Urban Load Curve Transformation Driven by Energy Transition Policies: Electric Vehicles and Distributed Photovoltaics in Panama City
エネルギー転換政策による都市負荷曲線の変容：パナマシティの電気自動車と分散型太陽光発電 (AI 翻訳)
Carlos Boya‐Lara, Omar Rivera‐Caballero, Cindy Galdámez-Lopez
🤖 gxceed AI 要約
日本語
パナマシティを対象に、EVと分散型太陽光発電（PV DG）の大規模導入が都市の電力負荷曲線をどう変えるかを確率的フレームワークで分析。コピュラモデルとカーネル密度推定を用い、ピーク時間が12時から19時へ移行し、配電系統の運用指標（PAR、ランプ率）が悪化することを示した。政策目標が時間構造を捉えていない点を指摘。
English
This study analyzes how large-scale EV and distributed PV deployment, driven by Panama's energy transition policies, reshapes the urban load curve in Panama City. Using copula-based EV charging simulation and kernel density estimation for PV, it finds a systematic peak-hour shift from 12:00 to 19:00 and significant increases in ramp rates, highlighting limitations of aggregate policy targets.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の再エネ導入拡大やEV普及策においても、系統負荷の時間構造変化が重要。特に分散型電源とEV同時導入時の運転計画や系統増強の検討に示唆を与える。
In the global GX context
This paper contributes to global energy transition scholarship by quantifying the temporal load-curve effects of EV and PV integration, which are often missed in capacity-based policy targets. The methodology is transferable to other cities with solar and EV adoption.
👥 読者別の含意
🔬研究者:Provides a probabilistic framework for assessing EV/PV impacts on load curves, with useful indicators (PAR, HRR) for system planning.
🏢実務担当者:For grid operators and utilities, offers quantitative insights on peak shifting and ramping requirements when integrating EVs and solar PV.
🏛政策担当者:Highlights the need for time-resolved targets rather than aggregate energy or capacity metrics in energy transition policies.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.37135/ns.01.18.01first seen 2026-07-31 05:21:34
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。