Charging pile scheduling method and system considering power grid carbon emission factors

Abstract

The invention relates to the technical field of charging pile power scheduling, and discloses a charging pile scheduling method and system considering a power grid carbon emission factor, and the method comprises the steps: obtaining a historical data sequence through interaction, and setting a power scheduling strategy of a charging pile on a scheduling day according to the historical data sequence; wherein the historical data sequence comprises a historical carbon emission factor sequence, a historical power grid load sequence, a historical charging load sequence and a historical weather data sequence; monitoring a carbon emission factor and a power grid load of the power grid on the dispatching day, and inputting the carbon emission factor and the power grid load into a pre-constructed carbon-electricity coupling model to obtain a real-time power distribution value of the charging pile; and correcting the power scheduling strategy based on the real-time power distribution value, and executing charging pile scheduling according to the corrected power scheduling strategy. According to the method and the system, the carbon emission factor is introduced, the power grid condition is monitored in real time, and the scheduling strategy is dynamically adjusted, so that low-carbon charging is realized, power grid load distribution is optimized, and the scheduling flexibility is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile power scheduling, and in particular to a charging pile scheduling method and system taking into account the carbon emission factor of a power grid. Background Art

[0002] With the rapid popularization of electric vehicles, the large-scale access of charging piles has brought new challenges to the operation of the power grid. Traditional charging pile scheduling methods mainly allocate power based on time-of-use electricity prices or grid load balancing, such as guiding centralized charging during low-price periods to reduce energy costs, or dynamically adjusting charging power based on load forecasts to avoid local overloads. The charging pile scheduling strategy does not incorporate the carbon emission factors of the power grid into the decision-making system, and does not establish a dynamic mapping relationship between charging power and carbon emissions. It is difficult to quantify the carbon emission reduction contribution of the scheduling strategy, resulting in concentrated charging loads during high-carbon emission periods (such as when coal-fired power generation accounts for a high proportion), which aggravates the carbon footprint of the power grid.

[0003] In addition, existing methods mostly use fixed power allocation modes or static scheduling based on day-ahead forecasts, which cannot respond to the dynamic changes of the grid carbon emission factor and load in real time. For example, when the wind power and photovoltaic power output suddenly increase and the carbon emission factor drops sharply, the charging pile power cannot be increased in time to utilize clean energy, resulting in a waste of resources, and the day-ahead scheduling is separated from real-time control, resulting in a significant deviation between the scheduling plan and the actual operation.

[0004] In recent years, the academic community has proposed several improvement plans for the above problems. The patent with publication number CN114726024A realizes dynamic dispatch by building a load-electricity price linkage model, but still takes economy as the core goal; although the document "Electric Vehicle Cooperative Dispatch under Low-Carbon Grid" introduces carbon emission constraints, its static weight allocation mechanism is difficult to adapt to the real-time changes in the grid energy structure. Summary of the invention

[0005] To this end, the technical problem to be solved by the present invention is to overcome the problems of the lack of carbon emission factors and insufficient dynamic response capabilities in the charging pile scheduling strategy in the prior art, and propose a charging pile scheduling method and system that takes into account the carbon emission factors of the power grid. By introducing the carbon emission factors, the power grid status is monitored in real time and the scheduling strategy is dynamically adjusted to achieve low-carbon charging, optimize the power grid load distribution, and improve the flexibility of scheduling.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a charging pile scheduling method taking into account the carbon emission factor of the power grid, comprising: Interactively obtain a historical data sequence, and set a power dispatch strategy for the charging pile on the dispatch day according to the historical data sequence; wherein the historical data sequence includes a historical carbon emission factor sequence, a historical power grid load sequence, a historical charging load sequence, and a historical weather data sequence; On the scheduling day, the carbon emission factor and the load of the power grid are monitored, and a pre-built carbon-electricity coupling model is input to obtain a real-time power allocation value of the charging pile; The power scheduling strategy is modified based on the real-time power allocation value, and the charging pile scheduling is performed according to the modified power scheduling strategy.

[0007] In one embodiment of the present invention, the power dispatching strategy of the charging pile on the dispatching day is set according to the historical data sequence, including: Build and train predictive models; The prediction model obtains the prediction result of the scheduling day according to the historical data sequence; wherein the prediction result includes the carbon emission factor prediction value, the power grid load prediction value and the charging load prediction value; The power scheduling strategy is set according to the prediction result.

[0008] In one embodiment of the present invention, setting the power scheduling strategy further includes: dividing the scheduling day into a plurality of time periods; According to the prediction result of the prediction model, a basic power allocation value is set for each of the time periods, and the power scheduling strategy is formed by the basic power allocation value of each of the time periods; The basic power allocation value for each time period is set according to the following method: ; Indicates the basic power allocation value of charging pile i in time period t; i represents the charging pile number; t represents the time period number; Indicates the maximum power of charging pile i; Indicates the maximum load capacity of the power grid; represents the predicted value of the grid load during period t; N represents the number of charging piles; represents the predicted value of charging load of charging pile i in period t; Indicates the length of the time period.

[0009] In one embodiment of the present invention, after setting the basic power allocation value, the method further includes: Compare the predicted carbon emission factor with the carbon emission factor threshold: If the predicted value of the carbon emission factor is less than the carbon emission factor threshold value 1, increasing the basic power allocation value according to the first factor; If the predicted value of the carbon emission factor is greater than the second carbon emission factor threshold, the basic power allocation value is reduced according to the second factor; wherein the first carbon emission factor threshold is less than the second carbon emission factor threshold; Generating the power scheduling strategy based on the increased or decreased basic power allocation value; The first factor A1 and the second factor A2 are respectively: ; ; in, represents the predicted value of carbon emission factor in period t; and Represent the maximum and minimum values ​​of the carbon emission factor respectively.

[0010] In one embodiment of the present invention, after setting the basic power allocation value, it also includes adjusting the basic power allocation value according to the user priority of the charging pile, and generating the power scheduling strategy based on the adjusted basic power allocation value.

[0011] In one embodiment of the present invention, the prediction model includes prediction channel 1 and prediction channel 2. Prediction channel 1 predicts the predicted value of the carbon emission factor on the scheduling day based on the historical carbon emission factor sequence and the historical weather data sequence; Prediction channel 2 predicts the grid load forecast value of the dispatching day according to the historical grid load sequence and the historical weather data sequence; Among them, constructing the prediction model includes extracting features from the historical weather data sequence to obtain a shared weather feature vector; wherein the shared weather feature vector has dual-task adaptability; the dual tasks are the task of predicting carbon emission factors and the task of predicting power grid loads.

[0012] In one embodiment of the present invention, constructing the prediction model further includes executing caching the shared weather feature vector into a cache for the prediction channel 1 and the prediction channel 2 to call when predicting their respective channels; Among them, when prediction channel 1 or prediction channel 2 initiates a call request, the shared weather feature vector is read from the cache first; if the cache misses or the data expires, real-time feature calculation is triggered and the cache is updated.

[0013] In one embodiment of the present invention, constructing the carbon-electric coupling model includes: The carbon-electricity coupling model is constructed with carbon emission factors, grid load and charging pile status information as input and the real-time power allocation value of each charging pile as output; Converting the carbon-electric coupling model into a state-space model, and updating state variables of the state-space model based on a Kalman filter algorithm; The optimization goals are to minimize the carbon emission factor and maximize user satisfaction, and the state space model is solved based on the distributed optimization algorithm to obtain the real-time power allocation value of each charging pile in different time periods.

[0014] In one embodiment of the present invention, the power scheduling strategy is modified based on the real-time power allocation value, including: Calculate the difference between the predicted value of carbon emission factor and the monitored value of carbon emission factor; Introducing carbon emission factor adjustment coefficient ; Adjustment factor based on the difference and carbon emission factor Adjusting the basic power allocation value; The adjustment formula is:

[0015] ; represents the basic power allocation value of charging pile i in time period t after adjustment according to the real-time power allocation value; It represents the difference between the predicted value of carbon emission factor and the monitored value of carbon emission factor; Represents the predicted value of carbon emission factor in period t.

[0016] In the second aspect, based on the same inventive concept, in order to solve the above technical problems, the present invention also provides a charging pile scheduling system taking into account the carbon emission factor of the power grid, comprising: A historical data acquisition module, which is used to interactively obtain a historical data sequence; the historical data sequence includes a historical carbon emission factor sequence, a historical power grid load sequence, a historical charging load sequence and a historical weather data sequence; A scheduling strategy generation module, used to generate a power scheduling strategy for the charging pile on the scheduling day according to the historical data sequence; A data monitoring and analysis module, used to monitor the carbon emission factor data and load data of the power grid on the dispatching day, and input a pre-built carbon-electricity coupling model to obtain the real-time power allocation value of the charging pile; The scheduling execution module is used to modify the power scheduling strategy according to the real-time power allocation value, and execute the charging pile scheduling according to the modified power scheduling strategy.

[0017] The above technical solution of the present invention has the following beneficial effects compared with the prior art: The charging pile scheduling method and system taking into account the carbon emission factor of the power grid described in the present invention, by introducing the carbon emission factor, monitors the power grid status in real time and dynamically adjusts the scheduling strategy, thereby realizing low-carbon charging and optimizing the power grid load distribution; in addition, by real-time monitoring of the carbon emission factor and power grid load of the power grid on the scheduling day, it is possible to quickly respond to changes in the power grid status, calculate the power distribution value of the charging pile in real time through the carbon-electricity coupling model, and dynamically adjust the power scheduling strategy, thereby improving the flexibility and adaptability of the scheduling, and ensuring that the power scheduling of the charging pile can adapt to the fluctuations in the power grid load and the carbon emission level in real time.

[0018] By combining carbon emission factors with grid load and adjusting power distribution in real time, the problem of traditional charging pile scheduling strategy lacking carbon emission considerations and insufficient dynamic response capabilities is overcome, which significantly improves scheduling efficiency, reduces carbon emissions, and optimizes grid load. At the same time, it makes positive contributions to the consumption of green energy and the sustainable development of the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein: Figure 1 It is a flow chart of a charging pile scheduling method taking into account the carbon emission factor of the power grid in a preferred embodiment of the present invention; Figure 2 This is a flowchart of setting a power dispatching strategy for a charging pile on a dispatching day according to a historical data sequence in one embodiment of the present invention; Figure 3 A flowchart of setting a power dispatching strategy of a charging pile on a dispatching day according to a historical data sequence in another embodiment of the present invention; Figure 4 A flowchart of constructing the carbon-electric coupling model in a preferred embodiment of the present invention; Figure 5 It is a structural block diagram of a charging pile scheduling system taking into account the carbon emission factor of the power grid in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0020] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0021] Embodiment 1

[0022] Reference Figure 1 As shown, the embodiment of the present invention discloses a charging pile scheduling method taking into account the carbon emission factor of the power grid, including: S100, interactively obtain a historical data sequence, and set a power dispatch strategy for a charging pile on a dispatch day according to the historical data sequence; wherein the historical data sequence includes a historical carbon emission factor sequence, a historical power grid load sequence, a historical charging load sequence, and a historical weather data sequence; S200, monitoring the carbon emission factor and grid load of the power grid on the scheduling day, and inputting a pre-built carbon-electricity coupling model to obtain a real-time power allocation value of the charging pile; S300: modifying the power scheduling strategy based on the real-time power allocation value, and executing the charging pile scheduling according to the modified power scheduling strategy.

[0023] The charging pile scheduling method and system taking into account the carbon emission factor of the power grid described in the present invention, by introducing the carbon emission factor, monitors the power grid status in real time and dynamically adjusts the scheduling strategy, thereby realizing low-carbon charging and optimizing the power grid load distribution; in addition, by real-time monitoring of the carbon emission factor and power grid load of the power grid on the scheduling day, it is possible to quickly respond to changes in the power grid status, calculate the power distribution value of the charging pile in real time through the carbon-electricity coupling model, and dynamically adjust the power scheduling strategy, thereby improving the flexibility and adaptability of the scheduling, and ensuring that the power scheduling of the charging pile can adapt to the fluctuations in the power grid load and the carbon emission level in real time.

[0024] In specific application scenarios, interactive acquisition of historical data sequences can be done through API interfaces, and historical data can be automatically acquired by connecting to system interfaces of power system operators, weather forecast services, carbon emission data providers, etc. For example, historical carbon emission factor sequences (sampled every 15 minutes), historical power grid load sequences (sampled every hour), and historical charging load sequences (recorded by charging piles, sampled every hour) can be obtained from power grid data centers; historical weather data sequences (temperature, humidity, wind speed, light intensity, etc., sampled every hour) can be obtained from meteorological departments.

[0025] Historical data sequences help identify the changing patterns of grid load, the fluctuation patterns of carbon emission factors, the spatiotemporal distribution of charging demand, and the impact of climate factors on charging behavior. Through the accumulation of historical data, charging pile scheduling can achieve optimization strategy setting based on historical laws. By analyzing historical weather data, the impact of weather on the grid and charging pile demand can be more accurately predicted. The introduction of weather data enhances the accuracy of scheduling, especially the changes in electricity demand in hot or cold weather. Combined with historical charging load data, it is possible to analyze the charging demand in a specific period of time, optimize the scheduling strategy, make the charging load more balanced, and thus avoid grid load fluctuations.

[0026] On the dispatching day, the carbon emission factor and load of the power grid are monitored in real time. The monitoring results of the power grid load and carbon emission factor will be used as input and passed to the pre-built carbon-electricity coupling model. The carbon-electricity coupling model calculates the real-time power allocation value of the charging pile by analyzing the real-time data. By correcting the dispatching strategy based on the real-time power allocation value, the charging pile can adapt to the changes in the power grid load and carbon emission factor in real time, rather than a fixed preset plan, to achieve the combination of day-ahead dispatching and real-time control, reduce the deviation between the dispatching plan and the actual operation, and improve the flexibility of the charging pile dispatching strategy.

[0027] In a further embodiment, referring to Figure 2As shown, setting the power dispatching strategy of the charging pile on the dispatching day according to the historical data sequence includes: constructing and training a prediction model; the prediction model obtains the prediction result of the dispatching day according to the historical data sequence; wherein the prediction result includes the carbon emission factor prediction value, the grid load prediction value and the charging load prediction value; and setting the power dispatching strategy according to the prediction result.

[0028] In the specific implementation, the prediction model adopts a multi-task prediction model architecture, and the multi-task includes carbon emission factor prediction, grid load prediction and charging load prediction; the multi-task prediction model architecture includes a branch for predicting carbon emission factors, a branch for predicting grid load and a branch for predicting charging load; among them, the branch for predicting carbon emission factors adopts an autoregressive integrated moving average (ARIMA) model, the branch for predicting grid load adopts a long short-term memory network (LSTM) model, and the branch for predicting charging load adopts an extreme gradient boosting decision tree (XGB00st) model. The prediction model should be understood as obtaining the grid load variation law, the fluctuation pattern of carbon emission factors and the spatiotemporal distribution of charging demand through deep learning of historical data sequences, and predicting the carbon emission factor, grid load and charging load on the scheduling day based on the learning results.

[0029] Reference Figure 2 As shown, in one embodiment of the present invention, setting the power scheduling strategy further includes: dividing the scheduling day into multiple time periods; setting a basic power allocation value for each of the time periods according to the prediction results of the prediction model, and the power scheduling strategy is composed of the basic power allocation values ​​of each of the time periods.

[0030] In other words, by subdividing the scheduling day and setting the basic power allocation for each period based on the prediction model, a complete scheduling strategy is finally formed. In specific application scenarios:

[0031] Step 1: Divide the dispatching day into multiple time periods: For example, divide the dispatching day into 96 time periods, each 15 minutes long; of course, the rules for dividing time periods can also be set according to the operation characteristics of the power grid, user charging behavior, and historical data analysis results, for example, a more specific division can be made according to the peak and valley periods of demand: morning peak, evening peak, night valley, etc. By subdividing the dispatching day into multiple time periods, the charging strategy can be adjusted more finely according to the real-time or predicted status of the power grid, and a more refined management of the charging strategy can be achieved.

[0032] Step 2: Set the basic power allocation value for each time period according to the prediction results of the prediction model; set the basic power for each time period through accurate prediction data to ensure that power resources are effectively used when they are most needed. For example, increase charging activities during periods with lower carbon emissions, and conversely, reduce charging activities when carbon emissions are higher.

[0033] Step 3: The power dispatching strategy is formed by the basic power allocation values ​​of each time period. The power allocation strategies of each time period are summarized into a complete dispatching strategy for the day, ensuring the orderliness and consistency of the charging strategy from morning to night.

[0034] Specifically, the basic power allocation value for each time period is set according to the following method:

[0035] ;

[0036] Indicates the basic power allocation value of charging pile i in time period t; i represents the charging pile number; t represents the time period number; Indicates the maximum power of charging pile i; Indicates the maximum load capacity of the power grid; represents the predicted value of the grid load during period t; N represents the number of charging piles; represents the predicted value of charging load of charging pile i in period t; Indicates the length of the time period.

[0037] Reference Figure 3 As shown, in another embodiment of the present invention, setting the power scheduling strategy further includes comparing the carbon emission factor prediction value with the carbon emission factor threshold after setting the basic power allocation value:

[0038] If the predicted value of the carbon emission factor is less than the carbon emission factor threshold 1, the basic power allocation value is increased according to the first factor; if the predicted value of the carbon emission factor is greater than or equal to the carbon emission factor threshold 1 and less than or equal to the carbon emission factor threshold 2, the basic power allocation value is set according to the prediction result of the prediction model; if the predicted value of the carbon emission factor is greater than the carbon emission factor threshold 2, the basic power allocation value is reduced according to the second factor; wherein the carbon emission factor threshold 1 is less than the carbon emission factor threshold 2;

[0039] Generating the power scheduling strategy based on the increased or decreased basic power allocation value;

[0040] The first factor A1 and the second factor A2 are respectively:

[0041] ;

[0042] ;

[0043] in, represents the predicted value of carbon emission factor in period t; and Represent the maximum and minimum values ​​of the carbon emission factor respectively.

[0044] In specific application scenarios, carbon emission factor threshold 1 and carbon emission factor threshold 2 are key parameters for determining whether to adjust the basic power allocation value, which can be obtained in a variety of ways, such as historical data analysis, standards or empirical rules provided by power grid operators, and industry practices. Among them, carbon emission factor threshold 1 represents a lower emission level (i.e., "green" operating state), while carbon emission factor threshold 2 represents a medium emission level.

[0045] When the predicted value of the carbon emission factor on the dispatching day is less than the carbon emission factor threshold 1, it indicates that the predicted carbon emission factor on the dispatching day is low, which means that the power grid is at a low carbon emission level. Usually, the supply of renewable energy is large and the load of the power grid is light. At this time, according to the first factor A1, the basic power allocation value can be increased to maximize the use of green energy without increasing carbon emissions, promoting environmental protection; when the predicted value of the carbon emission factor on the dispatching day is greater than the carbon emission factor threshold 2, it indicates that the predicted carbon emission factor on the dispatching day is high, and it may be in a state of high load or high proportion of fossil energy. At this time, according to the second factor A2, the basic power allocation value is reduced to reduce charging during high carbon emission periods and reduce the environmental burden; when the predicted value of the carbon emission factor on the dispatching day is between the carbon emission factor threshold 1 and the carbon emission factor threshold 2, it indicates that the predicted carbon emission factor on the dispatching day is in an appropriate state. At this time, the basic power allocation value set according to the prediction result is directly adopted. By setting the carbon emission factor threshold and comparing it with the carbon emission factor prediction day, the basic power allocation value is made more accurate, so that optimizing the dispatching of charging piles can not only reduce carbon emissions, but also optimize the power grid load management.

[0046] It should be noted that after the basic power allocation value is set according to the prediction result, the power is adjusted according to the first factor or the second factor on the basis of this power allocation value, and the adjusted power is used as the basic power allocation value for subsequent scheduling. If the carbon emission factor prediction value is greater than or equal to the carbon emission factor threshold 1 and less than or equal to the carbon emission factor threshold 2, the basic power allocation value is set according to the prediction result of the prediction model and directly used as the basic power allocation value for subsequent scheduling.

[0047] Furthermore, after setting the basic power allocation value, it also includes adjusting the basic power allocation value according to the user priority of the charging pile, and generating the power scheduling strategy by the adjusted basic power allocation value. User priorities include emergency service vehicles (such as ambulances and fire trucks) with the highest priority, public transportation (such as electric buses and taxis) with a higher priority, personal electric vehicles and electric delivery vehicles with a medium priority, and rental and shared vehicles with a low priority. Of course, user priority supports customization, and the basic power allocation value is adjusted according to the user priority, making the scheduling strategy more flexible.

[0048] In a further improvement of the embodiment of the present invention, the prediction model includes prediction channel one, prediction channel two and prediction channel three, prediction channel one predicts the predicted value of the carbon emission factor on the scheduling day based on the historical carbon emission factor sequence and the historical weather data sequence; prediction channel two predicts the predicted value of the power grid load on the scheduling day based on the historical power grid load sequence and the historical weather data sequence; prediction channel three predicts the predicted value of the charging load on the scheduling day based on the historical charging load sequence.

[0049] Both the carbon emission factor prediction and the power grid load prediction are related to the historical weather data series. In order to reduce computational redundancy and improve the operating efficiency of the prediction model, constructing the prediction model includes extracting features from the historical weather data series to obtain a shared weather feature vector; wherein the shared weather feature vector has dual-task adaptability; the dual tasks are the task of predicting the carbon emission factor and the task of predicting the power grid load.

[0050] In the specific application scenario, the prediction model is configured with a unified feature extraction module. First, the historical weather data sequence is reduced in dimension through the principal component analysis (PCA) algorithm to obtain a low-dimensional feature vector. The unified feature extraction module has a shared encoder, which takes the low-dimensional feature vector as input. During training, the loss functions of carbon emission prediction and power grid load prediction are optimized at the same time, forcing the encoder to extract features that are explanatory for both tasks. At the same time, a multi-head attention layer is added to the shared encoder to automatically identify weather features that are important for both tasks. SHAPShapley (Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations) is used to quantify the contribution of each weather feature to the two tasks, and the encoder structure is adjusted to enhance the expression of shared important features. A task discriminator is added after the encoder, and adversarial training is used to force the feature vector to be unable to distinguish the task type, thereby ensuring the universality of the features, and finally obtaining a shared weather feature vector. The shared weather feature vector has dual-task adaptability for predicting carbon emission factors and predicting power grid loads. The shared weather feature vector is called by prediction channel one and prediction channel two, thereby reducing computational redundancy and improving the operating efficiency of the prediction model.

[0051] Furthermore, constructing the prediction model also includes executing the caching of the shared weather feature vector to a cache for the prediction channel one and the prediction channel two to call when predicting their respective channels; wherein, when the prediction channel one or the prediction channel two initiates a call request, the shared weather feature vector is preferentially read from the cache; if the cache misses or the data expires, real-time feature calculation is triggered and the cache is updated. By setting up a cache mechanism, millisecond-level response to prediction requests is achieved, supporting high-concurrency scenarios; in addition, the cache mechanism can be horizontally expanded to multi-regional power grid systems. The cache mechanism combined with weather feature sharing can effectively reduce more than 60% of repeated calculations, reduce computing resource consumption, and ensure prediction accuracy and real-time performance.

[0052] In other embodiments of the present invention, referring to Figure 4 As shown, constructing the carbon-electric coupling model includes taking the carbon emission factor, grid load and charging pile state information as input, taking the real-time power allocation value of each charging pile as output, and constructing the carbon-electric coupling model; converting the carbon-electric coupling model into a state space model, and updating the state variables of the state space model based on the Kalman filter algorithm; taking minimizing the carbon emission factor and maximizing user satisfaction as the optimization goals, and solving the state space model based on the distributed optimization algorithm to obtain the real-time power allocation value of each charging pile in different time periods. The scheme of the present invention estimates the system state in real time through Kalman filtering, can handle the uncertainty caused by the fluctuation of the grid and carbon emission factors, and has higher adaptability to load changes and carbon emission prediction errors, thereby effectively avoiding the occurrence of grid overload and high carbon emissions, and improving the stability and safety of the grid. This scheme is different from the traditional method that relies on centralized computing. It adopts a distributed optimization algorithm and can be calculated in parallel on multiple charging pile nodes, which greatly reduces the amount of calculation required for the centralized optimization method and can make real-time power allocation decisions more quickly.

[0053] In other embodiments of the present invention, the power dispatching strategy is modified based on the real-time power allocation value, including calculating the difference between the carbon emission factor prediction value and the carbon emission factor monitoring value; introducing the carbon emission factor adjustment coefficient ; Adjustment coefficient based on the difference and carbon emission factor Adjust the basic power allocation value; wherein the adjustment formula is:

[0054] ;

[0055] represents the basic power allocation value of charging pile i in time period t after adjustment according to the real-time power allocation value; It represents the difference between the predicted value of carbon emission factor and the monitored value of carbon emission factor; Represents the predicted value of carbon emission factor in period t.

[0056] In the embodiment of the present invention, the carbon emission factor prediction error is perceived in real time by calculating the difference between the predicted value and the actual monitored value, and the power distribution of the charging pile is adjusted according to the difference. This mechanism can quickly correct the deviation caused by the prediction error, ensure that the power scheduling always matches the actual situation of the power grid, and avoid energy waste or excessive carbon emissions due to inaccurate predictions.

[0057] Embodiment 2

[0058] Based on the same inventive concept, Figure 5 As shown, an embodiment of the present invention provides a charging pile scheduling system taking into account the carbon emission factor of the power grid, including:

[0059] A historical data acquisition module, which is used to interactively obtain a historical data sequence; the historical data sequence includes a historical carbon emission factor sequence, a historical power grid load sequence, a historical charging load sequence and a historical weather data sequence;

[0060] A scheduling strategy generation module, used to generate a power scheduling strategy for the charging pile on the scheduling day according to the historical data sequence;

[0061] A data monitoring and analysis module, used to monitor the carbon emission factor data and load data of the power grid on the dispatching day, and input a pre-built carbon-electricity coupling model to obtain the real-time power allocation value of the charging pile;

[0062] The scheduling execution module is used to modify the power scheduling strategy according to the real-time power allocation value, and execute the charging pile scheduling according to the modified power scheduling strategy.

[0063] The embodiment of the present invention is used to execute the above-mentioned charging pile scheduling method taking into account the carbon emission factor of the power grid, and has the same technical effect in the process of solving the technical problem, which will not be repeated here.

[0064] In summary, the charging pile scheduling method and system taking into account the carbon emission factor of the power grid described in the present invention, by introducing the carbon emission factor, monitors the power grid status in real time and dynamically adjusts the scheduling strategy, thereby realizing low-carbon charging and optimizing the power grid load distribution; in addition, by real-time monitoring of the carbon emission factor and power grid load of the power grid on the scheduling day, it is possible to quickly respond to changes in the power grid status, calculate the power distribution value of the charging pile in real time through the carbon-electricity coupling model, and dynamically adjust the power scheduling strategy, thereby improving the flexibility and adaptability of the scheduling, and ensuring that the power scheduling of the charging pile can adapt to the fluctuations in the power grid load and the carbon emission level in real time.

[0065] By combining carbon emission factors with grid load and adjusting power distribution in real time, the problem of traditional charging pile scheduling strategy lacking carbon emission considerations and insufficient dynamic response capabilities is overcome, which significantly improves scheduling efficiency, reduces carbon emissions, and optimizes grid load. At the same time, it makes positive contributions to the consumption of green energy and the sustainable development of the grid.

[0066] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0067] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0068] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0070] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A charging pile scheduling method taking into account the carbon emission factor of the power grid, characterized by: include, Interactively obtain a historical data sequence, and set a power dispatch strategy for the charging pile on the dispatch day according to the historical data sequence; wherein the historical data sequence includes a historical carbon emission factor sequence, a historical power grid load sequence, a historical charging load sequence, and a historical weather data sequence; On the scheduling day, the carbon emission factor and the load of the power grid are monitored, and a pre-built carbon-electricity coupling model is input to obtain a real-time power allocation value of the charging pile; The power scheduling strategy is modified based on the real-time power allocation value, and the charging pile scheduling is performed according to the modified power scheduling strategy.

2. The charging pile scheduling method taking into account the carbon emission factor of the power grid according to claim 1 is characterized in that: The power dispatching strategy of the charging pile on the dispatching day is set according to the historical data sequence, including: Build and train predictive models; The prediction model obtains the prediction result of the scheduling day according to the historical data sequence; wherein the prediction result includes the carbon emission factor prediction value, the power grid load prediction value and the charging load prediction value; The power scheduling strategy is set according to the prediction result.

3. The charging pile scheduling method taking into account the carbon emission factor of the power grid according to claim 2 is characterized in that: Setting the power scheduling strategy also includes: dividing the scheduling day into a plurality of time periods; According to the prediction result of the prediction model, a basic power allocation value is set for each of the time periods, and the power scheduling strategy is formed by the basic power allocation value of each of the time periods; The basic power allocation value for each time period is set according to the following method: ； Indicates the basic power allocation value of charging pile i in time period t; i represents the charging pile number; t represents the time period number; Indicates the maximum power of charging pile i; Indicates the maximum load capacity of the power grid; represents the predicted value of the grid load during period t; N represents the number of charging piles; represents the predicted value of charging load of charging pile i in period t; Indicates the length of the time period.

4. The charging pile scheduling method taking into account the carbon emission factor of the power grid according to claim 3 is characterized in that: After setting the basic power allocation value, the method further includes: Compare the predicted carbon emission factor with the carbon emission factor threshold: If the predicted value of the carbon emission factor is less than the carbon emission factor threshold value 1, increasing the basic power allocation value according to the first factor; If the predicted value of the carbon emission factor is greater than the second carbon emission factor threshold, the basic power allocation value is reduced according to the second factor; wherein the first carbon emission factor threshold is less than the second carbon emission factor threshold; Generating the power scheduling strategy based on the increased or decreased basic power allocation value; The first factor A1 and the second factor A2 are respectively: ； ； in, represents the predicted value of carbon emission factor in period t; and Represent the maximum and minimum values ​​of the carbon emission factor respectively.

5. The charging pile scheduling method taking into account the carbon emission factor of the power grid according to claim 3 or 4, characterized in that: After setting the basic power allocation value, the method further includes adjusting the basic power allocation value according to the user priority of the charging pile, and generating the power scheduling strategy based on the adjusted basic power allocation value.

6. The charging pile scheduling method taking into account the carbon emission factor of the power grid according to claim 2 is characterized in that: The prediction model includes prediction channel one and prediction channel two. Prediction channel 1 predicts the predicted value of the carbon emission factor on the scheduling day based on the historical carbon emission factor sequence and the historical weather data sequence; Prediction channel 2 predicts the grid load forecast value of the dispatching day according to the historical grid load sequence and the historical weather data sequence; Among them, constructing the prediction model includes extracting features from the historical weather data sequence to obtain a shared weather feature vector; wherein the shared weather feature vector has dual-task adaptability; the dual tasks are the task of predicting carbon emission factors and the task of predicting power grid loads.

7. The charging pile scheduling method taking into account the carbon emission factor of the power grid according to claim 6 is characterized in that: Constructing the prediction model further includes executing caching the shared weather feature vector into a cache for the prediction channel 1 and the prediction channel 2 to call when predicting their respective channels; Among them, when prediction channel 1 or prediction channel 2 initiates a call request, the shared weather feature vector is read from the cache first; if the cache misses or the data expires, real-time feature calculation is triggered and the cache is updated.

8. The charging pile scheduling method taking into account the carbon emission factor of the power grid according to claim 1 is characterized in that: Constructing the carbon-electric coupling model includes: The carbon-electricity coupling model is constructed with carbon emission factors, grid load and charging pile status information as input and the real-time power allocation value of each charging pile as output; Converting the carbon-electric coupling model into a state-space model, and updating state variables of the state-space model based on a Kalman filter algorithm; The optimization goals are to minimize the carbon emission factor and maximize user satisfaction, and the state space model is solved based on the distributed optimization algorithm to obtain the real-time power allocation value of each charging pile in different time periods.

9. The charging pile scheduling method taking into account the carbon emission factor of the power grid according to claim 1 is characterized by: Modifying the power scheduling strategy based on the real-time power allocation value includes: Calculate the difference between the predicted value of carbon emission factor and the monitored value of carbon emission factor; Introducing carbon emission factor adjustment coefficient ; Adjustment factor based on the difference and carbon emission factor Adjust the basic power allocation value; The adjustment formula is: ； represents the basic power allocation value of charging pile i in time period t after adjustment according to the real-time power allocation value; It represents the difference between the predicted value of carbon emission factor and the monitored value of carbon emission factor; Represents the predicted value of carbon emission factor in period t.

10. A charging pile scheduling system taking into account the carbon emission factor of the power grid, used to execute the charging pile scheduling method taking into account the carbon emission factor of the power grid as claimed in any one of claims 1 to 9, characterized in that: include, A historical data acquisition module, which is used to interactively obtain a historical data sequence; the historical data sequence includes a historical carbon emission factor sequence, a historical power grid load sequence, a historical charging load sequence and a historical weather data sequence; A scheduling strategy generation module, used to generate a power scheduling strategy for the charging pile on the scheduling day according to the historical data sequence; A data monitoring and analysis module, used to monitor the carbon emission factor data and load data of the power grid on the dispatching day, and input a pre-built carbon-electricity coupling model to obtain the real-time power allocation value of the charging pile; The scheduling execution module is used to modify the power scheduling strategy according to the real-time power allocation value, and execute the charging pile scheduling according to the modified power scheduling strategy.

Images