Photovoltaic energy storage DC intelligent microgrid monitoring management system and method

Abstract

The invention relates to the technical field of photovoltaic energy storage, in particular to a photovoltaic energy storage direct current intelligent microgrid monitoring management system and method, and the method comprises the steps: predicting the predicted photovoltaic power generation amount of a parking shed in a period of time in the future based on weather forecast parameters and historical power generation data; predicting a charging curve based on historical data; calculating an insufficient generating capacity curve based on the charging curve and the predicted photovoltaic generating capacity of the parking shed; the energy storage equipment is divided into a standby state and a charging state, and when the value of the insufficient generating capacity curve is a positive value, the working states of the two kinds of energy storage equipment are switched; when the insufficient power generation curve is a positive value, generating a power grid supply curve based on the insufficient power generation curve; calculating a dynamic charging price based on the power grid supply curve; and sending the dynamic charging price to the user side. Therefore, the generating capacity and the power consumption can be predicted, corresponding energy planning is performed in advance, and the working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic energy storage, and particularly to a photovoltaic energy storage DC intelligent microgrid monitoring and management system and method. Background Art

[0002] The photovoltaic energy storage DC system is an energy solution that combines solar photovoltaic power generation and energy storage technology, aiming to improve the utilization rate of renewable energy and the stability of power supply. In this system, photovoltaic panels directly convert sunlight into direct current (DC) electrical energy, which is then transmitted to the battery energy storage system for storage through a controller, or directly supplied to DC loads for use. When the power generated by the photovoltaic exceeds the immediate demand, the excess energy is stored in an efficient battery; during periods of insufficient or no sunlight, such as at night or on cloudy days, the stored energy can be released to continue power supply, ensuring the continuity and reliability of power supply.

[0003] In the existing photovoltaic energy storage systems in industrial parks, a large part of the charging demand is to charge electric vehicles in the park. However, due to the unpredictable photovoltaic power generation and the large fluctuations in the charging demand of electric vehicles, it is impossible to effectively allocate the power supply of photovoltaic power generation, thereby reducing the energy utilization efficiency. Summary of the Invention

[0004] The purpose of the present invention is to provide a photovoltaic energy storage DC intelligent microgrid monitoring and management system and method, aiming to predict power generation and power consumption, so as to make corresponding energy plans in advance and improve work efficiency.

[0005] To achieve the above purpose, in the first aspect, the present invention provides a photovoltaic energy storage DC intelligent microgrid monitoring and management method, including predicting the predicted photovoltaic power generation of the parking shed in the next period based on weather forecast parameters and historical power generation data;

[0006] Obtaining the historical data of electric vehicle charging in the park and predicting the charging curve based on the historical data;

[0007] Calculating the insufficient power generation curve based on the charging curve and the predicted photovoltaic power generation of the parking shed;

[0008] Dividing the energy storage device into two states: standby and charging. When in the charging state, it is used to charge electric vehicles. When the value of the insufficient power generation curve is negative, the photovoltaic power generation of the parking shed charges the energy storage device in the standby state. When the value of the insufficient power generation curve is positive, the working states of the two energy storage devices are switched;

[0009] When the insufficient power generation curve is positive, generating a grid supply curve based on the insufficient power generation curve;

[0010] Calculating the dynamic charging price based on the grid supply curve;

[0011] Send the dynamic charging price to the user side.

[0012] Among them, the specific steps for predicting the photovoltaic power generation of the parking shed in the future period based on weather forecast parameters and historical power generation data include:

[0013] Monitor the photovoltaic power generation and the state of the energy storage device to obtain historical power generation data;

[0014] Obtain meteorological data, where the meteorological data includes temperature, humidity, wind speed, cloud cover, and solar radiation;

[0015] Perform time alignment and parallel processing on the historical power generation data and meteorological data;

[0016] Draw ACF and PACF diagrams to determine the autoregressive term, differencing order, and moving average term parameters in the ARIMA model;

[0017] Use the selected autoregressive term, differencing order, and moving average term parameters to construct an ARIMA model;

[0018] Adopt the maximum likelihood estimation method to train the ARIMA model using historical data;

[0019] Use the trained ARIMA model to predict the photovoltaic power generation in the next few days to obtain the predicted photovoltaic power generation of the parking shed.

[0020] Among them, the specific steps for obtaining the historical data of electric vehicle charging in the park and predicting the charging curve based on the historical data include:

[0021] Collect the historical data of electric vehicle charging;

[0022] Extract the charging behavior characteristics from the historical data of electric vehicle charging to obtain a charging training data set, where the charging behavior characteristics include date, holiday flag, time period, total daily charging amount, and average charging amount;

[0023] Train the Prophet model based on the charging training data set;

[0024] Use the trained Prophet model to predict the charging curve.

[0025] Among them, the specific steps for calculating the insufficient power generation curve based on the charging curve and the predicted photovoltaic power generation of the parking shed include:

[0026] Align the timestamps of the data of the charging curve and the photovoltaic power generation prediction;

[0027] Integrate the data of the charging curve and the photovoltaic power generation prediction into the same table;

[0028] For each time point, calculate the difference between the charging demand at that moment and the photovoltaic power generation, and finally obtain the insufficient power generation curve.

[0029] Among them, the energy storage device is divided into two states: standby and charging. When in the charging state, it is used to charge the electric vehicle. When the insufficient power generation curve is negative, the photovoltaic power generation of the parking shed charges the energy storage device in the standby state. When the insufficient power generation curve is positive, the specific steps of switching the working states of the two energy storage devices include:

[0030] Set the initial state of the energy storage device;

[0031] When the insufficient power generation in the insufficient power generation curve is negative, switch the energy storage device from the charging state to the standby state, and use the excess photovoltaic power generation to charge the energy storage device in the standby state;

[0032] When the insufficient power generation curve is positive, switch the energy storage device from the standby state to the charging state, and release power from the energy storage device to supplement the charging demand.

[0033] Among them, the specific steps of calculating the dynamic charging price based on the power grid supply curve include:

[0034] Obtain the curve reflecting the power grid power supply in different time periods

[0035] According to the power grid supply curve and the power cost structure, calculate the actual cost of purchasing electricity from the power grid at each time point;

[0036] Calculate the dynamic charging price by combining the actual cost and the additional cost. The additional cost includes the peak period surcharge and the carbon emission cost.

[0037] Among them, the specific steps of sending the dynamic charging price to the user side include:

[0038] Obtain the dynamic charging price data;

[0039] Obtain the location information of the user, and send the dynamic charging price data to the user side when the user is within the preset range.

[0040] In a second aspect, the present invention also provides a photovoltaic energy storage DC intelligent microgrid monitoring and management system, including a power generation prediction module, a charging amount prediction module, an insufficient power generation calculation module, an energy storage distribution module, a power grid energy supply module, a power price calculation module, and a notification module;

[0041] The power generation prediction module is used to predict the predicted photovoltaic power generation of the parking shed in the future for a period of time based on weather forecast parameters and historical power generation data;

[0042] The charging amount prediction module is used to obtain the historical data of electric vehicle charging in the park and predict the charging curve based on the historical data;

[0043] The insufficient power generation calculation module is used to calculate the insufficient power generation curve based on the charging curve and the predicted photovoltaic power generation of the parking shed;

[0044] The energy storage allocation module is used to divide the energy storage device into two states: standby and charging. In the charging state, it is used to charge the electric vehicle. When the value of the insufficient power generation curve is negative, the photovoltaic power generation of the parking shed charges the energy storage device in the standby state. When the value of the insufficient power generation curve is positive, the working states of the two energy storage devices are switched;

[0045] The power grid energy supply module is used to generate a power grid supply curve based on the insufficient power generation curve when the insufficient power generation curve is positive;

[0046] The electricity price calculation module is used to calculate the dynamic charging price based on the power grid supply curve;

[0047] The notification module is used to send the dynamic charging price to the user terminal.

[0048] A photovoltaic energy storage DC intelligent microgrid monitoring and management system and method of the present invention includes collecting weather forecast data, which includes parameters such as but not limited to temperature, light intensity, cloud cover rate, etc. The system will also analyze historical power generation records to understand the power generation performance of the photovoltaic system under different weather conditions. Using the above information, the system can build a prediction model to estimate the photovoltaic power generation of the parking shed within a certain period in the future. This period can be set as daily, weekly or monthly according to needs. By analyzing the historical data of electric vehicle charging in the park, the system can identify vehicle charging behavior patterns, such as peak hours, average charging duration, single charging power, etc. Based on these patterns, the system can predict the future charging curve, that is, how many electric vehicles are expected to need charging and the corresponding power demand at a specific time point. Comparing the predicted charging demand with the predicted photovoltaic power generation, calculating the difference between the two values, and forming a power shortage curve. This indicates whether the photovoltaic power generation is sufficient to meet the charging demand at any moment, and the degree of excess or shortage. The system classifies the energy storage device into two working states: standby (ready to provide electric energy) and charging (accepting and storing electric energy). When the prediction shows that the photovoltaic power generation is excessive (the power shortage curve is negative), the excess electric energy will be used to charge the energy storage device in the standby state. On the contrary, if the photovoltaic power generation is not enough to cover the demand (the power shortage curve is positive), the working state of the energy storage device will be switched, so that the energy storage device in the previous charging state will be changed to standby and supply power to the electric vehicle. When both the photovoltaic power generation and the energy storage cannot meet the charging demand (the power shortage curve remains positive), the system will consider obtaining power from the external power grid and generate a grid supply curve accordingly, indicating when and how much additional power support from the power grid is needed.

[0049] According to the grid supply curve and other factors (such as market electricity price, policy subsidy, etc.), the system will calculate a dynamic charging price. This price reflects the real-time electricity cost and changes with time and the supply-demand relationship. Finally, the dynamic charging price information will be sent to the user's terminal device, such as a smartphone application or the in-vehicle display of the electric vehicle. In this way, users can decide the best charging time according to the current price, so as to achieve cost savings.

[0050] This method not only helps to improve the intelligent level of energy use within the park, but also promotes the effective utilization of renewable energy, reduces the dependence on the traditional power grid, and at the same time provides users with more flexible and economical charging services. Brief Description of the Drawings

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0052] Figure 1 It is a flowchart of a photovoltaic energy storage DC intelligent microgrid monitoring and management method of the present invention.

[0053] Figure 2 It is a flowchart of predicting the photovoltaic power generation of a parking shed in the future for a period of time based on weather forecast parameters and historical power generation data of the present invention.

[0054] Figure 3 It is a flowchart of obtaining the historical data of electric vehicle charging in the park and predicting the charging curve based on the historical data of the present invention.

[0055] Figure 4 It is a flowchart of calculating the insufficient power generation curve based on the charging curve and the predicted photovoltaic power generation of the parking shed of the present invention.

[0056] Figure 5 It is a flowchart of the present invention that divides the energy storage device into two states: standby and charging. When in the charging state, it is used to charge the electric vehicle. When the insufficient power generation curve is negative, the photovoltaic power generation of the parking shed charges the energy storage device in the standby state. When the insufficient power generation curve is positive, the working states of the two energy storage devices are switched.

[0057] Figure 6 It is a flowchart of calculating the dynamic charging price based on the power grid supply curve of the present invention.

[0058] Figure 7 It is a flowchart of sending the dynamic charging price to the user terminal of the present invention. Detailed implementation manners

[0059] The following will describe in detail the embodiments of the present invention. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present invention, and should not be construed as a limitation of the present invention.

[0060] First embodiment

[0061] Please refer to Figures 1 to 7 , the present invention provides a photovoltaic energy storage DC intelligent microgrid monitoring and management method, including:

[0062] S101 Predict the predicted photovoltaic power generation of the parking shed for a period of time in the future based on weather forecast parameters and historical power generation data;

[0063] The specific steps include:

[0064] S201 Monitor the photovoltaic power generation and the status of the energy storage device to obtain historical power generation data;

[0065] To accurately predict the photovoltaic power generation, it is necessary to monitor the photovoltaic system of the parking shed in real time. This includes, but is not limited to, recording the electricity generated by the solar panels, the status of the energy storage device (such as a battery), and other relevant variables. Through sensors installed on the photovoltaic system, this information can be continuously obtained and stored as part of the historical power generation data.

[0066] S202 Obtain meteorological data, where the meteorological data includes temperature, humidity, wind speed, cloud cover, and solar radiation;

[0067] It is crucial to obtain the latest weather forecast information from meteorological service providers or local weather stations. These meteorological data should cover multiple factors affecting solar radiation, such as temperature, humidity, wind speed, cloud cover, and the directly measured solar radiation intensity, etc. Since weather conditions directly affect the conversion efficiency of solar energy, accurate meteorological data is very important for improving the prediction accuracy.

[0068] S203 Perform time alignment and parallel processing on the historical power generation data and the meteorological data;

[0069] Next is the preprocessing of the collected historical power generation data and meteorological data. This step involves aligning the data from different sources according to the time stamp to ensure that each power generation record has a corresponding description of meteorological conditions. In addition, parallel processing is also required to eliminate existing outliers or missing values, ensuring the consistency and integrity of the data set and providing high-quality data support for subsequent modeling.

[0070] S204 Plot the ACF and PACF graphs to determine the autoregressive term, the order of differencing, and the moving average term parameters in the ARIMA model;

[0071] To determine the most suitable statistical model structure for prediction, the autocorrelation function (ACF) graph and the partial autocorrelation function (PACF) graph are plotted at this stage. By observing these graphs, the important parameters in the ARIMA (AutoRegressive Integrated Moving Average) model - the autoregressive term (p), the order of differencing (d), and the moving average term (q) - can be initially judged. This process helps to find an optimal model configuration that can capture both the time series trend and the characteristics of random fluctuations.

[0072] S205 constructs an ARIMA model using the selected autoregressive terms, differencing order, and moving average term parameters;

[0073] Based on the parameter estimation values obtained in the previous step, the ARIMA model can now be formally constructed. That is, set the specific values of the three key parameters p, d, and q to define a complete mathematical framework to simulate the time series characteristics of photovoltaic power generation.

[0074] S206 trains the ARIMA model using historical data with the maximum likelihood estimation method;

[0075] After having the constructed ARIMA model, the next step is to train it using the historical data accumulated before. Here, the maximum likelihood estimation method (MLE) is adopted. This is a method widely used in parameter estimation, which can help us find a set of model parameters that maximize the probability of the observed data. Through this process, the ARIMA model gradually learns and adapts to the operation rules of the photovoltaic power generation system in the parking shed.

[0076] S207 makes a prediction of the photovoltaic power generation in the next few days using the trained ARIMA model to obtain the predicted photovoltaic power generation of the parking shed.

[0077] Finally, use the well-trained ARIMA model to make a prediction of the photovoltaic power generation of the parking shed in the next few days. The result output by this step is the predicted photovoltaic power generation of the parking shed we need. This prediction information can be used to plan energy scheduling in advance, optimize energy storage strategies, or adjust load distribution, ultimately achieving more efficient energy management and utilization.

[0078] S102 obtains the historical data of electric vehicle charging in the park and predicts the charging curve based on the historical data;

[0079] The specific steps include:

[0080] S301 collects the historical data of electric vehicle charging;

[0081] A comprehensive data collection mechanism needs to be established to record the charging activities of all electric vehicles in the park. This includes, but is not limited to, information such as the timestamp, charging amount, charging duration, and charging station location of each charge. In addition, data from different charging devices need to be integrated to ensure data integrity and consistency. To ensure data quality, abnormal or incomplete records should be regularly checked and cleaned. This stage is the basis of the entire prediction process, and high-quality data collection is crucial for subsequent analysis.

[0082] S302 Extract charging behavior features from the historical data of electric vehicle charging to obtain a charging training dataset. The charging behavior features include date, holiday flag, time period, total daily charging amount, and average charging amount.

[0083] Next, extract useful charging behavior features from the collected historical data of electric vehicle charging. These features include not only time-related factors such as specific dates, whether it is a holiday, and the time period of the day, but also quantitative indicators such as the total daily charging amount and the average charging amount. Through this feature engineering, the original data can be converted into a structured and easy-to-analyze form, namely the so-called charging training dataset. This dataset will be used as the input of a machine learning model to help identify charging patterns and trends.

[0084] S303 Train a Prophet model based on the charging training dataset.

[0085] Prophet is a powerful tool for time series prediction developed by Facebook, especially suitable for dealing with data with strong seasonal patterns. At this stage, we will use the previously prepared charging training dataset to train the Prophet model. Specifically, the Prophet model can automatically detect and adapt to various periodic and aperiodic changes, such as weekly, monthly, or even annual change patterns, and can also consider the impact of special events (such as holidays) on charging behavior. During the training process, the model will learn the laws of charging behavior based on the provided historical data, providing a basis for future predictions.

[0086] S304 Use the trained Prophet model to predict the charging curve.

[0087] Use the trained Prophet model to predict the charging curve for a period of time in the future. This prediction result will show the expected daily charging amount change trend, including peak hours, valley hours, and the distribution of the total charging amount. Such prediction information can help park managers plan power supply in advance, reasonably arrange the maintenance and upgrade of charging facilities, and can also provide more accurate charging suggestions for users, such as recommending the best charging time and location, thereby improving user experience and satisfaction.

[0088] S103 Calculate the insufficient power generation curve based on the charging curve and the predicted photovoltaic power generation of the parking shed.

[0089] The specific steps include:

[0090] S401 Align the timestamps of the data of the charging curve and the photovoltaic power generation prediction.

[0091] First, ensure that the data of the charging curve and the predicted photovoltaic power generation have the same time resolution (e.g., hourly or minute - by - minute), which is crucial for subsequent accurate analysis. If the time intervals of the two data sets are different, interpolation or other methods are required to unify the time granularity.

[0092] Next, strictly align the data of the charging curve and the predicted photovoltaic power generation according to the timestamps. This means finding the exact moment corresponding to each data point and ensuring that they accurately match on the same time axis. This step involves dealing with time zone differences, adjusting date formats, etc., to ensure the consistency of all timestamps. In addition, any time offset issues that occur need to be resolved to ensure the synchronization of the two sets of data without error.

[0093] S402 Integrate the data of the charging curve and the predicted photovoltaic power generation into the same table;

[0094] Once the timestamp alignment is completed, the next step is to create a new table containing charging demand and predicted photovoltaic power generation information. This table should include, but not be limited to, the following fields: timestamp, charging demand, predicted photovoltaic power generation value. In this way, the charging demand and photovoltaic output at the same time point can be intuitively compared.

[0095] During the integration process, the newly created data table should be carefully checked to ensure that there are no missing or duplicate records, and that all numerical values are within a reasonable range. For outliers or missing values, appropriate measures should be taken for correction or filling to maintain the integrity and accuracy of the data.

[0096] S403 For each time point, calculate the difference between the charging demand and the photovoltaic power generation at that moment, and finally obtain the insufficient power generation curve.

[0097] For each time point after integration, calculate the difference between the charging demand at that moment and the predicted photovoltaic power generation value. Specifically, subtract the predicted photovoltaic power generation value from the charging demand. A positive number indicates that there is a power gap during this period, that is, the charging demand exceeds the photovoltaic power generation; a negative number means that the photovoltaic power generation is excessive.

[0098] Based on the above calculation results, draw the insufficient power generation curve, which clearly shows the power supply - demand balance state at each time point. When the difference is positive, it indicates that the photovoltaic power generation during this period cannot fully meet the charging demand, forming "insufficient power generation". This curve is crucial for understanding and planning the power supply in the park. It can help managers identify peak load periods and make corresponding adjustments in advance, such as increasing other energy sources, optimizing charging arrangements, etc.

[0099] Finally, based on the insufficient power generation curve, further in-depth analysis can be carried out. For example, identifying long-existing power gap patterns, evaluating the efficiency of existing photovoltaic power generation facilities, and exploring improvement strategies. At the same time, this information can also be used to guide future infrastructure investment decisions, such as whether to expand the photovoltaic system or introduce energy storage solutions to improve the self-sufficiency of the park.

[0100] S104 classifies the energy storage device into two states: standby and charging. In the charging state, it is used to charge the electric vehicle. When the value of the insufficient power generation curve is negative, the photovoltaic power generation in the parking shed charges the energy storage device in the standby state. When the value of the insufficient power generation curve is positive, the working states of the two energy storage devices are switched.

[0101] The specific steps include:

[0102] S501 sets the initial state of the energy storage device.

[0103] First, clarify the two working states of the energy storage device: standby state and charging state.

[0104] Standby state: The energy storage device is in the standby mode and can receive excess electric energy for charging at any time.

[0105] Charging state: The energy storage device directly participates in the charging process of the electric vehicle and provides the required power to the electric vehicle.

[0106] Next, set the initial state of the energy storage device. Usually, the energy storage device can be set to the standby state when the system starts, so as to be able to immediately respond to any excess photovoltaic power generation. This configuration helps to maximize the utilization of renewable energy and ensure that the energy storage device is always in the best prepared state.

[0107] S502 When the insufficient power generation in the insufficient power generation curve is negative, switch the energy storage device from the charging state to the standby state, and use the excess photovoltaic power generation to charge the energy storage device in the standby state.

[0108] The system continuously monitors the insufficient power generation curve. When it is found that the value is negative (that is, the photovoltaic power generation exceeds the current charging demand), it indicates that there is excess electric energy available for storage.

[0109] At this time, switch the energy storage device from the charging state to the standby state so that they can receive and store the excess photovoltaic power generation. The specific operations include:

[0110] Stop charging the electric vehicle (if there is a charging task in progress), and switch the energy storage device to the standby mode.

[0111] Activate the charging interface and start using the excess photovoltaic power generation to charge the energy storage device.

[0112] Monitor the charging progress to ensure that the energy storage device is not overcharged and record the charging data for subsequent analysis

[0113] S503 When the deficit power generation curve is positive, switch the energy storage device from the standby state to the charging state and release power from the energy storage device to supplement the charging demand.

[0114] The system continues to track the deficit power generation curve. Once it detects that this value becomes positive (i.e., the charging demand exceeds the photovoltaic power generation), it means that the photovoltaic system in the park cannot meet all charging requests alone.

[0115] In this case, immediately switch the energy storage device back from the standby state to the charging state. Specific measures include:

[0116] Activate the discharge function of the energy storage device and start supplying power to the electric vehicle.

[0117] Monitor the power output to ensure stable and safe supply of the required power, while avoiding over-discharge that affects the lifespan of the energy storage device.

[0118] Adjust the output power in real time and flexibly regulate the power supply of the energy storage device according to the actual charging demand.

[0119] Throughout the process, the system should maintain dynamic management, continuously evaluate the changes in the deficit power generation curve, and react in a timely manner. If it is predicted that the future power gap will widen, early warnings can also be issued and preventive measures can be taken, such as restricting non-critical loads or guiding users to charge during off-peak hours

[0120] S105 When the deficit power generation curve is positive, generate a grid supply curve based on the deficit power generation curve;

[0121] When the deficit power generation curve is positive, it indicates that the photovoltaic power generation and energy storage are insufficient to meet the demand. At this time, power needs to be supplemented from the external grid. The specific steps are as follows: First, determine the time resolution (such as every 15 minutes), and then calculate the additional power demand by accumulating the positive values of the deficit power generation curve in each time interval. Then, consider the power supply capacity of the grid and market price fluctuations, select the time period with the best cost-effectiveness to purchase electricity, and integrate any applicable subsidy policies or agreement terms. Based on these analyses, formulate a smooth grid supply curve showing the expected electricity quantity to be obtained from the grid at each time period. Finally, the system will execute power procurement according to this curve and monitor and adjust in real time to ensure supply-demand balance and optimal operation.

[0122] S106 Calculate the dynamic charging price based on the grid supply curve;

[0123] The specific steps include:

[0124] S601 Obtain a curve reflecting the grid power supply in different time periods;

[0125] First, accurate data needs to be obtained from the power supplier or the internal power monitoring system of the park. These data should be able to reflect the grid power supply in different time periods, including but not limited to information such as real-time power output and historical electricity consumption records. Ensure that the time resolution of the data is high enough (such as every 15 minutes or shorter) to capture the subtle changes in power demand.

[0126] Next, based on the insufficient power generation curve (positive part) and the obtained grid power supply data, construct an accurate grid supply curve. This curve will show the additional power that the grid needs to provide at each time point when the photovoltaic power generation is insufficient to meet the charging demand. For each positive gap, there should be a corresponding grid power supply value to ensure a direct correspondence between the two.

[0127] Verify the generated grid supply curve to ensure it conforms to the actual situation. If abnormal fluctuations or unreasonable peaks are found, the reasons should be investigated and appropriate adjustments should be made. In addition, factors such as seasonal changes and special events that affect the grid power supply should also be considered to improve the accuracy of the prediction.

[0128] S602 Calculate the actual cost of purchasing electricity from the grid at each time point according to the grid supply curve and the electricity cost structure;

[0129] Deeply understanding the electricity cost structure is the key first step. This usually involves the following aspects:

[0130] Basic electricity charge: The fixed electricity price provided by the grid.

[0131] Time-of-use rate: Different electricity prices set according to different time periods of the day (such as peak, flat, and off-peak).

[0132] Capacity charge: The charge related to the maximum power demand.

[0133] Transmission and distribution charge: The charge generated during the power transmission and distribution process.

[0134] Based on the above cost structure and the grid supply curve, calculate the actual cost for the grid power supply at each time point. This means comprehensively considering all relevant charges and allocating them to each time point. For example, during peak hours, due to the higher time-of-use rate, even the same power supply generates a higher actual cost.

[0135] S603 Calculate the dynamic charging price by combining the actual cost and the additional cost, where the additional cost includes the peak-hour surcharge and the carbon emission cost.

[0136] In addition to the actual costs, additional costs need to be considered, including:

[0137] Peak-hour surcharge: To encourage users to avoid charging during peak hours, a certain percentage of surcharge can be added during this period.

[0138] Carbon emission cost: Considering environmental protection factors, the corresponding carbon emission cost can be calculated based on the power grid power structure (such as the proportion of thermal power) and incorporated into the final price.

[0139] Establish a dynamic pricing model that combines actual costs and additional costs to calculate the dynamic charging price at each time point. This model should respond flexibly to changes in market conditions, such as fluctuations in electricity market prices and updates in weather forecasts, and be able to automatically adjust to reflect the latest cost information.

[0140] Ensure that the dynamic charging price mechanism has sufficient transparency so that users can clearly understand the reasons for price changes. Real-time price information and suggestions can be provided to users through applications or websites to help them choose the most cost-effective charging periods. In addition, incentives such as coupons or point rewards can be provided to further encourage user participation.

[0141] S107 sends the dynamic charging price to the user side.

[0142] The specific steps include:

[0143] S701 obtains the dynamic charging price data;

[0144] First, ensure that the dynamic charging price data has been accurately calculated and updated by the pricing module of the system. This includes the actual costs and additional costs at the time point obtained from step S106, and the latest dynamic charging price is obtained in real time through the database or API interface.

[0145] S702 obtains the location information of the user and sends the dynamic charging price data to the user side when the user is within the preset range.

[0146] To determine whether the user is within the preset range, a reliable user location service needs to be integrated. This can be achieved in various ways:

[0147] Mobile application: If the user accesses the charging facility through a smartphone application, the precise location can be directly obtained using the GPS function of the mobile phone.

[0148] Bluetooth beacon: Install Bluetooth beacons in the park, and when the user enters a specific area, the user location is automatically identified through Bluetooth connection.

[0149] Wi-Fi positioning: Utilize the Wi-Fi network in the park to estimate the approximate location of the user based on the signal strength.

[0150] RFID / NFC technology: For fixed charging stations, RFID or NFC tags can be used to confirm whether the user is approaching the charging pile.

[0151] Clearly define which areas are considered "preset ranges". This is usually a geofence set around the charging station or parking lot. Only when the user enters this area will they receive notifications of dynamic charging prices. The geofence can be flexibly adjusted in size and shape according to the actual situation to cover all necessary charging facilities.

[0152] After confirming that the user is within the preset range, start the message push mechanism and send the dynamic charging price information to the user terminal. This can be achieved in the following ways:

[0153] Push notification: Directly send a push notification to the user's mobile device, including the latest dynamic charging price.

[0154] SMS / Email: For users who have not enabled push notifications, reminders can be sent via SMS or email.

[0155] In-application prompt: When the user opens the relevant application, a pop-up window displays the current price information.

[0156] Finally, establish a user feedback channel to collect opinions and suggestions on the sending effect of dynamic charging price information. By analyzing user behavior data, continuously optimize the sending strategy and service quality to ensure the effectiveness and timeliness of information transmission.

[0157] Second Embodiment

[0158] The present invention provides a photovoltaic energy storage DC intelligent microgrid monitoring and management system, including a power generation prediction module, a charging amount prediction module, a shortage of power generation calculation module, an energy storage allocation module, a grid power supply module, a power price calculation module, and a notification module; the power generation prediction module is used to predict the predicted photovoltaic power generation of a parking shed in the future for a period of time based on weather forecast parameters and historical power generation data; the charging amount prediction module is used to obtain the historical data of electric vehicle charging in the park and predict the charging curve based on the historical data; the shortage of power generation calculation module is used to calculate the shortage of power generation curve based on the charging curve and the predicted photovoltaic power generation of the parking shed; the energy storage allocation module is used to divide the energy storage device into two states: standby and charging. When in the charging state, it is used to charge the electric vehicle. When the value of the shortage of power generation curve is negative, the photovoltaic power generation of the parking shed charges the energy storage device in the standby state. When the value of the shortage of power generation curve is positive, the working states of the two energy storage devices are switched; the grid power supply module is used to generate a grid supply curve based on the shortage of power generation curve when the shortage of power generation curve is positive; the power price calculation module is used to calculate the dynamic charging price based on the grid supply curve; the notification module is used to send the dynamic charging price to the user terminal.

[0159] In this embodiment, the power generation prediction module can accurately predict the photovoltaic power generation of the parking shed within a certain period in the future by using an algorithm in combination with weather forecast parameters and historical power generation data. This enables the system to plan energy distribution in advance and ensure the efficient use of energy. The charging amount prediction module estimates the future charging demand curve by collecting the historical data of electric vehicle charging in the park and constructing a prediction model based on this data. This function helps the system arrange appropriate power supply in advance and avoid overloading during peak power periods. When the predicted power generation is insufficient to meet the charging demand, the insufficient power generation calculation module intervenes. By comparing the charging demand curve with the predicted power generation, it calculates the additional power that needs to be supplemented, that is, the insufficient power generation curve. This module is crucial for maintaining the stable operation of the system, ensuring that sufficient power can be provided for electric vehicles even under poor lighting conditions. The energy storage distribution module manages the working state of energy storage devices. Under normal circumstances, a part of the energy storage devices is in a standby state, and the other part is used to directly charge electric vehicles. If the insufficient power generation curve shows that the current power generation is excessive (negative value), then the excess photovoltaic power generation will be used to charge the energy storage devices in the standby state; conversely, if there is insufficient power generation (positive value), the role of the energy storage devices will be switched from charging to discharging to supplement the shortage of power. The grid power supply module plays a role when the power generation is insufficient to cover the demand. It can formulate a grid power supply strategy according to the insufficient power generation curve and generate a grid supply curve to ensure continuous and stable power supply in the park. The electricity price calculation module dynamically adjusts the charging price based on the grid supply curve. It takes into account the real-time electricity cost, including but not limited to factors such as peak-hour electricity premium, and thus calculates a reasonable dynamic charging price. This approach not only helps to fairly allocate the electricity cost but also encourages users to choose more economical charging periods. The notification module, as part of the user interface, is responsible for communicating the latest dynamic charging price and other relevant information to the end users. This enables users to timely understand the changes in electricity bills, reasonably arrange the charging time, and also helps to improve the user experience and service quality.

[0160] In summary, through the close cooperation among various modules, this photovoltaic energy storage DC intelligent microgrid monitoring and management system realizes the refined management of power resources in the park, improves the energy utilization efficiency, and promotes the development of green travel modes.

[0161] The above-disclosed is only a preferred embodiment of the present invention. Of course, it cannot be used to limit the scope of the rights of the present invention. Those of ordinary skill in the art can understand all or part of the processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present invention still fall within the scope covered by the invention.

Claims

1. A photovoltaic energy storage DC intelligent microgrid monitoring and management method, It is characterized in that it includes: predicting the photovoltaic power generation of the parking shed in the future period based on weather forecast parameters and historical power generation data; obtaining the historical data of electric vehicle charging in the park and predicting the charging curve based on the historical data; calculating the insufficient power generation curve based on the charging curve and the predicted photovoltaic power generation of the parking shed; dividing the energy storage device into two states: standby and charging. When in the charging state, it is used to charge electric vehicles. When the value of the insufficient power generation curve is negative, the photovoltaic power generation of the parking shed charges the energy storage device in the standby state. When the value of the insufficient power generation curve is positive, the working states of the two energy storage devices are switched; when the insufficient power generation curve is positive, generating a power grid supply curve based on the insufficient power generation curve; calculating the dynamic charging price based on the power grid supply curve; sending the dynamic charging price to the user side.

2. A method for monitoring and managing a photovoltaic energy storage DC intelligent microgrid according to claim 1, characterized in that the specific steps of predicting the photovoltaic power generation of the parking shed in the future period based on weather forecast parameters and historical power generation data include: monitoring the photovoltaic power generation and the state of the energy storage device to obtain historical power generation data; obtaining meteorological data, where the meteorological data includes temperature, humidity, wind speed, cloud cover, and solar radiation; performing time alignment and parallel processing on the historical power generation data and the meteorological data; drawing ACF and PACF diagrams to determine the autoregressive term, differencing order, and moving average term parameters in the ARIMA model; using the selected autoregressive term, differencing order, and moving average term parameters to construct an ARIMA model; training the ARIMA model using the maximum likelihood estimation method with historical data; using the trained ARIMA model to predict the photovoltaic power generation in the next few days to obtain the predicted photovoltaic power generation of the parking shed.

3. A method for monitoring and managing a photovoltaic energy storage DC intelligent microgrid according to claim 2, characterized in that the specific steps of obtaining the historical data of electric vehicle charging in the park and predicting the charging curve based on the historical data include: collecting the historical data of electric vehicle charging; extracting charging behavior characteristics from the historical data of electric vehicle charging to obtain a charging training data set, where the charging behavior characteristics include date, holiday flag, time period, daily total charging amount, and average charging amount; training a Prophet model based on the charging training data set; using the trained Prophet model to predict the charging curve.

4. A method for monitoring and managing a photovoltaic energy storage DC intelligent microgrid according to claim 3, characterized in that the specific steps of calculating the insufficient power generation curve based on the charging curve and the predicted photovoltaic power generation of the parking shed include: aligning the timestamps of the data of the charging curve and the photovoltaic power generation prediction; integrating the data of the charging curve and the photovoltaic power generation prediction into the same table; for each time point, calculating the difference between the charging demand and the photovoltaic power generation at that moment to finally obtain the insufficient power generation curve.

5. A method for monitoring and managing a photovoltaic energy storage DC intelligent microgrid according to claim 4, characterized in that The energy storage device is divided into two states: standby and charging. When in the charging state, it is used to charge the electric vehicle. When the deficit power generation curve is negative, the photovoltaic power generation of the parking shed charges the energy storage device in the standby state. When the deficit power generation curve is positive, the specific steps for switching the working states of the two energy storage devices are as follows: Set the initial state of the energy storage device; When the deficit power generation in the deficit power generation curve is negative, switch the energy storage device from the charging state to the standby state, and use the excess photovoltaic power generation to charge the energy storage device in the standby state; When the deficit power generation curve is positive, switch the energy storage device from the standby state to the charging state, and release power from the energy storage device to supplement the charging demand.

6. A photovoltaic energy storage DC intelligent microgrid monitoring and management method according to claim 5, characterized in that The specific steps for calculating the dynamic charging price based on the grid supply curve include: Obtain the curve reflecting the grid power supply volume in different time periods; According to the grid supply curve and the power cost structure, calculate the actual cost of purchasing electricity from the grid at each time point; Calculate the dynamic charging price by combining the actual cost and the additional cost, where the additional cost includes the peak period surcharge and the carbon emission cost.

7. A photovoltaic energy storage DC intelligent microgrid monitoring and management method according to claim 6, characterized in that The specific steps for sending the dynamic charging price to the user terminal include: Obtain the dynamic charging price data; Obtain the location information of the user, and send the dynamic charging price data to the user terminal when the user is within the preset range.

8. A photovoltaic energy storage DC intelligent microgrid monitoring and management system, which is applied to the photovoltaic energy storage DC intelligent microgrid monitoring and management method described in any one of claims 1 to 7, and is characterized in that, Including a power generation prediction module, a charging amount prediction module, a deficit power generation calculation module, an energy storage allocation module, a grid power supply module, a power price calculation module, and a notification module; The power generation prediction module is used to predict the predicted photovoltaic power generation of the parking shed in the next period based on weather forecast parameters and historical power generation data; The charging amount prediction module is used to obtain the historical data of electric vehicle charging in the park and predict the charging curve based on the historical data; The deficit power generation calculation module is used to calculate the deficit power generation curve based on the charging curve and the predicted photovoltaic power generation of the parking shed; The energy storage allocation module is used to divide the energy storage device into two states: standby and charging. When in the charging state, it is used to charge the electric vehicle. When the value of the deficit power generation curve is negative, the photovoltaic power generation of the parking shed charges the energy storage device in the standby state. When the value of the deficit power generation curve is positive, switch the working states of the two energy storage devices; The grid power supply module is used to generate a grid supply curve based on the deficit power generation curve when the deficit power generation curve is positive; The power price calculation module is used to calculate the dynamic charging price based on the grid supply curve; The notification module is used to send the dynamic charging price to the user terminal.

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