Abstract: A drilling tool includes an outer cylinder; a power rotary shaft arranged in an inner chamber of the outer cylinder; a percussion generator arranged below the power rotary shaft, having a transmission shaft extending in the outer cylinder and can be driven by the power rotary shaft to rotate around its axis, an output main shaft engaged with a lower end of the transmission shaft so as to be driven by the transmission shaft to rotate about its axis and is movable relative to the transmission shaft along an axial direction, and an percussion assembly The percussion assembly is arranged between an annulus formed between the upper end of the output main shaft and the outer cylinder and can generate reciprocating impact along the axial direction on the output main shaft. A drilling bit connected with the output main shaft extending out of the inner chamber of the outer cylinder.

Abstract: A speed-enhancing drilling tool includes an upstream drill string (10), which has a drive motor and a first driving rod coupled therewith, a downstream drilling bit; and a percussive device connected between the upstream drilling string and the downstream drilling bit. The first driving rod extending axially and the drive motor are configured to drive the first driving rod in rotation. The percussive device has a rotary driving part having an upper end engaged with the first driving rod to rotate together therewith; and a rotary working part having an upper end engaged with a lower end of the rotary driving part and a lower end connected with the downstream drilling bit. The rotary working part can be driven by the rotary driving part to rotate about its axis, and axially movable relative thereto; and a percussion generating part arranged around the rotary working part.

Abstract: Disclosed are a virtual power plant operation risk analysis method and a device. The method comprises: establishing a multi-state model of wind turbine output, analyzing influence of wind speed on wind turbine failure rate based on the multi-state model of wind turbine output, and establishing a wind turbine failure model considering wind turbine time-varying failure rate; establishing a multi-state model of wind turbine output considering the wind speed and the wind turbine time-varying failure rate by an improved general generating function method based on the multi-state model of wind turbine output and the wind turbine failure model considering the wind turbine time-varying failure rate; establishing a multi-state output model of virtual power plant based on the multi-state model of wind turbine output considering wind speed and wind turbine time-varying failure rate; and calculating operation risk indicators of virtual power plant through the multi-state output model of virtual power plant.

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: The present invention relates to a reliability calculation method of a power distribution system considering hierarchical decentralized control of demand-side resources, including the following steps: step 1, establishing a multi-state model of wind turbine output and a two-state model of wind turbine failure, which respectively consider randomness of wind speed and uncertainty of wind turbine failure; step 2, establishing a multi-state reliability model of distributed wind power system including a plurality of wind turbines; step 3, establishing a reliability model of information communication system considering hierarchical decentralized control of random failures and information delays; step 4, establishing a reliability model of demand-side resources considering hierarchical decentralized control; and step 5, calculating a value of reliability of a power distribution system considering hierarchical decentralized control of demand-side loads, to acquire an analysis result of the reliability of the power di

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: The present invention relates to a load forecasting method based on a multi-energy coupling scene, which comprises the following steps: step 1, establishing a multilevel indicator system of key influencing factors of load demand in a multi-energy mode; step 2, obtaining key influencing factors influencing the total load demand and the total supply of the multi-energy coupling load; step 3, normalizing the data of the key influencing factors extracted in step 2, and initializing population characteristic parameters of adaptive firework algorithm (AFWA); step 4, forecasting the regional total power demand and regional coupling power supply respectively by adopting LSSVM optimized by the AFWA algorithm; and step 5, forecasting the net power load demand on the regional coupling power supply. The present invention improves the calculation efficiency and model stability and also ensures the forecasting accuracy.

Abstract: The invention relates to a multi-subject flexible energy block bidding transaction system and method in a power market. The system includes a login authentication module, a data input module, a first processing module, a second processing module, a third processing module, a data verifying module and an output module, wherein the login authentication module is configured for self-identity confirmation of a power market subject and selecting a power transaction declaration mode; the data input module is configured for inputting an electricity price, an electricity quantity and corresponding time information; the three processing modules carry out matching under different modes, the data verifying module receives matching results obtained by each processing module and carries out safety verification, and the output module outputs final trading results of an electricity quantity and an electricity price of each subject in the power market.

Abstract: The present invention relates to a load forecasting method based on a multi-energy coupling scene, which comprises the following steps: step 1, establishing a multilevel indicator system of key influencing factors of load demand in a multi-energy mode; step 2, obtaining key influencing factors influencing the total load demand and the total supply of the multi-energy coupling load; step 3, normalizing the data of the key influencing factors extracted in step 2, and initializing population characteristic parameters of adaptive firework algorithm (AFWA); step 4, forecasting the regional total power demand and regional coupling power supply respectively by adopting LSSVM optimized by the AFWA algorithm; and step 5, forecasting the net power load demand on the regional coupling power supply. The present invention improves the calculation efficiency and model stability and also ensures the forecasting accuracy.