Recent advancements in photovoltaic (PV) and battery technologies, combined with improvements in power electronic converters, have accelerated the adoption of rooftop PV systems and electric vehicles (EVs) in distribution networks, while these technologies offer economic and environmental benefits and support the transition to sustainable energy systems, they also introduce operational challenges, including voltage fluctuations, increased system losses, and voltage regulation issues under high penetration levels. Traditional Voltage and Var Control (VVC) strategies, which rely on substation on-load tap changers, voltage regulators, and shunt capacitors, are insufficient to fully manage these challenges. This study proposes a novel Voltage, Var, and Watt Control (VVWC) framework that coordinates the operation of PV and EV resources, conventional devices, and demand responsive loads. A mixed-integer nonlinear multi-objective optimization model is developed, applying a Chebyshev goal programming approach to balance objectives that include minimizing PV curtailment, reducing system losses, flattening voltage profile, and minimizing demand not met. Unserved demand has, in particular, been modeled while incorporating the concepts of distributional and recognition energy justice. The proposed method is validated using a modified version of the IEEE 123-bus test distribution system. The results indicate that the proposed framework allows for high levels of PV and EV integration in the grid, while ensuring that EV demand is met and PV curtailment is negligible. This demonstrates an equitable access to energy, while maximizing renewable energy usage.

False data injection attacks represent a serious cyber-physical security challenge for modern smart grids because falsified measurements can affect state estimation, energy management, and operational decision-making. While various machine learning-based FDIA detection methods have been investigated in literature, many studies report biased results from leakage-prone experiments without rigorous statistical analysis. In this paper, a framework of leakage-safe and physics-informed machine learning for FDIA detection and operational cyber-resilience of renewable smart grids is proposed. The leakage-safe paired normal/attack dataset was constructed using CAISO-derived IEEE 118-bus simulation scenarios integrated with renewable and EMS-related variables. Metadata fields, including scenario ID, sample type, target label, attack severity, and number of attacked buses, were excluded from the model input feature matrix. The framework combines bus-level measurements, grid statistics, physics residual features, and indicators of renewable and EMS and tests Logistic Regression, Random Forest, Extra Trees, HistGradientBoosting, and XGBoost models. The proposed XGBoost model achieved 93.47% accuracy, 93.35% F1-score, 0.9813 ROC-AUC, and 0.9847 PR-AUC on the leakage-safe grouped test set. The repeated grouped split validation indicated good stability with a mean accuracy of 93.81% ± 0.10%. Holm-corrected McNemar tests indicated statistically significant paired-prediction differences between XGBoost and HistGBM, RF, and ET, while the difference between XGBoost and Logistic Regression was not statistically significant. Ablation study, bootstrap confidence intervals, attack severity analysis, and cyber-resilience index provide additional support for evaluation transparency. The results support the feasibility of leakage-safe machine-learning evaluation for FDIA detection under the simulated renewable smart-grid setting.

Day-ahead photovoltaic (PV) forecasting must resolve the coexistence of a smooth diurnal generation envelope and shorter-scale fluctuations while providing uncertainty information suitable for operational decision making. This paper develops a multi-resolution Wavelet–CNN–LSTM framework for 24 h ahead PV power forecasting and evaluates it using a five-year PVGIS-derived dataset for a 334.116 MW Rabigh case study. The forecasting information set is restricted to the preceding 24 h of PV power and meteorological observations, together with deterministic target-day temporal variables. A validation study selects a Haar level-2 decomposition of PV history into $A_2$, $D_2$, and $D_1$ components, which are processed through parallel convolutional branches before recurrent fusion. The resulting network contains 87,032 trainable parameters. On the 2023 temporal evaluation period, the proposed model achieves an RMSE of 17.228 MW, $R^2=0.9641$, and a daylight RMSE of 24.636 MW, corresponding to 25.69% forecast skill relative to 24 h seasonal persistence. A 7-day moving-block bootstrap confirms clear RMSE reductions relative to persistence and XGBoost, while the intervals for LSTM and CNN–LSTM include zero, indicating statistically comparable performance among the strongest recurrent models. Probabilistic forecasts obtained from 100-pass Monte Carlo dropout yield daylight PICP = 0.9279, MPIW = 84.06 MW, and Winkler score = 122.08 for nominal 90% intervals; an independent conformal calibration set requires no additional widening of the neural interval. The results show that multi-resolution representation can improve squared-error performance without sacrificing model compactness, while calibrated uncertainty provides a complementary measure of forecast reliability.