Optimal configuration method of electric vehicle's participating in Load Aggregator's VPP low-carbon economy

Summary: Multisource inverters (MSIs) are gaining considerable attention as a new approach for integrating energy and power sources in electric vehicle applications. These structures provide active control of dc sources without the need for dc/dc converters or magnetic elements, resulting in reduced weight and size of the power electronics interface. This article proposes a novel three-phase MSI for integrating and controlling high-voltage and low-voltage dc sources, with improved efficiency and power density.

Summary: This paper proposes an optimal dispatching model for improving voltage distribution in distribution networks using distributed battery energy storage stations (BESSs). The model considers various constraints and uses the big M method to linearize nonlinear variables. Simulations show that the model effectively reduces voltage deviation and improves voltage qualification rate.

Summary: This paper proposes a deep learning approach and optimization model for the optimal day-ahead scheduling of zero-carbon multi-energy systems (ZCMES) virtual power plants. The proposed model considers carbon capture, electric vehicle flexibility, and clean energy marketing strategy to ensure system reliability, and achieves optimal decision-making using integrated recurrent unit-bidirectional longshort term memory (GRU-BiLSTM), autoencoder (AE), and robust-stochastic modelling approaches. The model is verified using historical multi-energy data, demonstrating its superiority in achieving high self-consumption and load cover ratios, as well as cost reduction in day-ahead scheduling. The model also strengthens carbon-neutral feasibility and provides a reference tool for sustainable energy policymakers.