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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Journal of Applied Engineering and Technological Science (JAETS)
Faris A. Alhaddad
Al-Furat Al-Awsat Technical University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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DAM Price-Based Model Predictive Control for Smart EV Charging under Grid and User Constraints Sarab AL-Chlaihawi; Faris A. Alhaddad
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9472

Abstract

The rapid deployment of Electric Vehicles (EVs) has significantly increased grid congestion, particularly in regions with limited capacity for infrastructure expansion where system operators no longer permit customers to extend grid connections. Dynamic energy pricing has emerged to incentivize consumers to optimize energy use through time-of-day tariffs. However, existing smart charging approaches typically optimize grid constraints, cost, or user preferences in isolation, with limited integration of these objectives. This paper proposes a cloud-based Model Predictive Control (MPC) framework for smart EV charging that simultaneously enforces grid power limits, minimizes charging cost, and satisfies user-defined requirements. The proposed method incorporates day-ahead market (DAM) electricity prices, real-time building load, photovoltaic (PV) forecasts, and EV user inputs within a multi-objective optimization problem solved using a receding horizon strategy. The approach is validated through both simulation and a real-world deployment in a commercial building with multiple EV chargers. Results show that the proposed strategy achieves charging cost reductions of up to 95% under favorable overnight pricing conditions and up to 87% in real-world operation with grid constraints, while maintaining user satisfaction. The findings demonstrate the practical feasibility and contribution of an integrated, cloud-based MPC approach for scalable, cost-efficient, and grid-compliant EV charging.
TCM-Former: a transformer with temporal convolution for photovoltaic power forecasting Sarab Al-Chlaihawi; Mohammed A. T. Alrubei; Faris A. Alhaddad
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2127-2148

Abstract

To tackle the problem of modeling long-term trends and short-term and high-frequency variations in PV time series, a strong and efficient forecasting model of photovoltaic (PV) power generation is advanced. This paper presents STL-TCM-Former, a hybrid model that breaks down the raw PV signal with seasonal-trend decomposition with LOESS (STL) into trend and seasonal components. These elements are after that processed via a dual path encoder decoder transformer architecture that is improved with a temporal convolutional module (TCM). The period (transformer-based) path is used for capturing the global, long-range dependencies in seasonal component and temporal (TCM) path is used to extract the localized, short-term dynamics. Trend component is processed with a dedicated TCM branch, minimizing component interferences and providing a multiscale temporal representation, specific to PV generation patterns. The proposed model was evaluated on the Yulara (Uluru) solar dataset under short-term (60-hour) and long-term (300-hour) forecasting horizons. Compared with benchmark models including LSTM, WOA-LSTM, VMD-LSTM, WOA-VMD-LSTM, and hybrid WOA/VMD/LSTM configurations, STL-TCM-former achieved superior performance with R² = 99.76%, MAPE ≈ 2.24%, RMSE = 16.63, and MAE = 10.36. In addition, the PJM interconnection dataset was also employed to evaluate the generalization capability of the proposed method. The results demonstrate high accuracy, stability, and strong generalization capability under varying environmental conditions.
Co-Authors Mohammed A. T. Alrubei Sarab Al-Chlaihawi