AES Corporation

Watershed sediment budgets sum sediment sources (bank erosion and tributaries) and subtract sediment sinks (floodplains) to compute the watershed sediment output. Because computations are lumped, however, these budgets cannot quantify contributions from individual sources to the watershed output. We overcome this limitation by dividing the channel network into nested reaches, each with its own sediment budget. The nested framework partitions sediment between transport and storage as it moves downstream, quantifying the contributions from individual sources to watershed outputs. We quantify sediment fluxes using gaging station data, sediment fingerprinting, hydrodynamic modeling, geomorphic mapping, and measured rates of erosion and deposition. Bank erosion supplies 15 790 Mg/yr to the watershed budget, while upland hillslopes contribute 6 300 Mg/yr, with 10 380 Mg/yr stored on floodplains. The output computed from the budget (11 710 Mg/yr) is within the uncertainty of 9 300–43 000 Mg/yr of the measured output, so the budget balances. Reach-scale budgets identify local sediment hotspots, while routing computations indicate that bank erosion supplies 76 ± 5% of the watershed sediment flux, with upland hillslopes contributing 24 ± 3%. Legacy sediments comprise 34 ± 6% of the output. By quantifying individual source contributions to watershed sediment fluxes, the nested approach can improve sediment management decision-making.

Accurate short-term electricity load forecasting is a cornerstone of U.S. grid reliability; however, prevailing deep learning models remain opaque, limiting operator trust during extreme weather. A unified, interpretable, physics-informed ensemble framework is proposed, integrating a Convolutional Neural Network (CNN) branch for local feature extraction and a Transformer branch for long-range dependency modeling; the branches are fused through a validation-optimized weighted ensemble and regularized by a physics-informed loss derived from the piecewise parabolic temperature-demand relationship of the Electric Reliability Council of Texas (ERCOT) system. Post-hoc interpretability is provided through SHapley Additive exPlanations (SHAP) with the DeepExplainer backend, yielding global and event-level attributions. Using eight years of ERCOT hourly load data (2018-2025) fused with Automated Surface Observing System (ASOS) records from three Texas stations, the framework achieves 713 MW MAE, 812 MW RMSE, and 1.18

The accelerated digitalization of renewable energy smart grids through IoT sensors, AMI, and SCADA systems has significantly expanded the attack surface for sophisticated cyberattacks, FDI attacks that stealthily distort state estimation and DoS/DDoS attacks that flood communication channels. Current IDS, however, exhibit three inherent limitations: inadequate modeling of the temporal progression of multi-step attacks, degraded scalability under extremely skewed class distributions of standard benchmark datasets, and restricted generalization across heterogeneous network environments. In this study, we present a Hybrid CNN-LSTM IDS that jointly exploits CNN-based spatial feature extraction and LSTM-based temporal sequence modeling, enabling the detection of instantaneous volumetric anomalies and gradually evolving low and slow-attack campaigns in real time. The model was trained using a seven-step preprocessing workflow comprising missing-value imputation, min-max normalization, one-hot encoding, SMOTE class balancing, mutual-information feature selection, causal temporal sequence construction (T=10), and stratified partitioning. LSTM (96.1

Accurate forecasting of municipal electric vehicle (EV) charging demand is increasingly important for distribution system planning, charging infrastructure management, and demand-side operation. This study proposes a weather-aware Transformer-LSTM hybrid framework for spatio-temporal forecasting of EV charging load across municipal public charging stations. The proposed approach integrates multi-source information within a unified pipeline, including cyclic temporal encodings, multi-lag autoregressive features, rolling statistics, behavioral aggregates, and meteorological variables, while combining a Transformer encoder to capture long-range temporal dependencies with an LSTM decoder to model local sequential dynamics and nonlinear load patterns. The framework was evaluated using 211,324 charging sessions collected from eight New York City municipal charging stations between July 2021 and December 2025. Under controlled benchmarking against Simple RNN, standalone LSTM, and encoder-only Transformer models using identical preprocessing, feature engineering, and training settings, the proposed hybrid model achieved R2 = 0.9731, MAE = 62.71 kWh, RMSE = 94.21 kWh, and MAPE = 19.62%. Relative to the standalone Transformer, the proposed model reduced RMSE by 32.6% and MAPE by 34.5%. In addition, the model maintained strong forecasting performance across stations with heterogeneous demand profiles without station-specific retraining and remained robust across seasonal variations. These results demonstrate that the proposed framework provides a reproducible and scalable solution for municipal EV charging load forecasting in real-world urban environments.