Urban Load Curve Transformation Driven by Energy Transition Policies: Electric Vehicles and Distributed Photovoltaics in Panama City
DOI:
https://doi.org/10.37135/ns.01.18.01Keywords:
Load curve, Photovoltaic generation, Probabilistic modeling, Urban energy systems, Electric vehiclesAbstract
This study analyzes how large-scale integration of electric vehicles (EVs) and distributed photovoltaic generation (PV DG), promoted through national energy transition strategies initiated in the early 2020s, transforms the urban electricity load curve in Panama City. The study applies a probabilistic, policy-oriented framework in which EV charging demand is simulated using copula-based modeling based on mobility data, while PV generation variability is represented using hour-specific kernel density estimation (KDE) calibrated to 689 days of measured PV output. Policy-aligned deployment scenarios are evaluated by constructing net load curves and computing operational indicators associated with peak concentration, ramping behavior, and peak-hour displacement. Results show a systematic shift of the peak hour from 12:00 to 19:00 across all scenarios (+7 h). The Power-Average Ratio (PAR) increases from 1.17 in S1 to 1.64 in S9, while the Hourly Ramp Rate (HRR) rises from 4.12 MW/h in S1 to 102.54 MW/h in S9. Net demand at 12:00 decreases across high-PV scenarios, with the largest reductions of −74.28% in S7 and −74.15% in S9, both relative to the 2024 baseline. Net demand at 19:00 increases with EV adoption, reaching +22.70% in S9. These results show that policy-driven EV and PV DG deployment reshapes the temporal structure of urban electricity demand and generates load-curve effects that are not captured by aggregate energy- or capacity-based policy targets.
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