This paper introduces a novel fully distributed economic power dispatch (EPD) strategy for distribution networks, integrating dynamic tariffs. A two-layer model is proposed: the first layer comprises the physical power distribution network, including photovoltaic (PV) sources, wind turbine (WT) generators, energy storage systems (ESS), flexible loads (FLs), and other inflexible loads. The upper layer consists of agents dedicated to communication, calculation, and control tasks. Unlike previous EPD strategies, this approach incorporates dynamic tariffs derived from voltage constraints to ensure compliance with nodal voltage constraints. Additionally, a fast distributed optimization algorithm with an event-triggered communication protocol has been developed to address the EPD problem effectively. Through mathematical and simulation analyses, the proposed algorithm's efficiency and rapid convergence capability are demonstrated.

To address the cross-sessions variability of motor imagery electroencephalogram (EEG) and eliminate the need for lengthy recalibration step, this study proposes a motor imagery classification method based on discriminative transfer feature learning (DTFL). DTFL aims to reduce domain differences by jointly matching the marginal distribution and class conditional distribution of both domains. Simultaneously, DTFL maximizes interclass dispersion and minimizes intra-class scatter, preserving class discrimination information and improving classification performance. This method does not require class information for EEG samples in the target domain, effectively avoiding the need for long-term calibration. Experimental results on brain-computer interface competition datasets demonstrate that, compared with some transfer learning methods, the proposed DTFL mitigates cross-session variability and improves the classification accuracy of motor imagery EEG.