A Low-Carbon Operation Optimization Method for Distribution Systems Considering the Potential of Distributed Flexible Resource Regulation
Long Shi, Yang Zheng, Chao Xu, Jianzhong Zou, Jingjing Bai
🤖 gxceed AI 要約
日本語
本論文は、分散型フレキシブルリソース（EV、PV、蓄電池）の調整潜力を考慮した配電系統の低炭素運用最適化手法を提案。時間変動外部近似による集約モデルで大規模リソースの複雑性を低減し、ネットワーク損失と炭素排出コストを最小化する協調最適化を実現。シミュレーションにより、ピークカット・バレーフィリング効果とコスト・排出削減を確認。
English
This paper proposes a low-carbon operation optimization method for distribution systems that considers the regulation potential of distributed flexible resources (EVs, PV, storage). An aggregation model using time-varying outer approximation reduces complexity. A collaborative optimization minimizes network losses and carbon emission costs. Simulations confirm peak shaving, valley filling, and cost/emission reductions.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
In the global GX context
This paper contributes to the global effort on low-carbon power system operation by integrating multiple distributed resources and carbon costs into optimization. It is relevant for grid operators and utilities seeking to reduce emissions through operational changes.
👥 読者別の含意
🔬研究者:Provides a novel aggregation and optimization method for low-carbon distribution system operation, useful for further research on multi-resource coordination.
🏢実務担当者:Grid operators can apply the proposed method to reduce losses and carbon emissions by coordinating EV charging, PV, and storage.
🏛政策担当者:Highlights the potential of distributed flexible resources in achieving grid decarbonization, informing policies on DER integration and carbon pricing.
📄 Abstract（原文）
Abstract The large-scale integration of distributed resources poses challenges for distribution systems, including insufficient carrying capacity and increased network losses. To address these issues while balancing economic costs, this paper proposes a low-carbon operation-optimization method for distribution systems that accounts for the regulatory potential of distributed flexible resources. To overcome the “curse of dimensionality” and the prediction difficulties associated with modeling large-scale, flexible loads, an aggregation model based on a time-varying outer approximation is adopted. This model constructs aggregated constraints independent of resource scale using the theory of feasible region aggregation in scheduling, thereby significantly reducing complexity. Furthermore, a collaborative optimization model is established to minimize network loss and carbon-emission costs, coordinating various flexible resources, including aggregated electric vehicle loads, distributed photovoltaics, and energy storage. Simulation results demonstrate that the proposed method effectively achieves “peak shaving and valley filling” and significantly reduces total operating costs and carbon emissions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1088/1742-6596/3229/1/012017first seen 2026-05-29 05:03:05 · last seen 2026-05-31 05:22:37
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