Method, device and equipment for evaluating carbon reduction capability of virtual power plant, storage medium and program product

Abstract

The invention relates to a carbon reduction capability assessment method, device and equipment of a virtual power plant, a storage medium and a program product. According to the method, the cost information of a virtual power plant corresponding to an actual building is acquired, the first carbon emission information of the actual building before the virtual power plant is established is acquired, the second carbon emission information of the actual building after the virtual power plant is established is acquired, and the cost information, the first carbon emission information and the second carbon emission information of the virtual power plant are acquired; and inputting into a preset carbon reduction capability evaluation model to evaluate the carbon reduction capability of the virtual power plant to obtain an evaluation result. According to the method, when the carbon reduction capability of the virtual power plant is evaluated, the cost information and the carbon emission information are combined, and compared with an existing method that evaluation is performed only based on the carbon emission information, the evaluation method is beneficial to achieving economic cost minimization while pursuing a carbon reduction target, and then the accuracy of carbon reduction capability evaluation is improved.

Description

Technical Field

[0001] The present application relates to the technical field of electric energy systems, and in particular to a method, apparatus, equipment, storage medium, and program product for evaluating the carbon reduction capability of a virtual power plant. Background Art

[0002] As a new type of energy management system, virtual power plants in commercial buildings will become an effective means for commercial buildings to resolve the contradiction between scale growth and high carbon emissions. That is, virtual power plant resources can regulate the behavioral relationship between production capacity, energy storage, and energy consumption without affecting the operation of commercial buildings, reduce electricity demand during peak periods, and then achieve "peak shaving and valley filling" of building loads and energy conservation and carbon reduction through the combined application of renewable energy and energy storage technology.

[0003] At present, different types of carbon indicators are usually used to evaluate the carbon reduction capabilities of virtual power plants. However, the existing methods for evaluating the carbon reduction capabilities of virtual power plants have the problem of inaccurate evaluation. Summary of the Invention

[0004] Based on this, it is necessary to provide a virtual power plant carbon reduction capacity assessment method, device, equipment, storage medium and program product that can improve the accuracy of virtual power plant carbon reduction capacity assessment in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for evaluating the carbon reduction capability of a virtual power plant, the method comprising:

[0006] Obtaining cost information of a virtual power plant corresponding to an actual building, obtaining first carbon emission information of the actual building before establishing the virtual power plant, and obtaining second carbon emission information of the actual building after establishing the virtual power plant;

[0007] The cost information, the first carbon emission information, and the second carbon emission information of the virtual power plant are input into a preset carbon reduction capacity evaluation model to evaluate the carbon reduction capacity of the virtual power plant and obtain an evaluation result.

[0008] In some embodiments, obtaining cost information of a virtual power plant corresponding to an actual building includes:

[0009] Obtain the construction cost of the virtual power plant, the operating cost of the actual building, and the remaining residual value of the virtual power plant;

[0010] The construction cost, operating cost and residual value are input into the preset cost prediction model to predict the cost information of the virtual power plant and obtain the cost information of the virtual power plant corresponding to the actual building.

[0011] In some embodiments, obtaining the construction cost of the virtual power plant, the operating cost of the actual building, and the residual value of the virtual power plant includes:

[0012] Obtaining photovoltaic equipment information and energy storage equipment information in the actual building, and inputting the photovoltaic equipment information and energy storage equipment information into the first prediction model for prediction to obtain the construction cost of the virtual power plant;

[0013] Obtaining electricity transaction information of the actual building, and inputting the electricity transaction information into the second prediction model for prediction to obtain the actual building operation cost;

[0014] The residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment of the actual building are obtained, and the residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment are input into the third prediction model for prediction to obtain the remaining residual value of the virtual power plant.

[0015] In some embodiments, the electricity trading information of the actual building includes at least one of the unit price of purchased electricity of the actual building, the amount of purchased electricity of the actual building, the unit price of photovoltaic operating costs of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage discharging power of the actual building, the carbon trading unit price of the actual building, and the carbon emissions of the actual building.

[0016] In some embodiments, obtaining second carbon emission information of an actual building after establishing a virtual power plant includes:

[0017] Obtain the actual amount of purchased electricity for the building and the regional grid carbon emission factor for the building;

[0018] The regional power grid carbon emission factor and the amount of purchased electricity are input into a preset carbon emission prediction model to predict the carbon emissions of the actual building and obtain the second carbon emission information.

[0019] In some embodiments, obtaining the actual amount of purchased electricity for a building includes:

[0020] Obtain the load information of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage charging efficiency of the actual building, the energy storage discharge power of the actual building, and the energy storage discharge efficiency of the actual building;

[0021] The load information, photovoltaic output power, energy storage charging power, energy storage charging efficiency, energy storage discharge power and energy storage discharge efficiency are input into the power forecasting model for prediction to obtain the actual amount of purchased electricity for the building.

[0022] In a second aspect, the present application further provides a device for evaluating the carbon reduction capability of a virtual power plant, the device comprising:

[0023] An acquisition module, configured to acquire cost information of a virtual power plant corresponding to an actual building, acquire first carbon emission information of the actual building before establishing the virtual power plant, and acquire second carbon emission information of the actual building after establishing the virtual power plant;

[0024] The evaluation module is used to input the cost information, first carbon emission information and second carbon emission information of the virtual power plant into a preset carbon reduction capacity evaluation model to evaluate the carbon reduction capacity of the virtual power plant and obtain an evaluation result.

[0025] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0026] Obtaining cost information of a virtual power plant corresponding to an actual building, obtaining first carbon emission information of the actual building before establishing the virtual power plant, and obtaining second carbon emission information of the actual building after establishing the virtual power plant;

[0027] The cost information, the first carbon emission information, and the second carbon emission information of the virtual power plant are input into a preset carbon reduction capacity evaluation model to evaluate the carbon reduction capacity of the virtual power plant and obtain an evaluation result.

[0028] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0029] Obtaining cost information of a virtual power plant corresponding to an actual building, obtaining first carbon emission information of the actual building before establishing the virtual power plant, and obtaining second carbon emission information of the actual building after establishing the virtual power plant;

[0030] The cost information, the first carbon emission information, and the second carbon emission information of the virtual power plant are input into a preset carbon reduction capacity evaluation model to evaluate the carbon reduction capacity of the virtual power plant and obtain an evaluation result.

[0031] In a fifth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the following steps:

[0032] Obtaining cost information of a virtual power plant corresponding to an actual building, obtaining first carbon emission information of the actual building before establishing the virtual power plant, and obtaining second carbon emission information of the actual building after establishing the virtual power plant;

[0033] The cost information, the first carbon emission information, and the second carbon emission information of the virtual power plant are input into a preset carbon reduction capacity evaluation model to evaluate the carbon reduction capacity of the virtual power plant and obtain an evaluation result.

[0034] The aforementioned virtual power plant carbon reduction capacity assessment method, apparatus, equipment, storage medium, and program product obtains cost information of the virtual power plant corresponding to the actual building, first carbon emission information of the actual building before the virtual power plant is established, and second carbon emission information of the actual building after the virtual power plant is established. The method then inputs the cost information, first carbon emission information, and second carbon emission information of the virtual power plant into a pre-set carbon reduction capacity assessment model to assess the carbon reduction capacity of the virtual power plant and obtain an assessment result. This method combines cost information and carbon emission information when assessing the carbon reduction capacity of the virtual power plant. Compared to existing assessments based solely on carbon emission information, this method helps minimize economic costs while pursuing carbon reduction targets, thereby improving the accuracy of carbon reduction capacity assessments. Furthermore, by incorporating cost information into the assessment model, it is possible to screen out options with lower costs for the same carbon reduction effect, or options that can achieve greater carbon reduction at the same cost. This allows for a greater emphasis on cost-effectiveness when selecting virtual power plant construction options, avoiding the blind pursuit of high carbon reduction effects while ignoring economic costs, ultimately achieving a win-win situation for both economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a diagram of the internal structure of a computer device in some embodiments;

[0036] Figure 2 This is one of the flow charts of a method for evaluating the carbon reduction capability of a virtual power plant in some embodiments;

[0037] Figure 3 This is a second flow chart of a method for evaluating the carbon reduction capability of a virtual power plant in some embodiments;

[0038] Figure 4 This is a third flow chart of a method for evaluating the carbon reduction capability of a virtual power plant in some embodiments;

[0039] Figure 5 This is a fourth flow chart of a method for evaluating the carbon reduction capability of a virtual power plant in some embodiments;

[0040] Figure 6 This is a fifth flow chart of a method for evaluating the carbon reduction capability of a virtual power plant in some embodiments;

[0041] Figure 7 This is a structural block diagram of a device for evaluating the carbon reduction capability of a virtual power plant in some embodiments. DETAILED DESCRIPTION

[0042] In the embodiments of this application, the term "and / or" describes the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0043] In the embodiments of the present application, the term "plurality" refers to two or more than two, and other quantifiers are similar.

[0044] In the embodiments of the present application, the term "at least one" means one or more. For example, at least one of A, B and C can mean the following six situations: A exists alone, B exists alone, C exists alone, A and B exist at the same time, A and C exist at the same time, B and C exist at the same time, and A, B and C exist at the same time.

[0045] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0046] As a new energy management system, commercial building virtual power plants (VPPs) will become an effective means of resolving the conflict between scale growth and high carbon emissions in commercial buildings. Virtual power plant resources can regulate the relationship between production capacity, storage, and energy consumption without impacting commercial building operations, reducing electricity demand during peak periods. By combining renewable energy and energy storage technologies, they can achieve peak load shaving and valley filling, thereby reducing energy consumption and carbon emissions. Currently, the carbon reduction capabilities of VPPs are typically assessed using various carbon indicators. However, existing VPP assessment methods suffer from inaccuracies.

[0047] In view of this, the embodiments of the present application propose a method, device, equipment, storage medium and program product for evaluating the carbon reduction capability of a virtual power plant, which can improve the accuracy of carbon reduction capability evaluation by combining cost information and carbon emission information.

[0048] It should be noted that the beneficial effects or technical problems solved by the embodiments of the present application are not limited to this one, but may also include other implicit or related problems. For details, please refer to the description of the following embodiments.

[0049] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0050] In some embodiments, the carbon reduction capability assessment method of a virtual power plant provided in the embodiments of the present application can be applied to Figure 1 In the computer device shown, the computer device can be a power dispatching device inside the virtual power plant, or a power dispatching device outside and connected to the virtual power plant. The computer device can be a terminal or a server, and its internal structure can be as shown in FIG. Figure 1 As shown, the computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, while the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication, which can be achieved via Wi-Fi, mobile cellular networks, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for assessing the carbon reduction capabilities of a virtual power plant. The display unit of the computer device is used to produce visual images and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0051] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0052] In some embodiments, as Figure 2 As shown in the figure, a method for evaluating the carbon reduction capability of a virtual power plant is provided. Figure 1The computer device in the example is used to illustrate the process, including the following steps:

[0053] S201, obtaining cost information of a virtual power plant corresponding to an actual building, obtaining first carbon emission information of the actual building before establishing the virtual power plant, and obtaining second carbon emission information of the actual building after establishing the virtual power plant.

[0054] The actual building can be a commercial or public building. The cost information for a virtual power plant includes the construction cost of the virtual power plant, the operating cost of the actual building, and the residual value of the virtual power plant. The construction cost represents the initial investment in building the virtual power plant, the operating cost represents the ongoing expenses incurred during the operation of the virtual power plant, and the residual value represents the economic value obtained from recycling the power equipment in the virtual power plant after it reaches the end of its service life. The first carbon emission information is the carbon emission information of the actual building before the virtual power plant is established, and the second carbon emission information is the carbon emission information of the actual building after the virtual power plant is established.

[0055] In an embodiment of the present application, the computer device may obtain the cost information of the virtual power plant corresponding to the actual building, obtain the first carbon emission information of the actual building before the establishment of the virtual power plant, and obtain the second carbon emission information of the actual building after the establishment of the virtual power plant by means of web crawling or manual collection, and subsequently evaluate the carbon reduction capability of the virtual power plant based on the cost information, the first carbon emission information, and the second carbon emission information. Optionally, after obtaining the cost information, the first carbon emission information, and the second carbon emission information, the computer device may normalize and standardize the cost information, the first carbon emission information, and the second carbon emission information to obtain the processed cost information, the processed first carbon emission information, and the processed second carbon emission information, and subsequently evaluate the carbon reduction capability of the virtual power plant based on the processed cost information, the processed first carbon emission information, and the processed second carbon emission information.

[0056] S202: Input the cost information, the first carbon emission information, and the second carbon emission information of the virtual power plant into a preset carbon reduction capability evaluation model to evaluate the carbon reduction capability of the virtual power plant and obtain an evaluation result.

[0057] The carbon reduction capability assessment model can be a neural network model, specifically a convolutional neural network or a fully connected neural network, a machine learning model, or a mathematical relational model. The assessment result may include whether the virtual power plant has a strong carbon reduction capability or a weak carbon reduction capability. Optionally, the assessment result may also include a pass or fail.

[0058] In an embodiment of the present application, when the computer device obtains the cost information, first carbon emission information, and second carbon emission information of the virtual power plant based on the above steps, the cost information, first carbon emission information, and second carbon emission information of the virtual power plant can be input into a preset carbon reduction capability assessment model, and the carbon reduction capability of the virtual power plant can be assessed using the carbon reduction capability assessment model to obtain an assessment result. Optionally, the computer device can assess the carbon reduction capability of the virtual power plant using the carbon reduction capability assessment model to obtain an assessment value, and then determine the assessment result based on the assessment value and the expected value; for example, if the assessment value is less than the expected value, the assessment result can be determined to be a failure or the virtual power plant's carbon reduction capability is weak; if the assessment value is not less than the expected value, the assessment result can be determined to be a success or the virtual power plant's carbon reduction capability is strong.

[0059] It should be noted that the computer device can pre-build an initial carbon reduction capacity assessment model and then train the initial carbon reduction capacity assessment model based on the virtual power plant's cost information samples, first carbon emission information samples, and second carbon emission information samples to obtain a preset carbon reduction capacity assessment model. Specifically, the computer device can collect virtual power plant cost data corresponding to different actual buildings, including construction costs (such as hardware equipment procurement, software system development, and site rental costs), operating costs (such as energy management system maintenance, personnel training, and equipment repair costs), and residual values. First carbon emission information data for the actual building before the virtual power plant is established, covering carbon emissions generated by the building's energy consumption (such as electricity, natural gas, and coal), and second carbon emission information data for the actual building after the virtual power plant is established, can be obtained through energy metering equipment, carbon emission monitoring systems, and other channels. Optionally, other factors that may affect carbon reduction effectiveness can also be collected, such as building type (residential, commercial, industrial, etc.), building location, climate conditions, and energy usage patterns. After collecting the cost data and carbon emission information data, the computer device can pre-process the cost data and carbon emission information data, specifically removing noise, outliers, and missing values. For example, abnormally high or low values in cost data can be checked and corrected. Missing values in carbon emission data can be filled using interpolation or averaging. Different types of data are then normalized to the same scale, for example, using Z-score normalization. Finally, the preprocessed data is divided into training, validation, and test sets in a certain ratio, for example, 70%:15%:15%. Computer equipment can construct an initial carbon reduction capacity assessment model based on an appropriate machine learning or deep learning model. For example, the initial carbon reduction capacity assessment model can be constructed based on a linear regression model or a neural network model. Then, using a function such as mean squared error (MSE) or mean absolute error (MAE) as a loss function, an optimization algorithm such as stochastic gradient descent (SGD) or adaptive moment estimation (Adam) is used to minimize the loss function. Finally, the model is trained using the training set data. The model parameters can be continuously adjusted to minimize the loss function, resulting in a trained initial carbon reduction capacity assessment model. Furthermore, the trained initial carbon reduction capacity assessment model can be validated using the mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²) methods based on the validation set data to evaluate the model's performance. Based on the validation results, the model's hyperparameters (such as the learning rate, number of hidden layer neurons, and tree depth) can be adjusted to improve model performance. Finally, the tuned model can be tested using the test set data to evaluate its generalization ability. If the test results meet the requirements, the trained initial carbon reduction capacity assessment model will be determined as the preset carbon reduction capacity assessment model for use in the aforementioned application.

[0060] Optionally, a preset carbon reduction capacity assessment model can be constructed using the following relationship:

[0061]

[0062]

[0063] in, The reduction in carbon emissions before and after the construction of a virtual power plant for an actual building, To establish the first carbon emission information of the actual building before the virtual power plant, To establish the second carbon emission information of the actual building after the virtual power plant, Y is the carbon reduction potential efficiency of the virtual power plant corresponding to the actual building, and F is the cost information of the virtual power plant corresponding to the actual building.

[0064] The carbon reduction capacity assessment method for a virtual power plant provided in an embodiment of the present application obtains cost information of the virtual power plant corresponding to the actual building, obtains first carbon emission information of the actual building before the virtual power plant is established, and obtains second carbon emission information of the actual building after the virtual power plant is established. The cost information, first carbon emission information, and second carbon emission information of the virtual power plant are input into a preset carbon reduction capacity assessment model to evaluate the carbon reduction capacity of the virtual power plant and obtain an assessment result. The above method combines cost information and carbon emission information when evaluating the carbon reduction capacity of the virtual power plant. Compared with the existing assessment based only on carbon emission information, the above assessment method helps to minimize economic costs while pursuing carbon reduction goals, thereby improving the accuracy of carbon reduction capacity assessment. Moreover, by incorporating cost information into the assessment model, it is possible to screen out those solutions with lower costs under the same carbon reduction effect, or solutions that can achieve greater carbon reduction at the same cost input. This can enable the subsequent selection of virtual power plant construction plans to pay more attention to cost-effectiveness, avoid blindly pursuing high carbon reduction effects while ignoring economic costs, and ultimately achieve a win-win situation for economic and environmental benefits.

[0065] In some embodiments, a specific implementation method for obtaining cost information of a virtual power plant corresponding to an actual building is also provided, such as Figure 3 As shown, the "obtaining cost information of the virtual power plant corresponding to the actual building" in the above S201 includes:

[0066] S301, obtaining the construction cost of the virtual power plant, the operating cost of the actual building and the residual value of the virtual power plant.

[0067] Among them, the cost information of the virtual power plant corresponding to the actual building includes the construction cost of the virtual power plant, the operating cost of the actual building and the residual value of the virtual power plant.

[0068] In an embodiment of the present application, a computer device may obtain the construction cost of a virtual power plant, the operating cost of an actual building, and the residual value of a virtual power plant by crawling the web or manually collecting data.

[0069] Optional, such as Figure 4 As shown, the above-mentioned "obtaining the construction cost of the virtual power plant, the operating cost of the actual building and the residual value of the virtual power plant" in S302 includes:

[0070] S3011, obtaining photovoltaic equipment information and energy storage equipment information in the actual building, and inputting the photovoltaic equipment information and energy storage equipment information into the first prediction model for prediction to obtain the construction cost of the virtual power plant.

[0071] The photovoltaic equipment information in the actual building includes the initial investment per unit area of the photovoltaic equipment, the area covered by the photovoltaic equipment, the initial investment per unit capacity of the energy storage equipment in the actual building, and the energy storage equipment capacity. The first prediction model can be a neural network model, specifically a convolutional neural network, a fully connected neural network, a machine learning model, or a mathematical relational model.

[0072] In an embodiment of the present application, the computer device can obtain the photovoltaic equipment information and energy storage equipment information in the actual building, and then input the photovoltaic equipment information and energy storage equipment information into the first prediction model, perform cost prediction through the first prediction model, and obtain the construction cost of the virtual power plant. It should be noted that the computer device can pre-construct a first initial prediction model based on a neural network or a machine learning algorithm. After the first initial prediction model is constructed, the photovoltaic equipment sample data and the energy storage equipment sample data can be input into the first initial prediction model for training to obtain a first prediction result, and then the training loss is determined based on the first prediction result, the photovoltaic equipment sample labeling information, and the energy storage equipment sample labeling information, and the parameters of the first initial prediction model are adjusted according to the training loss until the training loss reaches a preset training condition, for example, the preset training condition includes that the value of the training loss is less than a preset loss threshold, or the training loss converges. The first initial prediction model obtained after the parameters are finally adjusted is used as the first prediction model.

[0073] Optionally, the first prediction model can be constructed by the following relationship:

[0074]

[0075] in, is the construction cost of the virtual power plant, is the initial investment per unit area of photovoltaic equipment in actual buildings, is the coverage area of photovoltaic equipment, is the initial investment per unit capacity of energy storage equipment in actual buildings, is the capacity of the energy storage device.

[0076] S3012: Obtain the power transaction information of the actual building, and input the power transaction information into the second prediction model for prediction to obtain the operating cost of the actual building.

[0077] The actual building's electricity trading information includes at least one of the actual building's purchased electricity unit price, the actual building's purchased electricity volume, the actual building's photovoltaic operating cost unit price, the virtual power plant's photovoltaic output power, the actual building's energy storage charging power, the actual building's energy storage discharging power, the actual building's carbon trading unit price, and the actual building's carbon emissions. The second prediction model can be a neural network model, specifically a convolutional neural network, a fully connected neural network, a machine learning model, or a mathematical relational model.

[0078] In an embodiment of the present application, the computer device can obtain the electricity trading information of the actual building, and then input the electricity trading information of the actual building into the second prediction model, perform cost prediction through the second prediction model, and obtain the operating cost of the actual building. It should be noted that the computer device can pre-construct a second initial prediction model based on a neural network or a machine learning algorithm. After the second initial prediction model is constructed, the sample data of the electricity trading information of the actual building can be input into the second initial prediction model for training to obtain a second prediction result, and then the training loss is determined based on the second prediction result and the annotation information of the electricity trading information sample, and the parameters of the second initial prediction model are adjusted according to the training loss until the training loss reaches the preset training conditions, for example, the preset training conditions include the value of the training loss being less than the preset loss threshold, or the training loss converges. The second initial prediction model obtained after the parameters are finally adjusted is used as the second prediction model.

[0079] Optionally, the second prediction model can be constructed using the following relationship:

[0080]

[0081] in, is the actual building operating cost, is the actual amount of purchased electricity of the building at time t, is the unit price of purchased electricity for the actual building, is the unit price of photovoltaic operation cost of the actual building, is the photovoltaic output power of the virtual power plant at time t, K is the charging and discharging cost coefficient of the energy storage system, is the energy storage charging power of the actual building at time t, is the energy storage discharge power of the actual building at time t, is the carbon trading unit price of the actual building at time t (i.e. the unit price of the actual building participating in carbon trading), To establish the second carbon emission information of the actual building after the virtual power plant, t represents the specific usage time and T represents the interval of t.

[0082] S3013, obtaining the residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment of the actual building, and inputting the residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment into the third prediction model for prediction to obtain the remaining residual value of the virtual power plant.

[0083] Among them, the third prediction model can be a neural network model, specifically a convolutional neural network or a fully connected neural network, or a machine learning model, or a mathematical relationship model.

[0084] In an embodiment of the present application, the computer device can determine the residual value information of the photovoltaic equipment of the actual building based on the residual value rate of the photovoltaic equipment in the actual building, the initial investment amount per unit area of the photovoltaic equipment, and the coverage area of the photovoltaic equipment, and determine the residual value information of the energy storage equipment based on the residual value rate of the energy storage equipment in the actual building, the initial investment amount per unit capacity of the energy storage equipment, and the capacity of the energy storage equipment. When the computer device obtains the residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment of the actual building, it can input the residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment into the third prediction model, and perform prediction through the third prediction model to obtain the remaining residual value of the virtual power plant. It should be noted that the computer device can construct a third initial prediction model in advance based on a neural network or a machine learning algorithm. After the third initial prediction model is constructed, sample data of the photovoltaic device residual value information and sample data of the energy storage device residual value information can be input into the third initial prediction model for training to obtain a third prediction result. A training loss is then determined based on the third prediction result, the sample labeling information of the photovoltaic device residual value information, and the sample labeling information of the energy storage device residual value information. The parameters of the third initial prediction model are adjusted according to the training loss until the training loss meets a preset training condition. For example, the preset training condition includes the training loss value being less than a preset loss threshold or the training loss converges. The third initial prediction model obtained after adjusting the parameters is used as the third prediction model.

[0085] Optionally, the third prediction model can be constructed by the following relationship:

[0086]

[0087] in, is the residual value of the virtual power plant, is the residual value rate of photovoltaic equipment in actual buildings, is the initial investment per unit area of photovoltaic equipment in actual buildings, is the coverage area of photovoltaic equipment, is the residual value rate of energy storage equipment in actual buildings, is the initial investment per unit capacity of energy storage equipment in actual buildings, is the capacity of the energy storage device.

[0088] S302: Input the construction cost, operation cost and residual value into a preset cost prediction model to predict the cost information of the virtual power plant, and obtain the cost information of the virtual power plant corresponding to the actual building.

[0089] Among them, the preset cost prediction model can be a neural network model, specifically a convolutional neural network or a fully connected neural network, or a machine learning model, or a mathematical relationship model.

[0090] In an embodiment of the present application, after the computer device obtains the construction cost of the virtual power plant, the operating cost of the actual building, and the residual value of the virtual power plant based on the above steps, the construction cost, operating cost, and residual value can be input into a preset cost prediction model, and the cost information of the virtual power plant can be predicted by the cost prediction model to obtain the cost information of the virtual power plant. It should be noted that the computer device can pre-construct an initial cost prediction model based on a neural network or a machine learning algorithm. After the initial cost prediction model is constructed, the construction cost sample data, the operating cost sample data, and the residual value sample data can be input into the initial cost prediction model for training to obtain a prediction result, and then the training loss is determined based on the prediction result, the construction cost sample labeling information, the operating cost labeling information, and the residual value sample labeling information, and the parameters of the initial cost prediction model are adjusted according to the training loss until the training loss reaches the preset training condition, for example, the preset training condition includes that the value of the training loss is less than the preset loss threshold, or the training loss converges. The initial cost prediction model obtained after adjusting the parameters is used as the preset cost prediction model.

[0091] Optionally, a preset cost prediction model can be constructed using the following relationship:

[0092]

[0093] in, is the cost information of the virtual power plant corresponding to the actual building, is the construction cost of the virtual power plant, is the actual building operating cost, is the residual value of the virtual power plant.

[0094] The method described in the embodiments of the present application predicts the cost of the entire life cycle from the construction, operation to the dismantling of the virtual power plant, which can improve the accuracy of the prediction.

[0095] In some embodiments, a specific implementation method for obtaining the first carbon emission information of the actual building after the virtual power plant is established is also provided, such as Figure 5 As shown, the "obtaining the second carbon emission information of the actual building after the virtual power plant is established" in the above S201 includes:

[0096] S401, obtaining the actual amount of purchased electricity of the building and the regional power grid carbon emission factor of the building.

[0097] The purchased electricity amount of the actual building refers to the electricity consumed by the actual building purchased from the external power system.

[0098] In the embodiment of the present application, the computer device can obtain the actual building's purchased electricity and the actual building's regional power grid carbon emission factor by web crawling or manual collection.

[0099] Optional, such as Figure 6 As shown, the "obtaining the actual amount of purchased electricity of the building and the actual regional power grid carbon emission factor of the building" in the above S401 includes:

[0100] S4011, obtaining the load information of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage charging efficiency of the actual building, the energy storage discharging power of the actual building, and the energy storage discharging efficiency of the actual building.

[0101] In an embodiment of the present application, the computer device can obtain the load information of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage charging efficiency of the actual building, the energy storage discharge power of the actual building and the energy storage discharge efficiency of the actual building by means of network crawling or manual collection.

[0102] S4012: Input load information, photovoltaic output power, energy storage charging power, energy storage charging efficiency, energy storage discharging power and energy storage discharging efficiency into the power prediction model for prediction to obtain the actual amount of purchased electricity for the building.

[0103] Among them, the power prediction model can be a neural network model, specifically a convolutional neural network or a fully connected neural network, or a machine learning model, or a mathematical relationship model.

[0104] In an embodiment of the present application, the computer device inputs the load information, photovoltaic output power, energy storage charging power, energy storage charging efficiency, energy storage discharge power and energy storage discharge efficiency into the power prediction model for prediction, and obtains the actual amount of purchased electricity of the building. It should be noted that the computer device can pre-construct an initial power prediction model based on a neural network or a machine learning algorithm. After the initial power prediction model is constructed, the load information sample data and the energy storage device sample data can be input into the initial power prediction model for training to obtain the power prediction result, and then the training loss is determined based on the power prediction result and the load information sample labeling information, and the parameters of the initial power prediction model are adjusted according to the training loss until the training loss reaches the preset training condition, for example, the preset training condition includes that the value of the training loss is less than the preset loss threshold, or the training loss converges. The initial power prediction model obtained after the parameters are finally adjusted is used as the power prediction model.

[0105] Optionally, the power consumption prediction model can be constructed using the following relationship:

[0106]

[0107] in, is the load information of the actual building at time t, is the photovoltaic output power of the virtual power plant at time t, is the energy storage charging power of the actual building at time t; is the energy storage charging efficiency of the actual building at time t, is the energy storage discharge power of the actual building, is the energy storage and discharge efficiency of the actual building, is the actual amount of purchased electricity of the building at time t.

[0108] S402: Inputting the regional power grid carbon emission factor and the amount of purchased electricity into a preset carbon emission prediction model to perform carbon emission prediction of the actual building, thereby obtaining second carbon emission information.

[0109] Among them, the carbon emission prediction model can be a neural network model, specifically a convolutional neural network or a fully connected neural network, or a machine learning model, or a mathematical relationship model.

[0110] In an embodiment of the present application, after the computer device obtains the amount of purchased electricity of the actual building and the regional power grid carbon emission factor of the actual building based on the above steps, the regional power grid carbon emission factor and the amount of purchased electricity can be input into a preset carbon emission prediction model to predict the carbon emissions of the actual building and obtain second carbon emission information. It should be noted that the computer device can pre-construct an initial carbon emission prediction model based on a neural network or a machine learning algorithm. After the initial carbon emission prediction model is constructed, the carbon emission sample data can be input into the initial carbon emission prediction model for training to obtain a carbon emission prediction result, and then the training loss is determined based on the carbon emission prediction result and the carbon emission sample annotation information, and the parameters of the initial carbon emission prediction model are adjusted according to the training loss until the training loss reaches a preset training condition, for example, the preset training condition includes that the value of the training loss is less than a preset loss threshold, or the training loss converges. The initial carbon emission prediction model obtained after the parameters are finally adjusted is used as the preset carbon emission prediction model.

[0111] Optionally, a preset carbon emission prediction model can be constructed using the following relationship:

[0112]

[0113] in, To establish the second carbon emission information of the actual building after the virtual power plant, is the regional grid carbon emission factor of the actual building, is the actual amount of purchased electricity of the building at time t.

[0114] In summary of all the above embodiments, a method for evaluating the carbon reduction capability of a virtual power plant is further provided, the method comprising:

[0115] S501, obtaining photovoltaic equipment information and energy storage equipment information in an actual building, and inputting the photovoltaic equipment information and energy storage equipment information into a first prediction model for prediction to obtain the construction cost of the virtual power plant.

[0116] S502: Obtain power transaction information for the actual building and input the power transaction information into a second prediction model for prediction to obtain the actual building's operating cost. The power transaction information for the actual building includes at least one of the actual building's purchased electricity unit price, the actual building's purchased electricity volume, the actual building's photovoltaic operating cost unit price, the virtual power plant's photovoltaic output power, the actual building's energy storage charging power, the actual building's energy storage discharging power, the actual building's carbon transaction unit price, and the actual building's carbon emissions.

[0117] S503, obtaining the residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment of the actual building, and inputting the residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment into the third prediction model for prediction to obtain the residual value of the virtual power plant.

[0118] S504: Input the construction cost, operation cost and residual value into a preset cost prediction model to predict the cost information of the virtual power plant, and obtain the cost information of the virtual power plant corresponding to the actual building.

[0119] S505, obtaining the regional power grid carbon emission factor of the actual building.

[0120] S506, obtaining the load information of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage charging efficiency of the actual building, the energy storage discharging power of the actual building, and the energy storage discharging efficiency of the actual building.

[0121] S507: Input the load information, photovoltaic output power, energy storage charging power, energy storage charging efficiency, energy storage discharging power and energy storage discharging efficiency into the power prediction model for prediction to obtain the actual purchased electricity of the building.

[0122] S508: Input the regional power grid carbon emission factor and the amount of purchased electricity into a preset carbon emission prediction model to predict the carbon emissions of the actual building, and obtain second carbon emission information of the actual building after the virtual power plant is established.

[0123] S509: Acquire first carbon emission information of actual buildings before establishing the virtual power plant.

[0124] S510: Inputting the cost information, the first carbon emission information and the second carbon emission information of the virtual power plant into a preset carbon reduction capability evaluation model to evaluate the carbon reduction capability of the virtual power plant and obtain an evaluation result.

[0125] In the embodiments of the present application, the virtual power plant resources of actual buildings (such as commercial buildings) are utilized to fine-tune the energy of commercial buildings, and the carbon trading mechanism is introduced into the operating costs of commercial buildings. While considering the optimal economic efficiency of virtual power plant resources, the carbon emissions of commercial buildings are guaranteed to be minimized, the carbon reduction potential of commercial buildings is explored, and ultimately low-carbon and low-cost operation of commercial buildings is achieved.

[0126] 1) Effective management of the benefits and costs of commercial building virtual power plants ensures commercial buildings' dominant position in participating in virtual power plant energy management and is key to maintaining their competitiveness in the carbon-electricity market. First, the full lifecycle cost calculation formula for commercial building virtual power plants, from construction, operation, to dismantling, is as follows:

[0127]

[0128]

[0129]

[0130]

[0131] in, is the cost information of the virtual power plant corresponding to the actual building, is the construction cost of the virtual power plant, is the actual building operating cost, is the residual value of the virtual power plant, is the initial investment per unit area of photovoltaic equipment in actual buildings, is the coverage area of photovoltaic equipment, is the initial investment per unit capacity of energy storage equipment in actual buildings, is the capacity of the energy storage device, is the actual building operating cost, is the actual amount of purchased electricity of the building at time t, is the unit price of purchased electricity for the actual building, is the unit price of photovoltaic operation cost of the actual building, is the photovoltaic output power of the virtual power plant at time t, K is the charging and discharging cost coefficient of the energy storage system, is the energy storage charging power of the actual building at time t, is the energy storage discharge power of the actual building at time t, is the carbon trading unit price of the actual building at time t (i.e. the unit price of the actual building participating in carbon trading), To establish the second carbon emission information of the actual building after the virtual power plant, t represents the specific usage time, T represents the interval of t, is the residual value rate of photovoltaic equipment in actual buildings.

[0132] 2) As virtual power plants manage and dispatch energy, the carbon emissions of commercial buildings will also change. Based on the carbon emissions of commercial building virtual power plants, the objective function of minimizing carbon emissions of commercial buildings is established. The calculation formula is as follows:

[0133]

[0134]

[0135] in, Carbon emissions from commercial buildings for virtual power plants, is the regional power grid carbon emission factor, The amount of electricity purchased by commercial buildings at time t, is the load information of the actual building at time t, is the photovoltaic output power of the virtual power plant at time t, is the energy storage charging power of the actual building at time t; is the energy storage charging efficiency of the actual building at time t, is the energy storage discharge power of the actual building, is the energy storage discharge efficiency of the actual building.

[0136] 3) Based on the economic feasibility and carbon emissions of virtual power plant construction, the carbon reduction potential of commercial buildings is evaluated. The specific calculation formula is:

[0137]

[0138]

[0139] in, The reduction in carbon emissions before and after the construction of a virtual power plant for an actual building, To establish the first carbon emission information of the actual building before the virtual power plant, To establish the second carbon emission information of the actual building after the virtual power plant, Y is the carbon reduction potential efficiency of the virtual power plant corresponding to the actual building. The higher Y, the better. F is the cost information of the virtual power plant corresponding to the actual building.

[0140] The method described in the embodiment of the present application utilizes the resources of a virtual power plant in a commercial building to fine-tune the energy of a commercial building, introduces a carbon trading mechanism into the operating costs of commercial buildings, and while considering the optimal economic efficiency of virtual power plant resources, ensures that the carbon emissions of commercial buildings are minimized, taps into the carbon reduction potential of commercial buildings, and ultimately achieves low-carbon and low-cost operation of commercial buildings. The embodiment of the present application combines the full life cycle cost of virtual power plant construction and evaluates the carbon reduction potential of virtual power plants from the perspective of commercial buildings, in order to tap into the carbon reduction potential of demand-side buildings, and ultimately achieve a win-win optimization of building operating costs and carbon reduction targets. The utilization of virtual power plant resources helps to find a solution to the high carbon emissions of commercial buildings. In order to accurately evaluate the economic feasibility and carbon-saving capabilities of the construction of virtual power plants for commercial buildings, a method for evaluating the carbon reduction potential of virtual power plants in commercial buildings based on the scenario of commercial buildings participating in carbon trading is proposed. A carbon emission measurement model for commercial buildings participating in virtual power plants is established, taking into account economic evaluation indicators such as the initial investment cost of virtual power plant resources, the operating costs of commercial buildings, and the income from participation of commercial buildings of virtual power plants in electricity and carbon market transactions. The economic feasibility of virtual power plants is evaluated, and an assessment indicator for the carbon reduction potential of virtual power plants is proposed to provide a scientific basis for improving energy efficiency and reducing greenhouse gas emissions.

[0141] The methods described in the above steps are all described in the above embodiments. Please refer to the above description for details and will not be repeated here.

[0142] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0143] Based on the same inventive concept, embodiments of the present application also provide a virtual power plant carbon reduction capability assessment device for implementing the aforementioned virtual power plant carbon reduction capability assessment method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the one or more virtual power plant carbon reduction capability assessment device embodiments provided below can be found in the aforementioned limitations of the virtual power plant carbon reduction capability assessment method, and will not be repeated here.

[0144] In some embodiments, as Figure 7 As shown, a device for evaluating the carbon reduction capability of a virtual power plant is provided, comprising:

[0145] The acquisition module 11 is used to obtain the cost information of the virtual power plant corresponding to the actual building, obtain the first carbon emission information of the actual building before the establishment of the virtual power plant, and obtain the second carbon emission information of the actual building after the establishment of the virtual power plant.

[0146] The evaluation module 12 is used to input the cost information, the first carbon emission information and the second carbon emission information of the virtual power plant into a preset carbon reduction capability evaluation model to evaluate the carbon reduction capability of the virtual power plant and obtain an evaluation result.

[0147] In some embodiments, the acquisition module includes:

[0148] The first acquisition unit is used to obtain the construction cost of the virtual power plant, the operating cost of the actual building and the residual value of the virtual power plant.

[0149] The first prediction unit is used to input the construction cost, operating cost and residual value into a preset cost prediction model to predict the cost information of the virtual power plant, and obtain the cost information of the virtual power plant corresponding to the actual building.

[0150] In some embodiments, the first acquiring unit includes:

[0151] The first acquisition subunit is used to obtain photovoltaic equipment information and energy storage equipment information in the actual building, and input the photovoltaic equipment information and energy storage equipment information into the first prediction model for prediction to obtain the construction cost of the virtual power plant.

[0152] The second acquisition sub-unit is used to obtain the electricity trading information of the actual building, and input the electricity trading information into the second prediction model for prediction to obtain the operating cost of the actual building; the electricity trading information of the actual building includes the unit price of purchased electricity of the actual building, the amount of purchased electricity of the actual building, the unit price of photovoltaic operating cost of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage discharging power of the actual building, the carbon trading unit price of the actual building and at least one of the carbon emissions of the actual building.

[0153] The third acquisition subunit is used to obtain the residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment of the actual building, and input the residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment into the third prediction model for prediction to obtain the remaining residual value of the virtual power plant.

[0154] In some embodiments, the acquisition module includes:

[0155] The second acquisition unit is used to obtain the actual amount of purchased electricity of the building and the actual regional power grid carbon emission factor of the building.

[0156] The second prediction unit is used to input the regional power grid carbon emission factor and the amount of purchased electricity into a preset carbon emission prediction model to perform carbon emission prediction of the actual building and obtain second carbon emission information.

[0157] In some embodiments, the second acquiring unit includes:

[0158] The third acquisition subunit is used to obtain the load information of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage charging efficiency of the actual building, the energy storage discharge power of the actual building and the energy storage discharge efficiency of the actual building.

[0159] The prediction subunit is used to input load information, photovoltaic output power, energy storage charging power, energy storage charging efficiency, energy storage discharge power and energy storage discharge efficiency into the power prediction model for prediction, and obtain the actual amount of purchased electricity for the building.

[0160] Each module in the aforementioned virtual power plant carbon reduction capability assessment device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0161] In some embodiments, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0162] Obtaining cost information of a virtual power plant corresponding to an actual building, obtaining first carbon emission information of the actual building before establishing the virtual power plant, and obtaining second carbon emission information of the actual building after establishing the virtual power plant;

[0163] The cost information, the first carbon emission information, and the second carbon emission information of the virtual power plant are input into a preset carbon reduction capacity evaluation model to evaluate the carbon reduction capacity of the virtual power plant and obtain an evaluation result.

[0164] In some embodiments, when the processor executes the computer program, it further implements the following steps:

[0165] Obtain the construction cost of the virtual power plant, the operating cost of the actual building, and the remaining residual value of the virtual power plant;

[0166] The construction cost, operating cost and residual value are input into the preset cost prediction model to predict the cost information of the virtual power plant and obtain the cost information of the virtual power plant corresponding to the actual building.

[0167] In some embodiments, when the processor executes the computer program, it further implements the following steps:

[0168] Obtaining photovoltaic equipment information and energy storage equipment information in the actual building, and inputting the photovoltaic equipment information and energy storage equipment information into the first prediction model for prediction to obtain the construction cost of the virtual power plant;

[0169] Obtain electricity trading information of the actual building, and input the electricity trading information into the second prediction model for prediction to obtain the operating cost of the actual building; the electricity trading information of the actual building includes at least one of the unit price of purchased electricity of the actual building, the amount of purchased electricity of the actual building, the unit price of photovoltaic operating cost of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage discharging power of the actual building, the carbon trading unit price of the actual building, and the carbon emissions of the actual building.

[0170] The residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment of the actual building are obtained, and the residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment are input into the third prediction model for prediction to obtain the remaining residual value of the virtual power plant.

[0171] Obtain the actual amount of purchased electricity for the building and the regional grid carbon emission factor for the building;

[0172] The regional power grid carbon emission factor and the amount of purchased electricity are input into a preset carbon emission prediction model to predict the carbon emissions of the actual building and obtain the second carbon emission information.

[0173] In some embodiments, when the processor executes the computer program, the processor further implements the following steps:

[0174] Obtain the load information of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage charging efficiency of the actual building, the energy storage discharge power of the actual building, and the energy storage discharge efficiency of the actual building;

[0175] The load information, photovoltaic output power, energy storage charging power, energy storage charging efficiency, energy storage discharge power and energy storage discharge efficiency are input into the power forecasting model for prediction to obtain the actual amount of purchased electricity for the building.

[0176] The computer device provided in the above embodiment has an implementation principle and technical effects similar to those of the above method embodiment, and will not be described in detail here.

[0177] In some embodiments, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0178] Obtaining cost information of a virtual power plant corresponding to an actual building, obtaining first carbon emission information of the actual building before establishing the virtual power plant, and obtaining second carbon emission information of the actual building after establishing the virtual power plant;

[0179] The cost information, the first carbon emission information, and the second carbon emission information of the virtual power plant are input into a preset carbon reduction capacity evaluation model to evaluate the carbon reduction capacity of the virtual power plant and obtain an evaluation result.

[0180] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0181] Obtain the construction cost of the virtual power plant, the operating cost of the actual building, and the remaining residual value of the virtual power plant;

[0182] The construction cost, operating cost and residual value are input into the preset cost prediction model to predict the cost information of the virtual power plant and obtain the cost information of the virtual power plant corresponding to the actual building.

[0183] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0184] Obtaining photovoltaic equipment information and energy storage equipment information in the actual building, and inputting the photovoltaic equipment information and energy storage equipment information into the first prediction model for prediction to obtain the construction cost of the virtual power plant;

[0185] Obtain electricity trading information of the actual building, and input the electricity trading information into the second prediction model for prediction to obtain the operating cost of the actual building; the electricity trading information of the actual building includes at least one of the unit price of purchased electricity of the actual building, the amount of purchased electricity of the actual building, the unit price of photovoltaic operating cost of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage discharging power of the actual building, the carbon trading unit price of the actual building, and the carbon emissions of the actual building.

[0186] The residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment of the actual building are obtained, and the residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment are input into the third prediction model for prediction to obtain the remaining residual value of the virtual power plant.

[0187] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0188] Obtain the actual amount of purchased electricity for the building and the regional grid carbon emission factor for the building;

[0189] The regional power grid carbon emission factor and the amount of purchased electricity are input into a preset carbon emission prediction model to predict the carbon emissions of the actual building and obtain the second carbon emission information.

[0190] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0191] Obtain the load information of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage charging efficiency of the actual building, the energy storage discharge power of the actual building, and the energy storage discharge efficiency of the actual building;

[0192] The load information, photovoltaic output power, energy storage charging power, energy storage charging efficiency, energy storage discharge power and energy storage discharge efficiency are input into the power forecasting model for prediction to obtain the actual amount of purchased electricity for the building.

[0193] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effects are similar to those of the above method embodiment, and will not be repeated here.

[0194] In some embodiments, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0195] Obtaining cost information of a virtual power plant corresponding to an actual building, obtaining first carbon emission information of the actual building before establishing the virtual power plant, and obtaining second carbon emission information of the actual building after establishing the virtual power plant;

[0196] The cost information, the first carbon emission information, and the second carbon emission information of the virtual power plant are input into a preset carbon reduction capacity evaluation model to evaluate the carbon reduction capacity of the virtual power plant and obtain an evaluation result.

[0197] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0198] Obtain the construction cost of the virtual power plant, the operating cost of the actual building, and the remaining residual value of the virtual power plant;

[0199] The construction cost, operating cost and residual value are input into the preset cost prediction model to predict the cost information of the virtual power plant and obtain the cost information of the virtual power plant corresponding to the actual building.

[0200] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0201] Obtaining photovoltaic equipment information and energy storage equipment information in the actual building, and inputting the photovoltaic equipment information and energy storage equipment information into the first prediction model for prediction to obtain the construction cost of the virtual power plant;

[0202] Obtain electricity trading information of the actual building, and input the electricity trading information into the second prediction model for prediction to obtain the operating cost of the actual building; the electricity trading information of the actual building includes at least one of the unit price of purchased electricity of the actual building, the amount of purchased electricity of the actual building, the unit price of photovoltaic operating cost of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage discharging power of the actual building, the carbon trading unit price of the actual building, and the carbon emissions of the actual building.

[0203] The residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment of the actual building are obtained, and the residual value information of the photovoltaic equipment and the residual value information of the energy storage equipment are input into the third prediction model for prediction to obtain the remaining residual value of the virtual power plant.

[0204] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0205] Obtain the actual amount of purchased electricity for the building and the regional grid carbon emission factor for the building;

[0206] The regional power grid carbon emission factor and the amount of purchased electricity are input into a preset carbon emission prediction model to predict the carbon emissions of the actual building and obtain the second carbon emission information.

[0207] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0208] Obtain the load information of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage charging efficiency of the actual building, the energy storage discharge power of the actual building, and the energy storage discharge efficiency of the actual building;

[0209] The load information, photovoltaic output power, energy storage charging power, energy storage charging efficiency, energy storage discharge power and energy storage discharge efficiency are input into the power forecasting model for prediction to obtain the actual amount of purchased electricity for the building.

[0210] The above embodiment provides a computer program product, whose implementation principle and technical effects are similar to those of the above method embodiment, and will not be repeated here.

[0211] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0212] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0213] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for evaluating the carbon reduction capability of a virtual power plant, characterized in that: The method comprises: Obtaining cost information of a virtual power plant corresponding to an actual building, obtaining first carbon emission information of the actual building before establishing the virtual power plant, and obtaining second carbon emission information of the actual building after establishing the virtual power plant; The cost information of the virtual power plant, the first carbon emission information and the second carbon emission information are input into a preset carbon reduction capability evaluation model to evaluate the carbon reduction capability of the virtual power plant and obtain an evaluation result.

2. The method according to claim 1, characterized in that The obtaining of cost information of the virtual power plant corresponding to the actual building includes: Obtaining the construction cost of the virtual power plant, the operating cost of the actual building, and the residual value of the virtual power plant; The construction cost, the operating cost and the residual value are input into a preset cost prediction model to predict the cost information of the virtual power plant, thereby obtaining the cost information of the virtual power plant corresponding to the actual building.

3. The method according to claim 2, characterized in that The obtaining of the construction cost of the virtual power plant, the operating cost of the actual building, and the residual value of the virtual power plant includes: Obtaining photovoltaic equipment information and energy storage equipment information in the actual building, and inputting the photovoltaic equipment information and the energy storage equipment information into a first prediction model for prediction to obtain the construction cost of the virtual power plant; Obtaining power transaction information of the actual building, and inputting the power transaction information into a second prediction model for prediction to obtain the operating cost of the actual building; The photovoltaic equipment residual value information and the energy storage equipment residual value information of the actual building are obtained, and the photovoltaic equipment residual value information and the energy storage equipment residual value information are input into a third prediction model for prediction to obtain the remaining residual value of the virtual power plant.

4. The method according to claim 3, characterized in that The electricity trading information of the actual building includes at least one of the unit price of purchased electricity of the actual building, the amount of purchased electricity of the actual building, the unit price of photovoltaic operation cost of the actual building, the photovoltaic output power of the virtual power plant, the energy storage charging power of the actual building, the energy storage discharging power of the actual building, the carbon trading unit price of the actual building and the carbon emissions of the actual building.

5. The method according to any one of claims 1 to 4, characterized in that The obtaining of second carbon emission information of actual buildings after establishing the virtual power plant includes: Obtaining the amount of purchased electricity of the actual building and the regional power grid carbon emission factor of the actual building; The regional power grid carbon emission factor and the purchased electricity amount are input into a preset carbon emission prediction model to perform carbon emission prediction of the actual building, thereby obtaining the second carbon emission information.

6. The method according to claim 5, characterized in that The obtaining of the actual amount of purchased electricity of the building includes: Obtaining load information of the actual building, photovoltaic output power of the virtual power plant, energy storage charging power of the actual building, energy storage charging efficiency of the actual building, energy storage discharge power of the actual building, and energy storage discharge efficiency of the actual building; The load information, the photovoltaic output power, the energy storage charging power, the energy storage charging efficiency, the energy storage discharging power and the energy storage discharging efficiency are input into an electricity prediction model for prediction to obtain the actual amount of purchased electricity for the building.

7. A device for evaluating the carbon reduction capability of a virtual power plant, characterized in that: The device comprises: an acquisition module, configured to acquire cost information of a virtual power plant corresponding to an actual building, acquire first carbon emission information of the actual building before establishing the virtual power plant, and acquire second carbon emission information of the actual building after establishing the virtual power plant; An evaluation module is used to input the cost information of the virtual power plant, the first carbon emission information and the second carbon emission information into a preset carbon reduction capacity evaluation model to evaluate the carbon reduction capacity of the virtual power plant and obtain an evaluation result.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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