Abstract: A method for aggregation regulation optimization of an electric vehicle load by considering a comprehensive response coefficient is disclosed. The method includes the following steps: Step 1, collecting electric vehicle charging information; Step 2, based on the electric vehicle charging information collected in Step 1, calculating aggregation regulation response indexes for user participation, and determining a weight of each aggregation regulation response index; Step 3, based on the calculation result of each aggregation regulation response index for user participation obtained in Step 2, calculating a comprehensive response coefficient of an electric vehicle; and Step 4, performing the aggregation regulation optimization of the electric vehicle load by using minimum load power deviation determined by a dispatching scheme and an actually considered comprehensive response coefficient as a target and using a load rate, a peak-valley difference and a load fluctuation rate after dispatching as constraints.

Abstract: The invention relates to an optimal allocation method for stored energy coordinating electric vehicles (EVs) to participate in auxiliary service market (ASM), including the following steps: 1. Predict the reported capacity of daily 96 points for EVs to participate in the ASM by least square support vector machine (LSSVM). 2. Fit the daily total load distribution of EVs. 3. Determine the error distribution between the reported capacity and the actual response capacity, and simulate the total daily load capacity of EVs in the future with Monte Carlo method. 4. Calculate the energy storage capacity required by EVs daily participating in ASM. 5. Build the objective function to minimize the scheduling risk of auxiliary service. 6. Solve the energy storage model in step 5 with particle swarm optimization (PSO), and output the configuration results of optimal energy storage capacity and energy storage power. The invention can improve the adjustable capacity of EVs participating in ASM.

Abstract: Disclosed is an electric vehicle load aggregation method based on continuous tracking of wind power curve, including: constructing an electric vehicle load consumption wind power curve aggregation model to obtain an electric vehicle call result, and calculating the abandoned wind power through the electric vehicle call result; optimizing the abandoned wind power quantity by energy storage equipment to obtain the abandoned wind power quantity after energy storage adjustment and optimization, setting the energy storage power and capacity configuration, and constructing a wind power curve continuous tracking model after energy storage adjustment and optimization; and solving the charging and discharging power of the energy storage equipment in each time period based on the wind power curve continuous tracking model after the energy storage adjustment and optimization, and calculating the cost of output aggregation.

Abstract: A prediction method for charging loads of electric vehicles with consideration of data correlation includes: collecting historical data of the charging loads of the electric vehicles; carrying out data correlation analysis on the historical data of the charging loads of the electric vehicles, and real-time data, and calculating correlation coefficients between the historical data of the charging loads of the electric vehicles and the real-time data; based on the correlation coefficients, selecting historical data of the charging loads of the electric vehicles, which has high correlation, as data of the charging loads of the electric vehicles, which is used for prediction; and predicting the historical data of the charging loads of the electric vehicles, serving as the data of the charging loads of the electric vehicles, which is used for prediction, by adopting an LSTM algorithm, to obtain prediction results.

Abstract: A method for optimizing dispatching of charging loads of electric vehicles to promote wind power consumption includes: acquiring blocked electric quantity of wind power at a peak down-regulation period; acquiring a curve of disorderly charging loads of the electric vehicles; establishing a model for optimizing the charging loads of the electric vehicles to promote wind power consumption, wherein an objective function of the model refers to that the electric vehicles participate in wind power consumption to minimize the remaining blocked quantity of the wind power, and the total charging cost of the electric vehicles is lowest, and acquiring constraint conditions of the model; and solving the optimization model by adopting an adaptive mutation particle swarm optimization algorithm, to obtain the target charging/discharging electric quantity and the target charging/discharging power of the electric vehicles.

Abstract: Disclosed is a prediction method and device for a clearing price of an auxiliary service for peak regulation, including: acquiring continuous N days of historical data of clearing prices of an auxiliary service market for peak regulation and respectively moving data of original clearing prices forwards by 1, 2, . . . k days, so as to obtain D?1, D?2 . . . D?k data columns; performing first-round training and prediction by adopting a BP (Back Propagation) neural network, a BP neural network optimized by a PSO (Particle Swarm Optimization) algorithm and an LSSVM (Least Square Support Vector Machine), and forming a BP-expansion column, a PSO_BP-expansion column and an LSSVM-expansion column; training the BP neural network by taking first N?k days of time points and D?1, D?2 . . . D?k clearing prices, the BP-expansion column, the PSO_BP-expansion column, the LSSVM-expansion column and the original clearing prices as training data; and performing prediction for the clearing price.

Abstract: The invention relates to an optimal allocation method for stored energy coordinating electric vehicles (EVs) to participate in auxiliary service market (ASM), including the following steps: 1. Predict the reported capacity of daily 96 points for EVs to participate in the ASM by least square support vector machine (LSSVM). 2. Fit the daily total load distribution of EVs. 3. Determine the error distribution between the reported capacity and the actual response capacity, and simulate the total daily load capacity of EVs in the future with Monte Carlo method. 4. Calculate the energy storage capacity required by EVs daily participating in ASM. 5. Build the objective function to minimize the scheduling risk of auxiliary service. 6. Solve the energy storage model in step 5 with particle swarm optimization (PSO), and output the configuration results of optimal energy storage capacity and energy storage power. The invention can improve the adjustable capacity of EVs participating in ASM.

Abstract: The present disclosure relates to a method for clustering forecasting of the electric vehicle charging load, comprising the following steps: collecting electric vehicle charging load data on a historical date and weather information data related to that historical date; preprocessing and then normalizing the collected data to obtain a new data set; performing fuzzy C-means clustering on the normalized data, and taking an actual load measurement point as a fuzzy clustering index to construct a similar daily load set of the date to be forecast; according to the similar daily load set, constructing and training a least-square SVM (support vector machine) forecasting model; inputting load values at the same time in three days ahead of the date to be forecast and the weather information data related to the three days into the trained least-square SVM forecasting model, and outputting a forecast load.

Abstract: A multifunctional wireless ring mouse with a laser pointer function includes a mouse body, an optical sensor cover, a rotating sleeve, a finger ring, a steel loop, a wheel, a laser emission port and an optical sensor, wherein a screw guide hole, a charging socket and the laser emission port are arranged on an upper end of the mouse body, a power switch is arranged on a back side of the mouse body, the wheel, a selection button, a confirmation button, and a laser micro switch are arranged on a left side of the mouse body; a cylinder is arranged on a lower end of the mouse body; an outer groove is arranged outside the cylinder, an inner groove is arranged on an inner cylindrical wall of the cylinder, and a limit protruded ridge is arranged within the inner groove; a flange is arranged on the optical sensor cover, and limit ridge is arranged on the flange; a limit ring, a limit protruded ridge and a positioning screw are arranged on the rotating sleeve; a through groove is arranged on the finger ring, and the st

Abstract: A multifunctional wireless ring mouse with a laser pointer function includes a mouse body, an optical sensor cover, a rotating sleeve, a finger ring, a steel loop, a wheel, a laser emission port and an optical sensor, wherein a screw guide hole, a charging socket and the laser emission port are arranged on an upper end of the mouse body, a power switch is arranged on a back side of the mouse body, the wheel, a selection button, a confirmation button, and a laser micro switch are arranged on a left side of the mouse body; a cylinder is arranged on a lower end of the mouse body; an outer groove is arranged outside the cylinder, an inner groove is arranged on an inner cylindrical wall of the cylinder, and a limit protruded ridge is arranged within the inner groove; a flange is arranged on the optical sensor cover, and limit ridge is arranged on the flange; a limit ring, a limit protruded ridge and a positioning screw are arranged on the rotating sleeve; a through groove is arranged on the finger ring, and the st