Transformer flexible capacity expansion method and system based on historical operation data

Abstract

The invention discloses a transformer flexible capacity expansion method and system based on historical operation data. The method comprises the steps that the electricity consumption of subordinate load equipment of a distribution transformer is predicted based on the historical operation data; predicting the generating capacity of subordinate photovoltaic power generation equipment of the distribution transformer based on the historical operation data; calculating the residual capacity according to the rated capacity of the distribution transformer, the total load amount and the total photovoltaic grid-connected amount; when the residual capacity is insufficient, the energy storage equipment is controlled to discharge or the flexible interconnection device is used for mutual assistance; and when the residual capacity is surplus, controlling the energy storage equipment to charge or supply power to the adjacent transformer. According to the invention, the low-voltage flexible interconnection device is installed between the two transformers, the peak time of the superposed transformer load is predicted and analyzed according to the transformer load and the historical data of the operation of each device, the edge side energy management strategy of demand consideration is synthesized, and the efficient allocation and transfer of power resources are realized through the flexible mutual aid technology. Therefore, the power utilization requirement of flexible capacity expansion of the transformer area is met.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network optimization control, and in particular relates to a transformer flexible expansion method and system based on historical operation data. Background Art

[0002] With the development of society and the improvement of people's living standards, the demand for electricity is increasing. Many transformers need to reserve a certain amount of spare capacity when they are designed, but with the frequent occurrence of extreme weather and the continuous progress of electricity substitution, the power load is getting larger and larger, and the original capacity is insufficient. For example, in the field of public power distribution, the power consumption and the maximum power load are growing rapidly, which has brought about the demand for new capacity expansion of distribution transformers. The batch opening of seasonal load equipment and the sudden increase in power load during peak power consumption periods have caused the transformer to be overloaded. The production, commissioning, load aging and aging of factory modules occupy a large amount of distribution capacity. In addition, the rapid development of new energy vehicles has also led to an increase in the number of charging piles, further increasing the demand for transformer expansion.

[0003] Most of the existing expansion technologies are based on the hard expansion of transformers. According to the collected transformer operation data, the transformers that need to be expanded are screened under the operating state, and the hardware expansion is carried out. Although the expansion of some distribution transformers can maximize the performance gain of the entire area, reduce the volatility of the area, and ensure the safety of the area, there are practical disadvantages such as long process cycle, high cost, and poor flexibility. It is impossible to solve the problem of insufficient transformer capacity during short-term peak moments in a short time and at a low cost. The existing flexible expansion relies too much on energy storage equipment. Once the energy storage equipment fails, the flexible expansion will be paralyzed, and the problem of transformer expansion cannot be truly solved. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a transformer flexible expansion method and system based on historical operation data. The technical solution provided by the present invention is as follows:

[0005] A transformer flexible capacity expansion method based on historical operation data, for two adjacent distribution transformers connected by a flexible interconnection device, wherein the flexible interconnection device is used to transmit power between different distribution transformers through a low-voltage power flow mutual aid device, comprising the following steps:

[0006] S1. Based on historical operation data, predict the power consumption of the load equipment under each distribution transformer to obtain the total energy load under each distribution transformer at the next moment;

[0007] S2. Based on the historical operation data, the power generation of the photovoltaic power generation equipment under each distribution transformer is predicted to obtain the photovoltaic power generation power under each distribution transformer at the next moment;

[0008] S3. Calculate the remaining capacity of the distribution transformer at the next moment according to the rated capacity of the distribution transformer, the total energy load of the distribution transformer at the next moment, and the total amount of photovoltaic grid-connected power;

[0009] S4. If the remaining capacity of a distribution transformer at the next moment is less than the set threshold, the power demand of the distribution transformer area is met by controlling the energy storage device under the distribution transformer to discharge and / or by flexibly transferring power from the adjacent distribution transformer through the flexible interconnection device;

[0010] S5. If the remaining capacity of a distribution transformer at the next moment is greater than the set threshold, the distribution transformer's subordinate energy storage device is controlled to charge and / or the adjacent distribution transformer is flexibly transferred to supply power through the flexible interconnection device to fully utilize the distribution transformer's load capacity. The energy storage charging and discharging can also be considered in combination with electricity charges from an economic perspective.

[0011] Further, step S1 includes:

[0012] Data collection: Install smart meters or remote monitoring equipment under each distribution transformer to collect historical power consumption data of various types of power-consuming equipment, including power, in real time, as well as corresponding weather, season and holiday information;

[0013] Data preprocessing: Use cleaning and denoising techniques on the collected historical electricity consumption data to remove abnormal data points;

[0014] Establish a load forecasting model: Based on historical electricity consumption data and corresponding weather, season and holiday information, build and train a machine learning model to predict future electricity loads;

[0015] Load forecasting: Based on the model prediction results, the total power consumption forecast of each distribution transformer at the next moment will be generated, and this value will be used as a reference for subsequent flexible scheduling.

[0016] Furthermore, the load forecasting model adopts the GBDT model. The mean absolute error is used to evaluate the model quality during GBDT model training. If 1-e mae / CL max ≥85%, the model meets the application requirements, e mae Represents the calculated mean absolute error, CL max is the maximum power load value in the data set.

[0017] Further, step S2 includes:

[0018] Data collection: Collect the historical power generation data of the photovoltaic system and the corresponding meteorological data. The historical power generation data includes daily power generation, instantaneous power, daily power generation peak, and average power generation efficiency. The meteorological data includes solar irradiance, temperature, sunshine duration, wind speed, humidity, and cloud cover.

[0019] Data preprocessing: Use cleaning and denoising techniques on the collected historical power generation data to remove abnormal data points, and align the historical power generation data with the meteorological data at the same time interval;

[0020] Establishing a photovoltaic prediction model: Building and training a photovoltaic prediction model based on historical power generation data and meteorological data to predict power generation at future times;

[0021] Power generation forecast: The meteorological data is input into the trained photovoltaic forecast model to generate the predicted value of future photovoltaic power generation.

[0022] Furthermore, the photovoltaic prediction model includes a physical power generation model based on the physical characteristics of the photovoltaic system and a data-driven machine learning model; the physical power generation model calculates the theoretical power generation of the photovoltaic module by inputting meteorological data; the machine learning model includes ARIMA, SARIMA, random forest and deep learning network.

[0023] A transformer flexible capacity expansion system based on historical operation data, comprising:

[0024] Data acquisition module: responsible for collecting the operating data of the distribution transformer and its subordinate equipment and transmitting it to the edge gateway module, including the power consumption data of the load equipment, the power generation data of the photovoltaic power generation equipment, the charging and discharging status of the energy storage equipment, and the meteorological data;

[0025] Data preprocessing module: used to clean, denoise and detect anomalies of the collected raw data to ensure the accuracy and consistency of the data and filter out faulty and incomplete data;

[0026] Load forecasting module: Based on historical operation data, predict the power load of the distribution transformer in the next period, generate a load demand curve, and provide it to the subsequent management and optimization platform for analysis and decision-making;

[0027] Photovoltaic power generation prediction module: Based on the historical power generation data and weather forecast information of photovoltaic power generation equipment, it predicts future power generation to ensure that photovoltaic resources can reasonably participate in load scheduling;

[0028] Energy storage management module: used to manage the charging and discharging process of energy storage equipment, ensuring that the energy storage equipment can be discharged in time to relieve load pressure during peak load, and charged to reserve electricity during low load;

[0029] Flexible interconnection device control module: used to realize flexible allocation of power resources between two adjacent transformers through flexible interconnection devices. When a distribution transformer is overloaded, the module will transfer power from the adjacent distribution transformer to reduce the load;

[0030] Edge gateway module: performs real-time data processing and short-term prediction on edge devices close to the transformer, and uploads the processed data to the management platform, reducing computing and communication delays and ensuring the timeliness and accuracy of load regulation;

[0031] Management and optimization platform: As the central command module of the system, it receives all data from the edge gateway module, performs global analysis and policy optimization, generates scheduling plans and sends them to each edge node.

[0032] Furthermore, the photovoltaic power generation prediction module is realized by establishing a photovoltaic power generation prediction model based on data drive or physical characteristics of the photovoltaic system, and communicates with the meteorological data service interface in real time to timely obtain meteorological data including solar irradiance, temperature, sunshine duration, wind speed, humidity, and cloud cover.

[0033] Furthermore, the energy storage management module dynamically adjusts the charging and discharging behavior of the energy storage device based on load forecasting and real-time power demand; an economic optimization algorithm is provided within the module to optimize the charging timing and discharging strategy through price signals and load demand.

[0034] Furthermore, the control modes of the flexible interconnection device control module include load balancing mode, microgrid economic mode and safe supply mode:

[0035] The load balancing mode refers to detecting the load rate difference of the transformers on both sides of the flexible interconnection device. When the load rate difference of the transformers on both sides is less than the set threshold value due to the mutual assistance of the power flow, the load balancing mode control is started;

[0036] The microgrid economic model refers to the interconnection of microgrids or substations with photovoltaic power generation equipment and energy storage equipment, taking into full consideration the differences in the distributed source-storage status and peak-valley electricity prices of the two substations, and building an optimized adjustment scheduling strategy with the minimum electricity purchase cost as the goal. By controlling the flexible interconnection device to control the mutual assistance time of the flow and the time of energy storage charging and discharging, the purpose of saving electricity bills can be achieved.

[0037] The safe power supply mode means that when one of the substations loses power or fails, the load is transferred in real time through flexible interconnection devices to help the substation restore power.

[0038] Furthermore, the system predicts the load conditions in future time periods through the load prediction module and generates a load demand curve. At the same time, the photovoltaic power generation prediction module predicts the future photovoltaic power generation based on historical power generation data and weather forecast information; the system summarizes and analyzes the rated capacity, real-time total load, and total photovoltaic grid-connected capacity of each transformer, and calculates the remaining capacity of the transformer; when the remaining capacity of the transformer is insufficient, the energy storage management module will dispatch the energy storage device to start discharging to supplement the load gap; if the energy storage device cannot fully meet the demand, the flexible interconnection device control module will allocate electricity from adjacent transformers for transfer to ensure the dynamic balance of the power load; when the load is low, the system will control the energy storage device to charge, or transfer excess electricity to adjacent transformers through the flexible interconnection device.

[0039] Compared with the prior art, the present invention has at least the following beneficial effects:

[0040] 1. Low cost: The biggest advantage of flexible capacity expansion technology is its low cost. By tapping into the peak energy consumption of the load during actual transformer operation, the existing transformer load capacity can be maximized to meet the transformer capacity expansion requirements without changing the existing cable laying.

[0041] 2. Multi-faceted considerations: While considering the flexible expansion of transformers, we also consider demand control during transformer operation, load balancing from the perspective of transformer life, and safety guarantees for transformer overload operation, and other strategic models to further promote the stable and reliable use of transformers in the park.

[0042] 3. Flexible and adaptable operation: The entire system does not change the installation of the original equipment. Through the edge-side collection and computing gateway, the management strategy monitoring platform is set up, and the joint coordination module is deployed on the platform and edge side to complete multiple modes of transformer flexible expansion operation technology under different weather conditions, different holiday conditions, different equipment operating conditions, and different equipment fault status combinations. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0044] Figure 1 This is a schematic diagram of the hierarchical composition of subordinate equipment of an existing distribution transformer in a certain park provided by an embodiment of the present invention;

[0045] Figure 2 It is a schematic diagram of a flow chart of a transformer flexible capacity expansion method provided by an embodiment of the present invention;

[0046] Figure 3 It is a schematic diagram of the GBDT architecture provided by an embodiment of the present invention;

[0047] Figure 4 This is a comparison chart of the average load prediction value and the actual value of a charging station provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, other embodiments obtained by ordinary technicians in this field without making creative work are all within the scope of protection of the present invention.

[0049] Embodiment 1

[0050] This embodiment provides a transformer flexible capacity expansion method based on historical operation data.

[0051] like Figure 1 As shown in the figure, the hierarchical composition of the subordinate equipment of the existing distribution transformer in a typical park is given, including two distribution transformers, and a low-voltage power flow flexible interconnection device is set between the distribution transformers. The main purpose of the transformer flexible expansion method based on historical operation data is to achieve efficient allocation and transfer of power resources between two adjacent distribution transformers through a flexible interconnection device to meet the power demand of the transformer area and make full use of the capacity of photovoltaic power generation and energy storage equipment. Figure 2 As shown, the method mainly includes the following steps:

[0052] Step S1: Predict the power consumption of the load equipment under the distribution transformer based on historical operation data

[0053] 1. Data collection:

[0054] Install smart meters or remote monitoring equipment under each distribution transformer to collect historical data of various electrical equipment in real time, including electrical parameters such as power, voltage, and current.

[0055] The collected data should cover various typical operating conditions, such as seasonal load changes, differences in electricity consumption between holidays and non-holidays, and the impact of special operating conditions (such as equipment maintenance period or abnormal shutdown).

[0056] 2. Data preprocessing:

[0057] The collected historical electricity consumption data is pre-processed, and cleaning and denoising techniques are used to remove abnormal data points (such as short-term voltage fluctuations or momentary power outages caused by equipment failures).

[0058] Use algorithms such as moving average to smooth the data and eliminate the interference of short-term fluctuations on load forecasting to improve the accuracy of the forecasting model.

[0059] 3. Establish load forecasting model:

[0060] Based on historical data, machine learning algorithms such as time series models (such as ARIMA), regression models (such as GBDT), or deep learning algorithms (such as LSTM) are used to predict the power load at future times.

[0061] When training the model, time factors (such as daily, weekly, and monthly periodic fluctuations), equipment operation modes (the difference between continuous production equipment and intermittent equipment), and external factors (such as the impact of temperature changes on air conditioning loads) should be considered.

[0062] 4. Load forecasting application:

[0063] According to the model prediction results, the total power consumption forecast value of each distribution transformer at the next moment will be generated, and this value will be used as a reference for subsequent flexible scheduling.

[0064] Step S2: Predict the power generation of the photovoltaic power generation equipment under the distribution transformer based on historical operation data

[0065] 1. Data Collection

[0066] (1) Historical data of photovoltaic power generation equipment:

[0067] Collect historical power generation data of the photovoltaic system. The recommended sampling interval for data is 15 minutes or less. This includes daily power generation, instantaneous power, daily power generation peak, average power generation efficiency, etc. The data can be obtained through power generation data recorded by the photovoltaic inverter, or collected in real time through a remote monitoring system (such as a SCADA system).

[0068] (2) Acquisition of meteorological data:

[0069] Meteorological monitoring sensors such as solar irradiance and temperature are installed at photovoltaic power generation sites to collect meteorological data in real time.

[0070] Solar irradiance: reflects the intensity of solar energy resources and is the main influencing factor of photovoltaic power generation.

[0071] Temperature: Temperature changes will affect the conversion efficiency of photovoltaic modules. The power generation efficiency of photovoltaic modules decreases at high temperatures.

[0072] Sunshine duration: reflects the effective time that photovoltaic modules are exposed to sunlight, and is another important factor in calculating power generation.

[0073] Factors such as wind speed, humidity, and cloud cover: These data will have an indirect impact on power generation, especially cloud cover will affect changes in solar irradiance.

[0074] 2. Data preprocessing

[0075] (1) Data cleaning:

[0076] Process missing values ​​and outliers in historical data. If there are discontinuities or outliers in the photovoltaic power generation data (such as negative power generation, etc.), interpolation or mean filling is required. Filter or correct anomalies in meteorological data (such as extreme weather, sensor failure, etc.).

[0077] (2) Data alignment:

[0078] In order to synchronize the photovoltaic power generation data with the meteorological data, the two types of data need to be aligned at the same time interval. If the sampling intervals of meteorological data and power generation data are different, they can be aligned by interpolation or averaging. Ensure that the data is continuous in time series for subsequent time series analysis.

[0079] 3. Prediction model selection and training

[0080] In order to improve the accuracy of photovoltaic power generation prediction, a suitable model can be selected according to the amount and complexity of data. There are mainly two types of models to choose from:

[0081] (1) Physical model (based on the physical characteristics of the photovoltaic system):

[0082] Based on the photovoltaic conversion efficiency and temperature coefficient of solar cell modules, a physical power generation model is constructed. By inputting data such as solar irradiance, temperature, and sunshine duration, the theoretical power generation of photovoltaic modules is calculated. This model can accurately reflect the physical characteristics of photovoltaic systems and is suitable for smaller-scale photovoltaic systems, but its ability to handle complex weather changes is limited, especially when there is a large amount of data and the model is relatively simple.

[0083] (2) Machine learning model (data-driven model):

[0084] ARIMA (Autoregressive Integrated Moving Average Model): Applicable to situations where photovoltaic power generation has seasonality and periodicity, and short-term prediction can be performed by adjusting model parameters.

[0085] SARIMA (Seasonal ARIMA): Taking into account seasonal factors, a more accurate forecast of the fluctuation trend of photovoltaic power generation is made.

[0086] Random Forest: It builds multiple decision trees and combines meteorological and historical power generation data for prediction, which is suitable for processing multi-dimensional features.

[0087] LSTM (Long Short-Term Memory Network): In view of the characteristics of time series data, LSTM can capture the long-term dependency between historical power generation and future power generation, and is very suitable for processing large-scale data, especially nonlinear changes.

[0088] (3) Model training:

[0089] Based on historical power generation and meteorological data, the model is trained and hyperparameters are adjusted. The trained model can be fed with real-time or expected meteorological data to obtain power generation forecasts for future moments.

[0090] 4. Power generation forecast

[0091] The meteorological forecast data such as solar irradiance, temperature, and sunshine duration are input into the trained model to generate the predicted value of future photovoltaic power generation. The model will output the predicted value of photovoltaic power generation at a future time (such as the next hour or the next day) and generate a power generation curve. According to actual needs, hourly and daily power generation forecasts can be generated.

[0092] Step S3: Calculate the remaining capacity based on the rated capacity of the distribution transformer, the total load and the total amount of photovoltaic grid-connected

[0093] 1. Rated capacity and real-time calculation:

[0094] According to the rated capacity information on the transformer nameplate, the theoretical load-bearing capacity of each distribution transformer is obtained. Then, based on the total load and the total photovoltaic grid-connected amount predicted in steps S1 and S2, the real-time remaining capacity of each transformer is calculated using the following formula: remaining capacity = rated capacity - (total load - total photovoltaic grid-connected amount). If the remaining capacity is lower than the safety threshold, the transformer may be overloaded and immediate control measures must be taken.

[0095] 2. Real-time monitoring and analysis:

[0096] The load changes of each transformer are monitored in real time, and the data can be transmitted to the centralized management platform through the edge computing gateway. The platform will analyze the load conditions and automatically generate a response strategy.

[0097] Step S4: When the remaining capacity is insufficient, control the energy storage device to discharge or transfer power through the flexible interconnection device

[0098] 1. Discharge scheduling of energy storage equipment:

[0099] When the system detects that the remaining capacity of a distribution transformer is lower than the set threshold, it automatically dispatches the energy storage device under the transformer to start discharging. Energy storage devices such as lithium batteries and supercapacitors will gradually release electricity according to the predicted load gap to ensure the safe operation of the transformer.

[0100] The discharge process is automatically adjusted by the management system to ensure that the discharge speed matches the load gap, and real-time monitoring and feedback are carried out through the edge computing platform.

[0101] 2. Power supply dispatching of flexible interconnection devices:

[0102] If the energy storage equipment cannot completely make up for the load gap, the system will transfer the surplus power resources of the adjacent distribution transformers to the transformer with heavier load through the flexible interconnection device to relieve the overload pressure. The flexible interconnection device can transmit power between different transformers through low-voltage flow mutual assistance equipment to ensure the real-time and stability of dynamic load scheduling.

[0103] Step S5: When there is sufficient remaining capacity, control the energy storage device to charge or transfer power to the adjacent transformer

[0104] 1. Energy storage equipment charging scheduling:

[0105] When a transformer has excess capacity, the energy storage device will be dispatched to enter charging mode. The charging process will be optimized based on the predicted future load demand and electricity price fluctuations to ensure maximum economic benefits. The charging rate and time will be dynamically adjusted based on the predicted results of the future load curve to avoid unnecessary energy waste.

[0106] 2. Transfer power to adjacent transformers:

[0107] If adjacent transformers are in a high-load state and their own energy storage is insufficient, power resources will be automatically transferred through flexible interconnection devices to help adjacent transformers relieve pressure and achieve efficient scheduling and optimal allocation of power grid resources.

[0108] Embodiment 2

[0109] This embodiment provides a transformer flexible expansion method based on historical operation data. Different from the first embodiment, this embodiment adopts a gradient boosting decision tree (GBDT) algorithm in the link of predicting the power consumption of load equipment, and predicts the load of the load equipment in the next few hours and days through historical power consumption data and related weather, season, whether it is a holiday and other characteristics.

[0110] Specifically, historical electricity consumption data refers to the historical electricity consumption data of the previous ten hours or the previous ten days at the prediction time point; weather refers to the weather forecast data of the prediction date obtained based on the date, including temperature, wind speed, humidity, etc.; season refers to one of the four seasons of spring, summer, autumn and winter corresponding to the prediction date calculated based on the date; whether it is a holiday refers to whether the prediction date is a holiday calculated based on the date.

[0111] Load prediction is a typical machine learning regression prediction problem. The complete process of the algorithm includes four parts: data preprocessing, feature extraction, model training and model evaluation.

[0112] 1. Data preprocessing

[0113] Missing value filling

[0114] 2. Feature extraction

[0115] According to the description of the prediction features, corresponding features including historical electricity consumption data, weather, season, and whether it is a holiday are obtained for each time point that needs to be predicted.

[0116] 3. Model training

[0117] Using the above data preprocessing and feature extraction methods, construct training data sets and validation data sets. Use the training data sets to train the GBDT model. Figure 3 As shown in the figure, the core idea of ​​the GBDT algorithm is to combine multiple decision trees to form a powerful integrated model. During the training process, the GBDT algorithm iteratively builds a series of decision trees and fits the residuals of the previous round to reduce the error. In each round of iteration, the algorithm builds a new decision tree based on the gradient information of the current data set and updates the prediction results of the model. The final prediction value is obtained by weighted summation of the prediction results of all decision trees.

[0118] Specifically, the process of the GBDT algorithm is as follows:

[0119] 1) Initialize an empty decision tree set;

[0120] 2) For each iteration:

[0121] a. Calculate the gradient information of the current data set;

[0122] b. Build a new decision tree based on the gradient information;

[0123] c. Add the newly constructed decision tree to the set;

[0124] d. Use the updated decision tree set to predict the data;

[0125] e. Calculate the prediction error and update the gradient information;

[0126] 3) Repeat step 2) until the preset number of iterations or error threshold is reached.

[0127] 4. Model Evaluation

[0128] The GBDT model is verified on the validation set and evaluated using the MAE (mean absolute error) and RMSE (root mean square error) methods. Assume that the error calculated by the model MAE is e mae The largest power load in the data set is CL maxThe default minimum value is 0. The prediction effect must meet the following requirements: 1-e mae / CL max ≥85%. Figure 4 As shown in FIG. 1 , a comparison diagram of the effects of using the above prediction method to predict the load of a charging station under a distribution transformer is shown. It can be seen from the figure that the predicted load curve is basically consistent with the actual load curve, and the prediction deviation at each moment is kept within the allowable deviation range.

[0129] Embodiment 3

[0130] Based on the method described in the above embodiment, this embodiment provides a transformer flexible expansion system based on historical operation data, which aims to dynamically realize the flexible expansion of transformers by making full use of historical operation data, real-time load monitoring, energy storage management and reasonable deployment of photovoltaic power generation. The system will include multiple modules, each of which has a specific function and works together to ensure that the transformer can operate efficiently under different load conditions. The following is the detailed content of the module composition, function description and workflow of the system.

[0131] 1. System modules and functions

[0132] (1) Data acquisition module:

[0133] Responsible for collecting all operating data of distribution transformers and their subordinate equipment, including power consumption data of load equipment, power generation data of photovoltaic power generation equipment, charging and discharging status of energy storage equipment, and meteorological data. Mainly used to support subsequent data analysis and prediction. Specifically, it may include smart meters and sensors installed on transformers, photovoltaic equipment, and energy storage equipment to transmit real-time data to edge computing nodes. The collected data includes operating parameters such as voltage, current, power, frequency, and temperature.

[0134] (2) Data preprocessing module:

[0135] It is used to clean, denoise and detect anomalies of the collected raw data to ensure the accuracy and consistency of the data and filter out faulty and incomplete data. Data smoothing algorithms and anomaly detection algorithms (such as anomaly recognition based on statistical analysis or machine learning) can be used to clean and preprocess the input data.

[0136] (3) Load forecasting module:

[0137] Based on historical operation data, the power load of the distribution transformer in the next period is predicted, and the load demand curve is generated and provided to the subsequent capacity management module for analysis and decision-making. The load forecasting model can be established using time series analysis (such as ARIMA model) or deep learning algorithms (such as LSTM). The main basis for the forecast includes historical load data, seasonal changes, holiday impacts, etc.

[0138] (4) Photovoltaic power generation prediction module:

[0139] Based on the historical power generation data and weather forecast information of photovoltaic power generation equipment, the future power generation is predicted to ensure that photovoltaic resources can reasonably participate in load scheduling. This can be achieved by establishing a photovoltaic power generation prediction model based on data drive or the physical characteristics of the photovoltaic system, taking into account parameters such as sunshine duration, light intensity, and temperature. This module communicates with the meteorological data service interface in real time to ensure the accuracy of power generation prediction.

[0140] (5) Energy storage management module:

[0141] It is used to manage the charging and discharging process of energy storage equipment, ensuring that the energy storage equipment can be discharged in time to relieve load pressure during peak load, and charged to reserve electricity during low load. The energy storage scheduling strategy dynamically adjusts the charging and discharging behavior of energy storage equipment based on load forecasting and real-time power demand. An economic optimization algorithm is set up inside the module to optimize the charging timing and discharging strategy through price signals and load demand.

[0142] (6) Flexible interconnection device control module:

[0143] Flexible interconnection devices are used to flexibly allocate power resources between two adjacent transformers. When a transformer is overloaded, the module will transfer power from adjacent transformers to reduce the load. The flexible interconnection device uses low-voltage power flow mutual assistance technology, combined with the real-time operating status and remaining capacity of the transformer to ensure dynamic balance of the load. The control module works in coordination with the transformer and energy storage device through a communication protocol.

[0144] In some embodiments, the flexible interconnection device control module mainly includes three control modes: load balancing mode, microgrid economic mode and safe supply mode.

[0145] The load balancing mode refers to detecting the load factors of the transformers on both sides of the flexible interconnection device and achieving transformer load balancing through power flow mutual assistance. The goal is to make the load factor deviation of the transformers on both sides less than the set threshold value (the default is 10%).

[0146] The microgrid economic model refers to the interconnection of microgrids or substations with photovoltaic power generation equipment and energy storage equipment, taking into full consideration the differences in resource endowments of the two substations in terms of distributed source-storage status and peak-valley electricity prices, and building an optimized and adjusted scheduling strategy with the goal of minimizing electricity purchase costs. By controlling the flow of electricity through flexible interconnection devices, the purpose of saving electricity bills can be achieved.

[0147] The safe power supply mode means that when one of the substations loses power or fails, the flexible interconnection device can support the real-time transfer of important loads to help the substation restore power.

[0148] (7) Edge computing module:

[0149] Real-time data processing and short-term prediction are performed on edge devices close to the transformer to reduce computing and communication delays and ensure the timeliness and accuracy of load regulation. The module has data collection, processing, analysis, storage and short-term prediction capabilities and can interact directly with cloud servers. It can also perform data preprocessing, status monitoring, fault detection and other functions, and upload the processed data to the cloud.

[0150] (8) Management and optimization platform (cloud):

[0151] As the central command module of the system, it receives all data from edge computing nodes, performs global analysis and policy optimization, generates scheduling plans and sends them to each edge node. The platform relies on big data analysis and optimization algorithms to schedule and optimize the power resources of the entire park. Its main tasks include load balancing, economic optimization, energy storage management, photovoltaic scheduling, etc. The management platform supports remote monitoring and maintenance functions.

[0152] 2. System workflow

[0153] (1) Data collection and preprocessing:

[0154] Smart meters and sensors of transformers and subordinate equipment continuously collect real-time operating data of various electrical equipment, including power consumption of load equipment, photovoltaic power generation, charging and discharging status of energy storage equipment, etc. All data are transmitted to edge computing nodes through the communication network. The data preprocessing module cleans and denoises the raw data to ensure the accuracy and consistency of the data.

[0155] (2) Load and power generation forecast:

[0156] The system uses the load forecasting module to predict the load conditions in the future period and generate a load demand curve based on historical load data, holidays, seasonal changes and other factors. At the same time, the photovoltaic power generation forecasting module predicts future photovoltaic power generation based on historical power generation data and weather forecast information to ensure the reasonable scheduling of photovoltaic power.

[0157] (3) Real-time load monitoring and remaining capacity calculation:

[0158] The system collects and analyzes the rated capacity of each transformer, the total real-time load, the total amount of photovoltaic grid-connected, and other data to calculate the remaining capacity of the transformer. If the remaining capacity of a transformer is close to the critical value (threshold), it enters the dispatch mode.

[0159] (4) Energy storage management and flexible interconnection device scheduling:

[0160] When the remaining capacity of the transformer is insufficient, the energy storage management module will dispatch the energy storage device to start discharging to fill the load gap. If the energy storage device cannot fully meet the demand, the flexible interconnection device control module will transfer power from the adjacent transformer to ensure the dynamic balance of the power load. When the load is low, the system will control the energy storage device to charge, or transfer excess power to the adjacent transformer through the flexible interconnection device.

[0161] (5) Management and optimization:

[0162] The management platform (cloud) monitors the operating status of the entire system in real time, and dynamically adjusts the operating strategy and power dispatch plan of each transformer based on load forecast, photovoltaic power generation forecast and energy storage equipment status. The optimization algorithm analyzes load distribution, energy storage status and electricity prices to develop economical and efficient dispatch strategies to ensure that the system operates safely and maximizes the economic benefits of the power grid.

[0163] In summary, the system achieves the goal of flexible transformer expansion through modular design. The modules work together through edge computing and cloud management platforms to ensure that the system can dynamically dispatch power resources under different load conditions, optimize grid operation efficiency, reduce hardware expansion costs, and extend the service life of transformers.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may also be combined, the steps may be implemented in any order, and there are many other changes in different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A transformer flexible expansion method based on historical operation data, for two adjacent distribution transformers connected by a flexible interconnection device, wherein the flexible interconnection device is used to transmit power between different distribution transformers through a low-voltage power flow mutual assistance device, characterized in that: The following steps are involved: S1. Based on historical operation data, predict the power consumption of the load equipment under each distribution transformer to obtain the total energy load under each distribution transformer at the next moment; S2. Based on the historical operation data, the power generation of the photovoltaic power generation equipment under each distribution transformer is predicted to obtain the photovoltaic power generation power under each distribution transformer at the next moment; S3. Calculate the remaining capacity of the distribution transformer at the next moment according to the rated capacity of the distribution transformer, the total energy load of the distribution transformer at the next moment, and the total amount of photovoltaic grid-connected power; S4. If the remaining capacity of a distribution transformer at the next moment is less than the set threshold, the power demand of the distribution transformer area is met by controlling the energy storage device under the distribution transformer to discharge and / or by flexibly transferring power from the adjacent distribution transformer through the flexible interconnection device; S5. If the remaining capacity of a distribution transformer at the next moment is greater than the set threshold, the load capacity of the distribution transformer is fully utilized by controlling the charging of the energy storage device under the distribution transformer and / or flexibly transferring power to the adjacent distribution transformer through the flexible interconnection device.

2. The transformer flexible capacity expansion method according to claim 1, characterized in that: Step S1 includes: Data collection: Install smart meters or remote monitoring equipment under each distribution transformer to collect historical power consumption data of various types of power-consuming equipment, including power, in real time, as well as corresponding weather, season and holiday information; Data preprocessing: Use cleaning and denoising techniques on the collected historical electricity consumption data to remove abnormal data points; Establish a load forecasting model: Based on historical electricity consumption data and corresponding weather, season and holiday information, build and train a machine learning model to predict future electricity loads; Load forecasting: Based on the model prediction results, the total power consumption forecast of each distribution transformer at the next moment will be generated, and this value will be used as a reference for subsequent flexible scheduling.

3. The transformer flexible capacity expansion method according to claim 2, characterized in that: The load forecasting model adopts the GBDT model. The mean absolute error is used to evaluate the model quality during GBDT model training. If 1-e mae / CL max ≥85%, the model meets the application requirements, e mae Represents the calculated mean absolute error, CL max is the maximum power load value in the data set.

4. The transformer flexible capacity expansion method according to claim 1, characterized in that: Step S2 includes: Data collection: Collect the historical power generation data of the photovoltaic system and the corresponding meteorological data. The historical power generation data includes daily power generation, instantaneous power, daily power generation peak, and average power generation efficiency. The meteorological data includes solar irradiance, temperature, sunshine duration, wind speed, humidity, and cloud cover. Data preprocessing: Use cleaning and denoising techniques on the collected historical power generation data to remove abnormal data points, and align the historical power generation data with the meteorological data at the same time interval; Establishing a photovoltaic prediction model: Building and training a photovoltaic prediction model based on historical power generation data and meteorological data to predict power generation at future times; Power generation forecast: The meteorological data is input into the trained photovoltaic forecast model to generate the predicted value of future photovoltaic power generation.

5. The transformer flexible capacity expansion method according to claim 4, characterized in that: The photovoltaic prediction model includes a physical power generation model based on the physical characteristics of the photovoltaic system and a data-driven machine learning model; the physical power generation model calculates the theoretical power generation of the photovoltaic module by inputting meteorological data; the machine learning model includes ARIMA, SARIMA, random forest and deep learning network.

6. A transformer flexible expansion system based on historical operation data, characterized in that: include: Data acquisition module: responsible for collecting the operating data of the distribution transformer and its subordinate equipment and transmitting it to the edge gateway module, including the power consumption data of the load equipment, the power generation data of the photovoltaic power generation equipment, the charging and discharging status of the energy storage equipment, and the meteorological data; Data preprocessing module: used to clean, denoise and detect anomalies of the collected raw data to ensure the accuracy and consistency of the data and filter out faulty and incomplete data; Load forecasting module: Based on historical operation data, predict the power load of the distribution transformer in the next period, generate a load demand curve, and provide it to the subsequent management and optimization platform for analysis and decision-making; Photovoltaic power generation prediction module: Based on the historical power generation data and weather forecast information of photovoltaic power generation equipment, it predicts future power generation to ensure that photovoltaic resources can reasonably participate in load scheduling; Energy storage management module: used to manage the charging and discharging process of energy storage equipment, ensuring that the energy storage equipment can be discharged in time to relieve load pressure during peak load, and charged to reserve electricity during low load; Flexible interconnection device control module: used to realize flexible allocation of power resources between two adjacent transformers through flexible interconnection devices. When a distribution transformer is overloaded, the module will transfer power from the adjacent distribution transformer to reduce the load; Edge gateway module: performs real-time data processing and short-term prediction on edge devices close to the transformer, and uploads the processed data to the management platform, reducing computing and communication delays and ensuring the timeliness and accuracy of load regulation; Management and optimization platform: As the central command module of the system, it receives all data from the edge gateway module, performs global analysis and policy optimization, generates scheduling plans and sends them to each edge node.

7. The transformer flexible capacity expansion system according to claim 6, characterized in that: The photovoltaic power generation prediction module is implemented by establishing a photovoltaic power generation prediction model based on data drive or physical characteristics of the photovoltaic system, and communicates with the meteorological data service interface in real time to timely obtain meteorological data including solar irradiance, temperature, sunshine duration, wind speed, humidity, and cloud cover.

8. The transformer flexible capacity expansion system according to claim 6, characterized in that: The energy storage management module dynamically adjusts the charging and discharging behavior of the energy storage device based on load forecasting and real-time power demand; an economic optimization algorithm is provided within the module to optimize the charging timing and discharging strategy through price signals and load demand.

9. The transformer flexible capacity expansion system according to claim 6, characterized in that: The control modes of the flexible interconnection device control module include load balancing mode, microgrid economic mode and safe supply mode: The load balancing mode refers to detecting the load rate difference of the transformers on both sides of the flexible interconnection device. When the load rate difference of the transformers on both sides is less than the set threshold value due to the mutual assistance of the power flow, the load balancing mode control is started; The microgrid economic model refers to the interconnection of microgrids or substations with photovoltaic power generation equipment and energy storage equipment, taking into full consideration the differences in the distributed source-storage status and peak-valley electricity prices of the two substations, and building an optimized adjustment scheduling strategy with the minimum electricity purchase cost as the goal. By controlling the flexible interconnection device to control the mutual assistance time of the flow and the time of energy storage charging and discharging, the purpose of saving electricity bills can be achieved. The safe power supply mode means that when one of the substations loses power or fails, the load is transferred in real time through flexible interconnection devices to help the substation restore power.

10. The transformer flexible capacity expansion system according to claim 6, characterized in that: The system predicts the load conditions in future time periods through the load prediction module and generates a load demand curve. At the same time, the photovoltaic power generation prediction module predicts the future photovoltaic power generation based on historical power generation data and weather forecast information; the system summarizes and analyzes the rated capacity, real-time total load, and total photovoltaic grid-connected capacity of each transformer, and calculates the remaining capacity of the transformer; when the remaining capacity of the transformer is insufficient, the energy storage management module will dispatch the energy storage device to start discharging to supplement the load gap; if the energy storage device cannot fully meet the demand, the flexible interconnection device control module will allocate electricity from adjacent transformers for transfer to ensure the dynamic balance of the power load; when the load is low, the system will control the energy storage device to charge, or transfer excess electricity to adjacent transformers through the flexible interconnection device.

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