on the above steps, a box-type uncertainty set constraint that can take into account both the random fluctuations in wind and solar power output and the controllability of the conservative degree of solution is constructed, so that the day-ahead decision has the feasibility guarantee within the physical boundary.

[0100] Example 5: In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 5 As shown, the above S203 can be specifically implemented through the following S501 to S503: S501. Decompose the day-ahead bidding optimization model into a main problem and sub-problems. The main problem uses the day-ahead power purchase and sale plan as the decision variable to solve for the upper bound of the objective function. The sub-problems find the worst-case scenario and update the lower bound of the objective function under the given day-ahead decision.

[0101] The main problem involves finding the upper bound of the day-ahead decision variables and the objective function on a finite set of candidate worst-case scenarios. The subproblem involves identifying the worst-case landscape scenario that minimizes the objective function given the day-ahead decision and returning the lower bound of the objective function.

[0102] In this embodiment of the application, the decision variables of the main problem are the day-ahead stage decision variable vector x, which consists of the day-ahead stage power purchase and sale plan, gas turbine output plan, energy storage charging and discharging plan, demand response plan, etc., and the objective function is the net revenue F of the virtual power plant; the subproblem is to find the wind and solar scenario that minimizes the objective function (i.e. the worst net revenue) given the day-ahead stage decision variable vector x and any possible worst-case wind and solar scenario p, and return the lower bound of the objective function.

[0103] S502. Based on the iterative solution results of the main problem and sub-problems, update the constraints corresponding to the worst-case landscape scenario.

[0104] In this process, updating the constraints corresponding to the worst-case scenario gradually brings the feasible domain of the main problem closer to the real worst-case scenario, thereby ensuring that the final output day-ahead decision is feasible under all uncertain scenarios.

[0105] In this embodiment, after each solution to the main problem, the real-time stage constraints corresponding to the worst-case scenario are added to the feasible region of the main problem, so that the main problem gradually approaches the optimal day-ahead decision under the real worst-case scenario; the sub-problems, based on the optimal day-ahead decision of the current main problem, solve in reverse the worst-case scenery scenario that minimizes the net profit.

[0106] S503, in response to the upper and lower bounds of the objective function satisfying the preset convergence accuracy, outputs the day-ahead power purchase and sale plan and the real-time power adjustment strategy.

[0107] Among them, the day-ahead power purchase and sale plan is used for day-ahead market declaration, and the real-time power adjustment strategy is used for real-time response to forecast deviations.

[0108] In this embodiment of the application, the specific solution steps include: Step a: Initialization, setting the initial landscape scene to the daytime predicted power output scene. Set the upper bound UB=+∞, the lower bound LB=-∞, the convergence accuracy ε=0.001, and the number of iterations k=0; Step b: Solve the main problem, in the current worst-case scenario set. , ,..., Solve the principal problem to obtain the optimal day-ahead decision. and the main problem objective value Update the upper boundary ; Step c: Solve the subproblems and convert the day-ahead decisions obtained from the main problem into the subproblems. Substituting the subproblem, we can obtain the worst-case landscape scenario. Objective value of subproblem Update the Nether ; Step d: Convergence judgment, if If the algorithm converges, it will output the optimal bidding plan for the current day. Otherwise, the new scenario Add the corresponding constraints to the main problem, let Return to step b.

[0109] It should be noted that after the iterative convergence of the main problem and subproblems, the output optimal day-ahead bidding scheme includes the power purchase and sale plan of the virtual power plant day-ahead market, the carbon quota plan for selling and buying, and the real-time power adjustment strategy of each distributed energy source under the worst-case scenario. This scheme mathematically guarantees the feasibility of real-time wind and solar fluctuations and electricity price deviations. The above-mentioned main-subproblem iterative framework is the column and constraint generation algorithm (C&CG), whose convergence speed depends on the "steepness" of the worst-case scenario and the rationality of the initial scenario selection.

[0110] Based on the above steps, the worst-case day-ahead decision is obtained by solving the principal-subproblem iteratively with minimal computational overhead, thereby improving the robustness of the bidding scheme in uncertain environments.

[0111] Example 6: In one possible implementation of this application, the day-ahead bidding optimization model further includes day-ahead stage constraints and real-time stage constraints; the day-ahead stage constraints include day-ahead power balance constraints, day-ahead market power purchase and sale plan upper and lower limit constraints, gas turbine day-ahead output constraints, energy storage charging and discharging power and state of charge day-ahead constraints, and demand response day-ahead constraints; the real-time stage constraints include real-time power balance constraints, gas turbine real-time output adjustment constraints, energy storage charging and discharging power and state of charge real-time constraints, demand response real-time adjustment constraints, and real-time market power purchase and sale constraints.

[0112] In this embodiment of the application, the specific constraint relationship is as follows: Current power balance constraints:

[0113] in, , The day-ahead forecasts for wind power and solar power are respectively provided. , These are the day-ahead energy storage charging and discharging power, respectively. This represents the base electrical load power of the virtual power plant.

[0114] Current market power purchase and sale plan upper and lower limits constraints:

[0115] in, This represents the upper limit of the day-ahead market power purchase and sale capacity of virtual power plants.

[0116] Gas turbine output constraints:

[0117] in, , These represent the minimum and maximum output of the gas turbine, respectively. This refers to the operating state variables of the gas turbine.

[0118] Energy storage charge / discharge power and day-ahead constraints on state of charge:

[0119] in, , These represent the state variables of energy storage charging and discharging during time period t. or A value of 1 indicates that the energy storage is charging or discharging during time period t, while a value of 0 indicates that the energy storage is in a static state during time period t, neither charging nor discharging. , These are the upper limits for energy storage charging and discharging power, respectively. , , These represent the initial energy storage capacity, minimum capacity, and maximum capacity, respectively. For charging and discharging efficiency, For time step.

[0120] Demand response day constraints:

[0121] in, The maximum response ratio for the set time period t. This is the maximum percentage of the total load that can be reduced.

[0122] Real-time power balance constraints:

[0123]

[0124] in, , These represent the actual output of wind power and solar power, respectively.

[0125] Constraints on real-time output adjustment of gas turbines:

[0126] in, , These are the lower and upper limits of the real-time output adjustment of the gas turbine, respectively.

[0127] Real-time constraints on energy storage charging and discharging power and state of charge:

[0128] Real-time adjustment constraints for demand response:

[0129] in, and These represent the lower and upper limits of the real-time adjustment amount for demand response, respectively.

[0130] Real-time market power consumption constraints:

[0131] in, This is the upper limit for the real-time market power supply.

[0132] Based on the above technical solution, by fully constraining the day-ahead and real-time stages, carbon market constraints, wind and solar uncertainty set constraints, and equipment physical operation boundaries are all incorporated into the day-ahead bidding optimization model. This enables the virtual power plant to solve for the optimal day-ahead bidding scheme that is robust to real-time fluctuations while satisfying physical and market constraints.

[0133] Example 7: The above primarily describes the solutions of the embodiments of this application from the perspective of device implementation. It is understood that each device, such as a virtual power plant carbon market day-ahead bidding device, includes at least one of the hardware structures and software modules corresponding to the execution of each function in order to achieve the above-mentioned functions. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0134] This application embodiment can divide the virtual power plant's day-ahead bidding device for the carbon market into functional units based on the above method example. For example, each function can be divided into separate functional units, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0135] When using integrated units, Figure 6A possible structural schematic diagram of the virtual power plant carbon market day-ahead bidding device (referred to as virtual power plant carbon market day-ahead bidding device 60) involved in the above embodiments is shown. The virtual power plant carbon market day-ahead bidding device 60 includes a processing unit 601 and a communication unit 602, and may also include a storage unit 603. Figure 6 The schematic diagram shown can be used to illustrate the structure of the virtual power plant carbon market day-ahead bidding device involved in the above embodiments.

[0136] when Figure 6 The schematic diagram shown illustrates the structure of the virtual power plant carbon market day-ahead bidding device involved in the above embodiments. The processing unit 601 is used to control and manage the operation of the virtual power plant carbon market day-ahead bidding device, the communication unit 602 is used for the virtual power plant carbon market day-ahead bidding device to communicate with other devices, and the storage unit 603 is used to store the program code and data of the virtual power plant carbon market day-ahead bidding device.

[0137] For example, the communication unit 602 is used to acquire the operating parameters, load forecast data, market electricity price signals, and carbon price signals of each distributed energy source in the virtual power plant; Processing unit 601 is used to determine the day-ahead bidding optimization model based on operating parameters, load forecast data, market electricity price signals, and carbon price signals. The day-ahead bidding optimization model includes a composite carbon quota constraint based on the benchmark quota and clean energy power generation incentives, as well as a box uncertainty set constraint constructed based on uncertainty adjustment parameters. A two-stage robust optimization algorithm is used to solve the day-ahead bidding optimization model to generate the day-ahead power purchase and sale plan of the virtual power plant and the real-time power adjustment strategy of each distributed energy source.

[0138] In one possible implementation, the processing unit 601 is further configured to determine a baseline carbon quota based on the power generation of the gas turbine and the carbon quota baseline coefficient; determine a carbon quota reward based on the power generation of the wind turbine and the reward coefficient; determine carbon emissions based on the power generation of the gas turbine and the carbon emission factor coefficient; and determine a composite carbon quota constraint based on the baseline carbon quota, the carbon quota reward, and the carbon emissions.

[0139] In one possible implementation, the composite carbon quota constraint satisfies the following relationship:

[0140] in, , These represent the carbon allowance and carbon emissions of a virtual power plant during time period t under the electricity price scenario s. This serves as the baseline coefficient for carbon quotas for gas turbines. For the gas turbine's output during the day-ahead period t, This refers to the real-time output adjustment of the gas turbine during the day-ahead period (t). , These are the incentive coefficients for wind turbines and photovoltaic units, respectively. , These are the actual outputs of wind turbines and photovoltaic units, respectively. This refers to the carbon emission factor coefficient of the gas turbine. This refers to the number of time periods within a bidding cycle.

[0141] In one possible implementation, the processing unit 601 is further configured to obtain the day-ahead predicted output, maximum fluctuation coefficient, and uncertainty adjustment parameters of the wind turbine and photovoltaic unit; determine the fluctuation boundary of the actual output in the real-time stage based on the day-ahead predicted output, maximum fluctuation coefficient, and uncertainty adjustment parameters; and generate a box-type uncertainty set constraint based on the fluctuation boundary.

[0142] In one possible implementation, the box-type uncertainty set constraint satisfies the following relationship:

[0143] in, This represents the set of uncertainties regarding the actual output of wind turbines and photovoltaic units. , , These are the actual power output and their combined output of wind turbines and photovoltaic units, respectively, considering uncertainties. , The day-ahead forecast power outputs for wind turbines and photovoltaic units are respectively. , These are the maximum power output fluctuation coefficients for wind turbines and photovoltaic units, respectively. and For state variables, a value of 1 indicates that the output drops to the lower boundary during time period t. and These are the set uncertainty adjustment parameters for wind turbines and photovoltaic units, respectively.

[0144] In one possible implementation, the processing unit 601 is further configured to decompose the day-ahead bidding optimization model into a main problem and sub-problems. The main problem solves for the upper bound of the objective function with the day-ahead power purchase and sale plan as the decision variable. The sub-problems find the worst-case scenario and update the lower bound of the objective function under a given day-ahead decision. Based on the iterative solution results of the main problem and sub-problems, the constraints corresponding to the worst-case scenario are updated. In response to the upper bound and lower bound of the objective function satisfying the preset convergence accuracy, the day-ahead power purchase and sale plan and the real-time power adjustment strategy are output.

[0145] In one possible implementation, the processing unit 601 is further configured to initialize the initial landscape scene as the day-ahead predicted power output scene, and set an initial upper bound, an initial lower bound, and a preset convergence precision; solve the main problem under the current worst-case scenario set to obtain the optimal day-ahead decision and the main problem objective value, and update the upper bound of the objective function based on the main problem objective value; substitute the optimal day-ahead decision into the subproblem to obtain the new worst-case landscape scene and the subproblem objective value, and update the lower bound of the objective function based on the subproblem objective value; determine that the algorithm has converged in response to the difference between the upper bound and the lower bound of the objective function satisfying the preset convergence precision; otherwise, add the constraints corresponding to the new worst-case landscape scene to the main problem, and return to the steps of solving the main problem.

[0146] In one possible implementation, the day-ahead bidding optimization model also includes day-ahead stage constraints and real-time stage constraints. The day-ahead stage constraints include: day-ahead power balance constraints, day-ahead market power purchase and sale plan upper and lower limit constraints, gas turbine day-ahead output constraints, energy storage charging and discharging power and state of charge day-ahead constraints, and demand response day-ahead constraints. The real-time stage constraints include: real-time power balance constraints, gas turbine real-time output adjustment constraints, energy storage charging and discharging power and state of charge real-time constraints, demand response real-time adjustment constraints, and real-time market power purchase and sale constraints.

[0147] The processing unit 601 can be a processor or a controller, and the communication unit 602 can be a communication interface, transceiver, transceiver circuit, transceiver device, etc. The term "communication interface" is a general term and may include one or more interfaces. The storage unit 603 can be a memory. When the virtual power plant carbon market day-ahead bidding device 60 is a chip, the processing unit 601 can be a processor or a controller, and the communication unit 602 can be an input interface and / or an output interface, pins, or circuits, etc. The storage unit 603 can be a storage unit within the chip (e.g., a register, cache, etc.) or a storage unit located outside the chip (e.g., read-only memory (ROM), random access memory (RAM, etc.)).

[0148] The communication unit can also be called a transceiver unit. The antenna and control circuit with transceiver functions in the virtual power plant carbon market day-ahead bidding device 60 can be considered as the communication unit 602 of the virtual power plant carbon market day-ahead bidding device 60, and the processor with processing functions can be considered as the processing unit 601 of the virtual power plant carbon market day-ahead bidding device 60. Optionally, the device in the communication unit 602 used to implement the receiving function can be considered as a communication unit, which is used to execute the receiving steps in the embodiments of this application. The communication unit can be a receiver, a receiver circuit, etc. The device in the communication unit 602 used to implement the transmitting function can be considered as a transmitting unit, which is used to execute the transmitting steps in the embodiments of this application. The transmitting unit can be a transmitter, a transmitter, a transmitting circuit, etc.

[0149] Figure 6 If the integrated units in the process are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. Storage media for storing computer software products include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0150] Figure 6 The units in the process can also be called modules; for example, a processing unit can be called a processing module.

[0151] This application also provides a hardware structure diagram of a virtual power plant carbon market day-ahead bidding device (denoted as virtual power plant carbon market day-ahead bidding device 70), see [link to diagram]. Figure 7 The virtual power plant carbon market bidding device 70 includes a processor 701, and optionally, a memory 702 connected to the processor 701.

[0152] In the first possible implementation, see Figure 7The virtual power plant carbon market day-ahead bidding device 70 also includes a transceiver 703. The processor 701, memory 702, and transceiver 703 are connected via a bus. The transceiver 703 is used to communicate with other devices or communication networks. Optionally, the transceiver 703 may include a transmitter and a receiver. The device in the transceiver 703 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of this application. The device in the transceiver 703 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of this application.

[0153] Based on the first possible implementation method Figure 7 The schematic diagram shown can be used to illustrate the structure of the virtual power plant carbon market day-ahead bidding device involved in the above embodiments.

[0154] in, Figure 7 This can also be illustrated by the system chip in the virtual power plant's day-ahead carbon market bidding device. In this case, the actions performed by the aforementioned virtual power plant's day-ahead carbon market bidding device can be implemented by this system chip. The specific actions performed can be found above and will not be repeated here.

[0155] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0156] The processor in this application may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., which are various computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a separate semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may be integrated with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits) to form a SoC (System-on-a-Chip), or it may be integrated as a built-in processor within an ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), PLDs (programmable logic devices), or logic circuits that implement dedicated logic operations.

[0157] The memory in the embodiments of this application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto.

[0158] This application also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0159] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0160] This application also provides a chip including a processor and an interface circuit. The interface circuit is coupled to the processor. The processor is used to run computer programs or instructions to implement the above-described method. The interface circuit is used to communicate with other modules outside the chip.

[0161] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0162] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0163] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A virtual power plant carbon market day-ahead bidding method, characterized in that, include: Acquire operating parameters, load forecast data, market electricity price signals, and carbon price signals for each distributed energy source within the virtual power plant; Based on the operating parameters, the load forecast data, the market electricity price signal, and the carbon price signal, a day-ahead bidding optimization model is determined. The day-ahead bidding optimization model includes a composite carbon quota constraint based on the benchmark quota and clean energy power generation incentive, as well as a box uncertainty set constraint constructed based on uncertainty adjustment parameters. A two-stage robust optimization algorithm is used to solve the day-ahead bidding optimization model, generating the day-ahead power purchase and sale plan of the virtual power plant and the real-time power adjustment strategy of each distributed energy source.

2. The method according to claim 1, characterized in that, The composite carbon quota constraint based on benchmark quotas and clean energy power generation incentives includes: The benchmark carbon quota is determined based on the power generation of the gas turbine and the carbon quota benchmark coefficient. Carbon allowance rewards are determined based on the power generation of wind turbines and photovoltaic units and the incentive coefficients. Carbon emissions are determined based on the power generation and carbon emission factor coefficient of the gas turbine. The composite carbon quota constraint is determined based on the baseline carbon quota, the carbon quota incentive, and the carbon emissions.

3. The method according to claim 2, characterized in that, The composite carbon quota constraint satisfies the following relationship: in, , These represent the carbon allowance and carbon emissions of a virtual power plant during time period t under the electricity price scenario s. This serves as the baseline coefficient for carbon quotas for gas turbines. For the gas turbine's output during the day-ahead phase t period, This refers to the real-time output adjustment of the gas turbine during the day-ahead period (t). , These are the incentive coefficients for wind turbines and photovoltaic units, respectively. , These are the actual outputs of wind turbines and photovoltaic units, respectively. This refers to the carbon emission factor coefficient of the gas turbine. This refers to the number of time periods within a bidding cycle.

4. The method according to claim 1, characterized in that, The box-shaped uncertainty set constraint constructed based on uncertainty adjustment parameters includes: Obtain the day-ahead predicted output, maximum fluctuation coefficient, and uncertainty adjustment parameters of wind turbines and photovoltaic units; Based on the day-ahead predicted output, the maximum fluctuation coefficient, and the uncertainty adjustment parameter, the fluctuation boundary of the actual output in the real-time stage is determined. Based on the fluctuation boundary, the box-shaped uncertainty set constraint is generated.

5. The method according to claim 4, characterized in that, The box-type uncertainty set constraint satisfies the following relationship: in, This represents the set of uncertainties regarding the actual output of wind turbines and photovoltaic units. , , These are the actual power output and their combined output of wind turbines and photovoltaic units, respectively, considering uncertainties. , The day-ahead forecast power outputs for wind turbines and photovoltaic units are respectively. , These are the maximum power output fluctuation coefficients for wind turbines and photovoltaic units, respectively. and For state variables, a value of 1 indicates that the output drops to the lower boundary during time period t. and These are the set uncertainty adjustment parameters for wind turbines and photovoltaic units, respectively.

6. The method according to claim 1, characterized in that, The method employs a two-stage robust optimization algorithm to solve the day-ahead bidding optimization model, generating the day-ahead power purchase and sale plan for the virtual power plant and the real-time power adjustment strategies for each distributed energy source, including: The day-ahead bidding optimization model is decomposed into a main problem and sub-problems. The main problem uses the day-ahead power purchase and sale plan as the decision variable to solve for the upper bound of the objective function. The sub-problems find the worst-case scenario and update the lower bound of the objective function under a given day-ahead decision. Based on the iterative solution results of the main problem and the sub-problems, the constraints corresponding to the worst-case landscape scenario are updated. In response to the upper and lower bounds of the objective function satisfying a preset convergence accuracy, the day-ahead power purchase and sale plan and the real-time power adjustment strategy are output.

7. The method according to claim 6, characterized in that, The step of updating the constraints corresponding to the worst-case landscape scenario based on the iterative solution results of the main problem and the sub-problems includes: The initial landscape scene is initialized as the day-ahead predicted power output scene, and the initial upper bound, initial lower bound, and preset convergence accuracy are set. Solve the main problem under the current worst-case scenario set to obtain the optimal day-ahead decision and the main problem objective value, and update the upper bound of the objective function based on the main problem objective value; Substitute the optimal day-ahead decision into the subproblem to solve for the new worst-case landscape scenario and the subproblem objective value, and update the lower bound of the objective function based on the subproblem objective value; If the difference between the upper bound and the lower bound of the objective function satisfies the preset convergence precision, the algorithm is determined to have converged; otherwise, the constraints corresponding to the new worst-case landscape scenario are added to the main problem, and the steps for solving the main problem are returned.

8. The method according to claim 1, characterized in that, The day-ahead bidding optimization model also includes day-ahead stage constraints and real-time stage constraints; The day-ahead stage constraints include: day-ahead power balance constraints, day-ahead market power purchase and sale plan upper and lower limits constraints, gas turbine day-ahead output constraints, energy storage charging and discharging power and state of charge day-ahead constraints, and demand response day-ahead constraints; The real-time stage constraints include: real-time power balance constraints, real-time output adjustment constraints of gas turbines, real-time constraints of energy storage charging and discharging power and state of charge, real-time adjustment constraints of demand response, and real-time market power sales and purchase constraints.

9. A virtual power plant carbon market day-ahead bidding device, characterized in that, The device includes: a communication unit and a processing unit; The communication unit is used to acquire the operating parameters, load forecast data, market electricity price signals, and carbon price signals of each distributed energy source in the virtual power plant. The processing unit is used to determine a day-ahead bidding optimization model based on the operating parameters, the load forecast data, the market electricity price signal, and the carbon price signal. The day-ahead bidding optimization model includes a composite carbon quota constraint based on the benchmark quota and clean energy power generation incentive, as well as a box uncertainty set constraint constructed based on uncertainty adjustment parameters. A two-stage robust optimization algorithm is used to solve the day-ahead bidding optimization model to generate the day-ahead power purchase and sale plan of the virtual power plant and the real-time power adjustment strategy of each distributed energy source.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a virtual power plant carbon market day-ahead bidding device, cause the virtual power plant carbon market day-ahead bidding device to perform the method as described in any one of claims 1-8.

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