State Grid Zhejiang Electric Power Co., Ltd. Taizhou Power Supply Company

Zhejiang University

State Grid Corporation of China

State Grid Zhejiang Electric Power Company Limited

Taizhou Power Supply Company

Dongbo Zhang

Jie Yu

Guode Ying

Jian Yang

Yuande Zheng

Yizhi Zhu

Lintong Wang

Xinjian Chen

Chenghuai Hong

Zihuai Zheng

Chi Zhang

Weiming Lu

Feng Guo

Min Chen

Daojian Hong

Ning Chen

Xiaoqing Wang

Yuxi Tu

Zhouhong Wang

Xinnan Sun

Minzhi Chen

Jiahao Zhou

Yefeng Luo

Binren Wang

Bodong Li

Zhijian Yu

Zhen Huang

Jiandong Si

Zhaopei Liang

Wenqi He

Lei Jing

Huili Xie

Lei Hong

Ying Ren

Yilun Zhu

Minyan Xia

Huiying Gao

Bingren Wang

Qinye Chen

Mingxu Chen

Zi Ying

Xiaoping Fu

Yuyin Qiu

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.

Photovoltaic energy storage offline coordination system and method based on demand management

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.

Method for intelligently adjusting power flow based on Q-learning algorithm

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.

Net load forecasting method and apparatus for new energy electric power market

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 as a dispatching strategy.

Source-network-load-storage coordination dispatching method in background of coupling of renewable energy sources

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.

Bidirectional CLLC resonant circuit with coupled inductor

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. The protection module is configured to provide low-frequency protection, over-frequency protection, over-voltage protection and low-voltage protection for the low-voltage DGR.

Anti-islanding protection system

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.

Method for controlling smooth switching of operation direction of bidirectional resonant CLLC circuit

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Zhejiang, China

Dongbo Zhang