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 multi-modal adaptive fusion deep clustering model based on an auto-encoder includes an encoder structure, a multi-modal adaptive fusion layer, a decoder structure and a deep embedding clustering layer. The encoder is configured to enable a dataset to be respectively subjected to three types of nonlinear mappings of the auto-encoder, a convolutional auto-encoder and a convolutional variational auto-encoder to obtain potential features, respectively. The multi-modal adaptive feature fusion layer is configured to fuse the potential features into a common subspace in an adaptive spatial feature fusion mode to obtain a fused feature. The decoder is configured to decode the fused feature by using a structure symmetrical to the encoder to obtain a decoded reconstructed dataset. The deep embedding clustering layer is configured to cluster the fused feature Z and obtain a final accuracy ACC by comparing a clustering result with a true label.

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.