DQNアルゴリズムに基づく港湾バーチャルパワープラントの最適運用戦略：電力・炭素取引の二重影響を考慮して
Optimal Scheduling Strategy for Port Virtual Power Plant Based on DQN Algorithm: Considering the Dual Impact of Electricity–Carbon Trading (原題)
Yinchen Zhang, Ruoli Tang, Gang Li, Siwen Ning, Xin Li, Yan Zhang
🤖 gxceed AI 要約
日本語
港湾バーチャルパワープラント（VPP）が電力市場と炭素市場の双方に参加する際の協調最適化に取り組む。風力・太陽光・蓄電池・多種負荷を統合した物理モデルと、ガス発電や系統電力購入等を精緻化した炭素会計モデルを構築。DQNアルゴリズムによる前日・当日の二段階運用で、経済性と炭素削減を両立できることをケーススタディで示した。
English
This paper proposes a two-stage day-ahead/intraday scheduling strategy using a deep Q-network (DQN) for a port virtual power plant participating in both electricity and carbon trading markets. It builds a physical model integrating wind, PV, storage and loads, plus a refined carbon accounting model for gas generation and grid purchases. Case studies show economic gains and notable emission reductions with strong adaptability to renewable and load uncertainty.
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
港湾という産業拠点での電力・炭素市場統合運用は、日本でもGX推進やカーボンプライシング導入議論と親和的。炭素会計の精緻化はSSBJ開示やScope1/2算定の実務にも示唆を与える。
In the global GX context
Integrating electricity and carbon markets at industrial ports speaks to global carbon-pricing and disclosure debates, and the refined carbon accounting model is relevant to Scope 1/2 measurement under ISSB/CSRD.
👥 読者別の含意
🔬研究者:電力・炭素市場の協調最適化に強化学習を適用する際の二段階枠組みと炭素会計モデルの設計例として参考になる。
🏢実務担当者:港湾・産業拠点のエネルギー運用チームが、蓄電池・ガス発電の運用と炭素コストを同時に最適化する発想を得られる。
🏛政策担当者:炭素市場と電力市場の制度設計が拠点レベルの脱炭素運用に与える影響を検討する材料になる。
📄 Abstract（原文）
Abstract In order to address the challenge of cooperative optimization of port virtual power plants (VPPs) participating in both electricity and carbon trading markets, this paper proposes a two-stage day-ahead–intraday optimal scheduling strategy using the deep Q -network (DQN) algorithm strategy, which can achieve both economic and environmental benefits for the port. First, a physical model of a port VPP was constructed in which the wind and photovoltaic (PV) power, energy storage systems (ESS), and multiple types of loads are integrated. Second, a detailed carbon accounting model was developed to refine current carbon accounting methods and quantify in detail the carbon emissions from the port’s energy system, i.e., gas-fired power generation, grid electricity purchases, and other sources. Based on this, a two-stage scheduling framework is proposed. During the day-ahead stage, the DQN algorithm is employed to derive the optimal day-ahead electricity sales declaration curve. In the intraday stage, according to real-time deviations, the DQN algorithm is reapplied to dynamically adjust the charging and discharging power of energy storage and the output of gas-fired units, while incorporating an ε -greedy strategy to address uncertainties. Finally, the effectiveness of the developed method was verified with a comprehensive set of case studies. Experimental results show that the developed method can achieve considerable economic benefits as well as remarkable reduction of carbon emissions. The DQN algorithm has strong adaptability to renewable power fluctuation and load uncertainty, which provides an effective solution for port VPP in multimarket cooperative trading.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1061/jleed9.eyeng-6710first seen 2026-10-31 04:56:06 · last seen 2026-11-06 04:47:13
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