Abstract: Disclosed are a photovoltaic energy storage offline coordination system and method based on demand management. The system includes a photovoltaic generator set, an inverter, a dispatch and control device for charge-discharge, a load monitor, a first and second energy storage device, and a remote console. The photovoltaic generator set is electrically connected to the dispatch and control device for charge-discharge via the inverter; the first energy storage device is connected to a load-side power supply bus via a first dispatch and control unit; the second energy storage device is connected to the load-side power supply bus via a third dispatch and control unit; a second dispatch and control unit is connected to the load-side power supply bus; the load monitor is provided at the load-side power supply bus, and configured to monitor a load of the load-side.

Abstract: A method for intelligently adjusting a power flow based on a Q-learning algorithm includes: converting a variable, an action, and a goal in a power grid to a state, an action, and a reward in the algorithm, respectively; selecting an action from an action space, giving an immediate reward based on a result of power flow calculation, and correcting a next state; forwardly observing a next exploration action based on a strategy in the Q-learning algorithm; updating a Q value in a corresponding position in a Q-value table based on the obtained reward; if a final state is not reached, going back to step 2; otherwise, increasing the number of iterations by 1; if the number of iterations does not reach predetermined value K, that is, Episode<K, going back to step 2; otherwise, that is, Episode=K, outputting the Q-value table; and outputting an optimal unit combination.

Abstract: Disclosed are a net load forecasting method and apparatus for a new energy electric power market. The method includes: obtaining and performing data preprocessing on new energy output data and external environmental data, and extracting strongly correlated features from the new energy output data and the external environmental data after the data preprocessing; performing feature expansion on the strongly correlated features, and inputting the strongly correlated features after the feature expansion into a preconstructed regression forecasting model, to obtain a first forecast value; obtaining and performing data preprocessing on user load data and load influencing factor data, and inputting the user load data and the load influencing factor data after the data preprocessing into a FNN-LSTM hybrid model, to obtain a second forecast value; and calculating a difference between the second forecast value and the first forecast value, to obtain a net load forecasting result.

Abstract: Disclosed are an overhead monitoring method and apparatus, and a non-transitory computer-readable storage medium. The method may include: receiving a flexible Ethernet (FlexE) signals from one or more PHY interfaces respectively; de-interleaving each FlexE signal with the transmission rate of N*Y Gbps in the received FlexE signals respectively into N PHY signals each having a transmission rate of Y Gbps; selecting M valid data signals from the FlexE signal with the transmission rate being the target transmission rate in the received FlexE signals and the de-interleaved PHY signals with the transmission rate of Y Gbps; removing each pad from each of the M valid data signals, performing frame delimitation on each of the M valid data signals, and extracting each FlexE overhead from each of the M valid data signals; and generating a linear overhead frame based on the extracted FlexE overheads, and outputting the linear overhead frame.

Abstract: Systems and methods of deep learning-based multi-objective pacing content deployment are disclosed. A first set of input parameters is received and a first set of pacing parameters are generated by a trained pacing model that receives the first set of input parameters. The trained pacing model includes a k-nearest neighbor (KNN) portion and Neural Basis Expansion Analysis for Time Series (N-BEATS) portion. In response to generating the first set of pacing parameters, a pacing pipeline is modified to incorporate the set of pacing parameters. The pacing pipeline is configured to generate deployment parameters. Content is deployed to one or more content systems based on the deployment parameters and feedback data representative of the deployed content is received. A second set of pacing parameters is generated by the trained pacing model. The trained pacing model receives a second set of input parameters that are based at least in part on the feedback data.

Abstract: A source-network-load-storage coordination dispatching method in a background of a coupling of renewable energy sources, including: taking an expectation of a minimum grid operating cost in a dispatching cycle as an objective function; generating an approximate value function of an output of a set for generating electricity from renewable energy sources and a user load, and constructing a source-network-load-storage coordination dispatching model with combination of the objective function; obtaining forecast data of the output of a set for generating electricity from renewable energy sources and the user load, and inputting the forecast data into the dispatching model for solving; performing iterative updating on the approximate value function, importing the approximate value function after the iterative updating into the dispatching model for iterative solving, and terminating an iterative process until a solving result satisfies a preset convergence condition; and using a solving result of a last iteration

Abstract: A source-network-load-storage coordination dispatching method in a background of a coupling of renewable energy sources, including: taking an expectation of a minimum grid operating cost in a dispatching cycle as an objective function; generating an approximate value function of an output of a set for generating electricity from renewable energy sources and a user load, and constructing a source-network-load-storage coordination dispatching model with combination of the objective function; obtaining forecast data of the output of a set for generating electricity from renewable energy sources and the user load, and inputting the forecast data into the dispatching model for solving; performing iterative updating on the approximate value function, importing the approximate value function after the iterative updating into the dispatching model for iterative solving, and terminating an iterative process until a solving result satisfies a preset convergence condition; and using a solving result of a last iteration

Abstract: Disclosed are a photovoltaic energy storage offline coordination system and method based on demand management. The system includes a photovoltaic generator set, an inverter, a dispatch and control device for charge-discharge, a load monitor, a first and second energy storage device, and a remote console. The photovoltaic generator set is electrically connected to the dispatch and control device for charge-discharge via the inverter; the first energy storage device is connected to a load-side power supply bus via a first dispatch and control unit; the second energy storage device is connected to the load-side power supply bus via a third dispatch and control unit; a second dispatch and control unit is connected to the load-side power supply bus; the load monitor is provided at the load-side power supply bus, and configured to monitor a load of the load-side.

Abstract: Disclosed are a net load forecasting method and apparatus for a new energy electric power market. The method includes: obtaining and performing data preprocessing on new energy output data and external environmental data, and extracting strongly correlated features from the new energy output data and the external environmental data after the data preprocessing; performing feature expansion on the strongly correlated features, and inputting the strongly correlated features after the feature expansion into a preconstructed regression forecasting model, to obtain a first forecast value; obtaining and performing data preprocessing on user load data and load influencing factor data, and inputting the user load data and the load influencing factor data after the data preprocessing into a FNN-LSTM hybrid model, to obtain a second forecast value; and calculating a difference between the second forecast value and the first forecast value, to obtain a net load forecasting result.

Abstract: Related to is a bidirectional CLLC circuit with a coupled inductor, which is associated with a circuit topology and operation control of a bidirectional CLLC resonant converter. Provided is a structure of a bidirectional CLLC resonant circuit with a coupled inductor, including a primary side bridge, a secondary side bridge, a primary side resonant capacitor, a secondary side resonant capacitor, a coupled resonant inductor, and a transformer. Compared with a structure of a conventional bidirectional CLLC resonant circuit, two separate resonant inductors located at a primary side and a secondary side in an original resonant cavity are replaced with one coupled resonant inductor in the circuit; the coupled resonant inductor has opposite dotted terminals with the transformer, and a primary side and a secondary side of the coupled resonant inductor are respectively in serial connection with a primary side and a secondary side of the transformer.

Abstract: A package structure of an embedded power module includes a top insulation layer, a top metal pattern layer, a solder layer, a device layer, a bottom metal pattern layer and a bottom insulation layer sequentially arranged from top to bottom. The device layer includes at least two MOSFET bare dies and several metal connection blocks, and is filled with insulation filler to isolate the MOSFET bare dies and the metal connection blocks from each other. The drain electrodes of the bare dies are connected with the top metal pattern layer through the solder layer, and the source electrodes and the gate electrodes of the bare dies are electrically connected to the bottom metal pattern layer, respectively. The upper and lower surfaces of the metal connection blocks are electrically connected to the top metal pattern layer and the bottom metal pattern layer, respectively.

Abstract: Related to is a bidirectional capacitance - inductance - inductance - capacitance (CLLC) circuit with a coupled inductor, which is associated with a circuit topology and operation control of a bidirectional CLLC resonant converter. A bidirectional CLLC resonant circuit with a coupled inductor includes a primary side bridge, a secondary side bridge, a primary side resonant capacitor, a secondary side resonant capacitor, a coupled resonant inductor, and a transformer. Compared with a structure of a conventional bidirectional CLLC resonant circuit, two separate resonant inductors located at a primary side and a secondary side in an original resonant cavity are replaced with one coupled resonant inductor in the circuit; the coupled resonant inductor has opposite dotted terminals with the transformer, and a primary side and a secondary side of the coupled resonant inductor are respectively in serial connection with a primary side and a secondary side of the transformer.

Abstract: Disclosed are an overhead monitoring method and apparatus, and a non-transitory computer-readable storage medium. The method may include: receiving a flexible Ethernet (FlexE) signals from one or more PHY interfaces respectively; de-interleaving each FlexE signal with the transmission rate of N*Y Gbps in the received FlexE signals respectively into N PHY signals each having a transmission rate of Y Gbps; selecting M valid data signals from the FlexE signal with the transmission rate being the target transmission rate in the received FlexE signals and the de-interleaved PHY signals with the transmission rate of Y Gbps; removing each pad from each of the M valid data signals, performing frame delimitation on each of the M valid data signals, and extracting each FlexE overhead from each of the M valid data signals; and generating a linear overhead frame based on the extracted FlexE overheads, and outputting the linear overhead frame.

Abstract: A method for intelligently adjusting a power flow based on a Q-learning algorithm includes: converting a variable, an action, and a goal in a power grid to a state, an action, and a reward in the algorithm, respectively; selecting an action from an action space, giving an immediate reward based on a result of power flow calculation, and correcting a next state; forwardly observing a next exploration action based on a strategy in the Q-learning algorithm; updating a Q value in a corresponding position in a Q-value table based on the obtained reward; if a final state is not reached, going back to step 2; otherwise, increasing the number of iterations by 1; if the number of iterations does not reach predetermined value K, that is, Episode<K, going back to step 2; otherwise, that is, Episode=K, outputting the Q-value table; and outputting an optimal unit combination.

Abstract: Provided is an anti-islanding protection system. The system is applied to a low-voltage distributed generation resource (DGR) and includes a box, a reverse power protector, a protection module and an output controller. The reverse power protector has an end connected to a first current transformer and has another end connected to the output controller. The reverse power protector is configured to provide reverse power protection for the low-voltage DGR. The output controller has an end connected to the protection module and the reverse power protector and has another end connected to a grid-connection switch of the low-voltage DGR. The output controller is configured to control the grid-connection switch to turn off when reserve power is detected. The protection module has an end connected to a second current transformer and has another end connected to the output controller.

Abstract: The present invention relates to a method for identifying parameters of a 10 kV static load model based on similar daily load curves. In the present invention, an optimization model for identifying full-period parameters of the static load model is proposed based on a large number of daily load curves with response characteristic, a structure and constraints of the static load model, and two theoretical basic assumptions about loads. Full-period (including 96 moments) static voltage model parameters of 10 kV loads are given through optimization solution. A rule that active power and reactive power of the loads at each moment change with voltage is obtained. In addition, a change rule of load constituents is obtained. The method delivers good applicability, satisfies actual demands, and is suitable for large-scale static model analyses for 10 kV loads.

Abstract: A method for creating a knowledge representation model for a product includes: systematically capturing and representing knowledge and experience generated in product design and production processes, classifying the knowledge into five aspects, namely requirement, function, behavior, structure and evolution, and transforming unstructured tacit knowledge into structured knowledge in terms of know-what, know-how and know-why. Based on the knowledge representation model and knowledge bases, the present invention can be used to guide the construction of corresponding geometric model libraries, material libraries and process libraries, etc., and then to create design big data. The present invention can also be combined with an intelligent recommendation algorithm to intelligently recommend relevant data, information and knowledge based on the design engineers' working context and tasks undertaken.

Abstract: Provided is an anti-islanding protection system. The system is applied to a low-voltage distributed generation resource (DGR) and includes a box, a reverse power protector, a protection module and an output controller. The reverse power protector has an end connected to a first current transformer and has another end connected to the output controller. The reverse power protector is configured to provide reverse power protection for the low-voltage DGR. The output controller has an end connected to the protection module and the reverse power protector and has another end connected to a grid-connection switch of the low-voltage DGR. The output controller is configured to control the grid-connection switch to turn off when reserve power is detected. The protection module has an end connected to a second current transformer and has another end connected to the output controller.

Abstract: Provided is a method for controlling smooth switching of an operation direction of a bidirectional resonant CLLC circuit, which applies to the bidirectional resonant CLLC circuit. The method includes the following steps: Step 1: detecting a current circuit state and controlling the bidirectional resonant CLLC circuit to operate in a forward operation state by means of a primary bridge and a secondary bridge, by a controller; Step 2: performing Step 3 when an externally transmitted reference signal received by the controller or an internal preset reference signal in the controller is an operation direction switching signal; Step 3: performing frequency conversion control, by the controller; Step 4: performing preparation of phase shift control and generating a driving signal of the secondary bridge, by the controller; Step 5: performing the phase shift control, by the controller; and Step 6: switching a circuit operation state to an inverse operation mode.

Abstract: Embodiments described herein provide a method and apparatus for monitoring and correcting a transmit signal. A first sample is taken before the signal is input to a digital to analog converter (DAC) in a transmit chain. A second sample is taken of the transmit signal after the signal has passed through the power amplifier (PA). The first and second transmit samples are then compared and an equalizer interpolation value is determined. This equalizer interpolation value is applied to the transmit signal before transmission to provide a transmit signal with improved quality. The apparatus includes a feedback receive correction unit; a time domain processor in communication with the feedback receive correction unit; a frequency domain processing equalizer in communication with the time domain processor; an equalizer interpolation unit; an absolute value squaring unit in communication with the equalizer interpolation unit; and a processor for computation of a transmit quality parameter.