The state estimation of an active distribution network (ADN) in the absence of enough measurements, and with the presence of distributed energy resources (DERs) located behind-the-meter (BTM), is a significant challenge. The operational philosophy of power distribution networks is also changing from "generation following demand" to "demand following generation". Consequently, emerging demand and generation patterns are difficult to handle through standard state estimators. This paper introduces a profiling framework that characterises responsive/flexible demands and BTM DERs to represent non-conventional load power. The impact of these non-conventional load power on distribution network monitoring and the challenges of system observability are addressed using a deep learning-based forecaster. This forecaster utilises weather and other relevant input features to tackle the irregularities in demand profiles. Additionally, the framework includes proposing a strategy for time-varying smoothing parameters in forecasting-aided state estimation to address the uncertainties associated with loads and the outputs of DERs. The framework is validated on a modified IEEE-123 bus unbalanced three-phase distribution network for demonstrating improved accuracy of the estimation.

Saudi Arabia’s Vision 2030 includes various pillars in its programs, one of which is to strengthen and diversify its economy. Improving the energy sector in Saudi Arabia by including renewable resources will expand Saudi’s energy mix, enhancing its economy. In this paper, a detailed evaluation of the social aspect of deploying the first large-scale photovoltaics (PV) solar project in the Kingdom of Saudi Arabia is discussed. Furthermore, a survey which contained ten multiple choice questions covering two aspects had been conducted among Saudis to measure the social acceptance of Al-Jouf Solar Project.

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.

Distribution system state estimation (DSSE) is essential in active distribution networks (ADNs). However, new emerging load demands, including the profiles of behind-the-meter (BTM) resources and responsive loads, increase the uncertainty during pseudo-measurement generation. Most existing methods struggle to accurately generate pseudo measurements of load demands incorporating the BTM resources. To address this, a dynamic equivalent model (DEM) for each node within the distribution system is constructed using the Numerical algorithm for Subspace State Space System IDentification (N4SID) algorithm to mimic the dynamics of these nodes. These DEMs are then integrated into an unscented Kalman filter (UKF) to predict and refine the measurements. The output of the UKF stage is utlised to perform the state estimation process using forecasting-aided state estimation (FASE). The proposed approach was tested and validated using the modified IEEE-123 tests system. The numerical results demonstrated the effectiveness of the proposed DEMs, reducing the mean absolute error (MAE) from 5.90 kW, achieved by the state-of-the-art deep learning models, to 2.69 kW with UKF prediction and further to 1.64 kW with UKF correction.