A framework for multi-interval optimal power flow under solar energy penetration
太陽光エネルギー導入下での多期間最適電力潮流のフレームワーク (AI 翻訳)
Ricky Maulana, Syafii, Aulia
🤖 gxceed AI 要約
日本語
本論文は、SARIMAモデルによる太陽光発電予測と多期間最適電力潮流を統合したフレームワークを提案。予測誤差は低く、PV導入により系統運用コストが削減されることを実証。従来の単期間最適化と比較して平均コストが低く、太陽光変動を反映した実用的なツールを提供。
English
This paper proposes a framework integrating SARIMA-based PV forecasting with multi-interval optimal power flow. Forecast errors are low, and PV integration reduces system operating costs. Compared to conventional single-interval OPF, the framework achieves lower average costs and reflects solar variability, offering a practical tool for system planning.
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
日本では太陽光発電の大量導入に伴い系統運用の課題が顕在化しており、本フレームワークは日本の系統計画や需給調整に応用可能。ただし、日本の実データによる検証が必要。
In the global GX context
As solar penetration increases globally, such forecasting-optimization frameworks are critical for reliable and economical grid operation. This work provides a methodology that can be adapted to different grid contexts and support renewable integration.
👥 読者別の含意
🔬研究者:Methodology for integrating time-series forecasting with optimization for renewable integration.
🏢実務担当者:Utility operators and system planners can use this framework for day-ahead scheduling with solar PV.
📄 Abstract（原文）
The increasing penetration of renewable energy introduces variability and uncertainty into power system operations, thus requiring accurate forecasting methods to ensure reliable and economical scheduling. This study presents a multi-interval day-ahead optimal power flow (OPF) analysis integrated with photovoltaic (PV) generation, where hourly PV forecasts are obtained using the seasonal autoregressive integrated moving average (SARIMA) (1,0,1)(4,0,3)24 model. The forecast results achieved low error values (root mean square error (RMSE)=0.354, normalized RMSE (NRMSE)=4.192%, mean absolute error (MAE)=0.202), successfully capturing the daily PV generation pattern and providing sufficiently accurate input for the OPF simulation. The forecasted PV profiles were then integrated into a multiinterval OPF framework using the MATPOWER interior point solver (MIPS) solver. Results show that PV integration reduces system operating costs compared to cases without PV, with cost savings observed at various time intervals (e.g., reduction from $802.22/hour to $780.65/hour during PV peak hours). Compared to the conventional single-interval OPF benchmark based on Weibull distribution assumptions for PV, the proposed framework achieves lower average costs ($790.97/hour vs. $869.70/hour) while also reflecting the real variability of solar dynamics and load. Overall, the integrated forecasting-optimization framework demonstrates that SARIMAbased PV forecasting provides reliable inputs for OPF and offers a practical tool to support future system planning and operation with higher renewable energy penetration.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/21051569first seen 2026-06-30 04:26:49 · last seen 2026-07-01 04:17:49
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