The integration of electric vehicles (EVs) into distribution networks poses challenges for peak demand management and voltage regulation. This paper presents a hierarchical framework combining deep reinforcement learning (DRL) and model predictive control (MPC) for voltage-aware EV charging coordination. A Proximal Policy Optimization (PPO) agent learns strategic power allocation policies from historical data, while a lower-level MPC layer enforces network constraints using linearized power flow (LinDistFlow) equations. Validation on 873,286 smart meter measurements from 25 Pecan Street households over a full calendar year demonstrates that the proposed method achieves a 6.8% additional peak demand reduction (95% CI: [5.2%, 8.4%]) compared to standalone MPC, while maintaining zero voltage violations under LinDistFlow-enforced constraints, within the validated operating regime. PV self-consumption reaches 72.1% versus 61.2% for MPC-only (p < 0.001, Cohen’s d = 2.00). Comparison against six baselines confirms consistent improvements, though gains are incremental rather than transformative. Computational analysis demonstrates within-interval computational feasibility (211±50 ms) and scalability up to 50 EVs. The results suggest that hierarchical learning–optimization architectures offer modest but statistically significant benefits for network-constrained EV charging coordination.

The maritime industry is under increasing pressure to reduce greenhouse gas emissions, especially in countries such as Saudi Arabia that are actively working to transition to cleaner energy. In this paper, a new hybrid shipboard power system, which incorporates wind turbines, solar photovoltaic (PV) panels, proton-exchange membrane fuel cells (PEMFCs), and a battery energy storage system (BESS) together for propulsion and hotel load services, is proposed. A multi-loop Energy Management System (EMS) based on proportional–integral control (PI) is developed to coordinate the interconnections of the power sources in real time. In contrast to the widely reported model predictive or artificial intelligence optimization schemes, the PI-derived EMS achieves similar power stability and hydrogen utilization efficiency with significantly reduced computational overhead and full marine suitability. By taking advantage of the high solar irradiance and coastal wind resources in Saudi Arabia, the proposed configuration provides continuous near-zero-emission operation. Simulation results show that the PEMFC accounts for about 90% of the total energy demand, the BESS (±0.4 MW, 2 MWh) accounts for about 3%, and the stationary renewables account for about 7%, which reduces the demand for hydro-gas to about 160 kg. The DC-bus voltage is kept within ±5% of its nominal value of 750 V, and the battery state of charge (SOC) is kept within 20% to 80%. Sensitivity analyses show that by varying renewable input by ±20%, diesel consumption is ±5%. These results demonstrate the system’s ability to meet International Maritime Organization (IMO) emission targets by delivering stable near-zero-emission operation, while achieving high hydrogen efficiency and grid stability with minimal computational cost. Consequently, the proposed system presents a realistic, certifiable, and regionally optimized roadmap for next-generation hybrid PEMFC–battery–renewable marine power systems in Saudi Arabian coastal operations.

As Saudi Arabia accelerates its transition to intelligent, sustainable energy systems under Vision 2030, advanced control of renewable microgrids becomes critical. This study proposes an artificial neural network-based hierarchical intelligent control framework for a fully renewable hybrid microgrid powering a residential villa in Jeddah, Saudi Arabia. The system integrates a 36 kW solar PV array, 10 kW wind turbine system, and 100 kWh lithium-ion battery to meet a daily load of 177.5 kWh. Neural networks are embedded across all control layers: MLP-based MPPT for solar and wind sources, NARMA-L2 for battery management, and an intelligent EMS for system-wide coordination. Simulations conducted in MATLAB/Simulink, under realistic variable irradiance (0.1–0.8 kW/m²) and wind speeds (3.5–5 m/s) characteristic of Jeddah, quantitatively demonstrate the superior performance of the proposed framework. Specifically, the MLP-based MPPT controllers achieves a power tracking enhancement of up to 12% compared to conventional MPPT methods, significantly improving energy harvesting efficiency. The NARMA-L2 battery management controller exhibits a reduction in DC bus voltage error by approximately 10% and an improvement in recovery time by over 5% when benchmarked against traditional PI control, underscoring its enhanced stability and responsiveness. Furthermore, the intelligent EMS successfully maintained real-time load balancing and optimized battery operation across five distinct dynamic load scenarios, confirming its robustness. The simulation results validate the effectiveness, resilience, and scalability of the proposed neural network-based hierarchical control system, positioning it as a promising solution for smart residential microgrids in challenging coastal environments.

The gradual substitution of internal combustion engines with electric vehicles (EVs) gives a lot of flexibility potential to the power systems; however, this potential is not fully utilized because there are no utility-deployable, data-driven approaches to the measurement of EV charging flexibility based on already accessible infrastructure of utilities. This paper presents a data-driven framework for constructing flexibility envelopes for pilot-scale residential EV fleet aggregations using smart-meter data without the need for EV-level submetering, direct user input, or vehicle-specific data. The proposed framework integrates non-invasive load monitoring (NILM) and multi-feature clustering to identify EV charging sessions using aggregate whole-home smart-meter readings and to identify prevailing charging behavior patterns. Based on these behaviorally defined clusters, time-dependent power and energy flexibility sheaths are built, therefore defining practicable charging paths, adjustable energy capacity, and peak-reduction potential both at the cluster level and at the fleet level. One year of residential smart-meter data from ten EV-equipped households of the Pecan Street Dataport is used to validate. The findings indicate that EV charging episodes could be determined from 15-minute smart-meter records with high recall (0.83-0.91) and moderate-to-high precision (0.77-0.85), and that the charging behavior will naturally fall into a few explainable patterns with specific flexibility properties. At the fleet scale, the derived envelopes show that about 60% of total EV charging energy could be shifted over time, temporarily allowing up to a 50% of coincident peak charging demand, which allows meeting historical user charging demands. Unlike forecasting-based techniques, the current technique provides operationally significant flexibility limits that can be effectively fitted in demand-response, aggregation, and grid-optimization applications. The results highlight the feasibility of using the available smart-meter infrastructure to describe the flexibility of the EV fleet in residential aggregation settings on a pilot basis.

This paper proposes an advanced metering infrastructure (AMI)-informed hierarchical energy management framework for coordinated operation of electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESS) in campus microgrids. The proposed two-layer architecture integrates a soft actor–critic (SAC) deep reinforcement learning (DRL) agent in the upper layer with a receding horizon model predictive control (MPC) optimizer in the lower layer. The key novelty is an AMI-to-control pipeline that transforms historical 15 min smart-meter measurements into operational flexibility features and embeds them into a hierarchical SAC–MPC architecture, where the DRL layer provides adaptive coordination and the MPC layer enforces grid, storage, and EV-service constraints. The proposed framework using the real-world Pecan Street data (15 min resolution) of 73 homes across Austin, Texas and California (2014–2019) achieves a 53.1% cost reduction and a 25.7% peak demand reduction when compared with uncontrolled charging, and the proposed framework outperforms MPC-only (50.9%), DRL-only (−5.2%), and rule-based (5.1%) baselines. The statistically significant contributions of network-aware constraints, demand-response activation, and predictive look-ahead horizon are statistically significant (n = 10 independent runs) contributions (p = 0.001). The state representation informed by AMI offers directional cost improvement (+8.4%, p = 0.055) with 11% faster convergence of training. The zero network constraint violation is observed in all evaluation scenarios and the average MPC solve time is around 150 ms, which is much less than the 15 min sampling period. Sensitivity analyses show that the hierarchical DRL–MPC architecture remains computationally feasible across EV penetration, seasonal, and forecast-uncertainty scenarios. However, BESS provided no net economic benefit under the evaluated energy-only TOU tariff, increasing weekly cost by $15.25 and peak grid demand by 14.2 kW. Break-even analysis indicates that demand charges of approximately $9.9/kW per month are required for BESS to become cost-effective in the proxy system, highlighting that storage value depends strongly on tariff design and peak-demand objective formulation.

In alignment with Saudi Arabia’s Vision 2030 goals for energy diversification, sustainability, and increased renewable energy penetration, this paper presents an ANN-assisted adaptive droop/Proportional–Integral (PI) control strategy for residential hybrid microgrids integrating photovoltaic (PV) systems, optional micro-wind turbines, and battery energy storage. The proposed scheme is designed for a grid-tied residential microgrid with islanding capability and is modeled and simulated in MATLAB/Simulink. By dynamically tuning droop coefficients and PI gains in real time, the controller enhances system adaptability under fluctuating renewable generation and rapidly varying residential loads. Simulation results demonstrate that, compared with conventional fixed-gain drop/PI control, the proposed approach improves renewable energy utilization by approximately 15%, increases PV self-consumption by 20%, and reduces peak grid import by about 18%, while maintaining stable voltage and frequency regulation. Efficient energy shifting is achieved through coordinated battery operation, with charging during mid-day solar peaks and discharging during evening demand; battery power reaches up to 5 kW, and State of Charge (SoC) is maintained within 35–90% limits. An Internet of Things (IoT) monitoring layer implemented using ThingSpeak provides near real-time telemetry, with median communication latency below 500 ms and full command success rate. A detailed case study based on the climatic conditions and AC-dominated residential load profiles of Jeddah, Saudi Arabia confirms reduced PV curtailment, improved self-consumption, and decreased reliance on grid imports during peak demand periods. The results highlight the effectiveness of ANN-driven adaptive control combined with secure IoT monitoring in enhancing the performance and operational resilience of residential microgrids in challenging environments.