End-to-end learning in optical communication systems is a promising technique to solve difficult communication problems, especially for peak to average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems. The less complex, highly adaptive hardware and advantages in the analysis of unknown or complex channels make deep learning a valid tool to improve system performance. In this paper, we propose an autoencoder network combined with extended selected mapping methods (ESLM-AE) to reduce the PAPR for the DC-biased optical OFDM system and to minimize the bit error rate (BER). The constellation mapping/de-mapping of the transmitted symbols and the phase factor of each subcarrier are acquired and optimized adaptively by training the autoencoder with a combined loss function. In the loss function, both the PAPR and BER performance are taken into account. The simulation results show that a significant PAPR reduction of more than 10 dB has been achieved by using the ESLM-AE scheme in terms of the complementary cumulative distribution function. Furthermore, the proposed scheme exhibits better BER performance compared to the standard PAPR reduction methods.

This paper considers that wind turbines participate in Primary Frequency Regulation (PFR) by deloaded operation mode together with conventional units. At the same time, it further taps the fast start-up capability of power resources and warm and hot thermal power units are also considered as part of the intra-day emergency reserve. Based on the above, this paper proposes an intra-day dispatch model considering coordination of both steady state and transient state, which evaluates the transient frequency security of system in a variety of high-risk stochastic scenarios. Besides, interruptible load is also used as one of the PFR reserve sources. In this paper, an inner-outer iterative method is proposed to solve the above model. The outer layer selects out high-risk scenarios for transient optimization. The inner layer carries out coordination optimization of steady state and transient state to determine the unit commitment result, the amount of interruptible load to be shed and forced wind curtailment. Finally, the effectiveness of the proposed method is validated on the improved New England 10-machine 39-bus system. The results show that the proposed method can achieve full coverage of the system's intra-day reserve requirements, while taking into account the security and economy of the system.

As distributed photovoltaic (PV) systems increasingly integrate with smart grids, accurate ultra-short-term power forecasting is critical for optimizing grid dispatch and maintaining grid stability. However, the intermittency of solar irradiance and the complex multivariate temporal correlations in PV operational data present notable challenges to conventional forecasting models. Such models frequently fail to characterize nonlinear dynamics and effectively capture multiscale features of PV output. To mitigate these limitations, this study proposes a hybrid framework integrating adaptive feature-weighted dynamic time warping (AFDTW) clustering with an improved temporal convolutional network-long short-term memory (ITCN-LSTM) encoder-decoder. First, the AFDTW clustering algorithm employs an adaptive weighting mechanism based on Markov distance to prioritize high-variance features, enabling the capture of subtle patterns in PV output. A pruning strategy is additionally incorporated to reduce computational complexity. Subsequently, the clustered data are input into the ITCN-LSTM model. The ITCN leverages asymmetric causal and spatial convolutions to complex multivariate temporal correlations, thereby modeling geographic correlations. Meanwhile, the LSTM decoder captures long-term trends via its gated memory units. This architecture effectively bridges the gap between static, history-based models and real-time meteorological dynamics, facilitating reliable predictions under diverse weather conditions. Empirical evaluations using a real dataset from a 1.8-MW PV plant in Australia show that the proposed method outperforms the benchmark models across multiple forecasting horizons (5 min to 4 h). The proposed method significantly improves PV power-forecasting accuracy and supports more effective and reliable utilization of solar energy resources.