Multiobjective optimization of rural multienergy microgrids using offline deep reinforcement learning
深層強化学習を用いた農村マルチエネルギーマイクログリッドの多目的最適化 (AI 翻訳)
Xin Fang
🤖 gxceed AI 要約
日本語
本研究は、バイオマス・バイオガス・ディーゼル発電機の特性をモデル化し、炭素取引費用や補助金を目的関数に組み込んだ多目的最適化フレームワークを提案。オフライン深層強化学習（SAC, TD3, DDPG）を用いて、総コスト、炭素排出、電力バランスを同時最適化。感度分析により、炭素価格と補助金の弾性値を示した。
English
This study models biomass, biogas, and diesel generators and proposes a multi-objective optimization framework incorporating carbon trading costs and subsidies. Using offline deep reinforcement learning (SAC, TD3, DDPG), it simultaneously optimizes total cost, carbon emissions, and power balance. Sensitivity analysis reveals cost elasticities for carbon pricing and subsidies.
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
日本でも農村部でのバイオマス発電やマイクログリッド導入が進んでおり、炭素価格や補助金設計に示唆を与える。特に、オフラインデータを活用した深層強化学習手法は、データが少ない日本の農村地域にも応用可能。
In the global GX context
This paper provides an optimization framework for rural microgrids with carbon trading and subsidies, relevant for global energy transition and carbon pricing policy design. The use of offline deep reinforcement learning is novel and applicable to data-scarce environments.
👥 読者別の含意
🔬研究者:Demonstrates the effectiveness of offline DRL for multiobjective microgrid optimization, offering a reusable modeling pathway.
🏢実務担当者:Provides a practical framework for optimizing microgrid operations with carbon costs and subsidies, aiding cost-effective decarbonization.
🏛政策担当者:Offers quantitative insights on carbon price and subsidy elasticities, useful for designing effective incentive mechanisms.
📄 Abstract（原文）
Amid the global transition toward renewable energy, rural microgrids offer a vital pathway to sustainable and accessible electricity supply. This study models the operational and carbon emission characteristics of biomass, biogas and diesel generator sets, proposing a unified multi-dimensional dynamic coupling optimisation framework. This framework incorporates carbon trading costs (65–70 RMB/tonne), government subsidies (0.04–0.05 RMB/kWh), waste recovery economic, and real-time electricity prices into the objective function. Adjustable parameters enable adaptation to rural microgrids of varying scales. To address the scarcity of rural scenario samples and the prevalence of offline data, off-policy deep reinforcement learning algorithm (SAC, TD3, DDPG) is employed for coordinated scheduling within the MDP framework, simultaneously optimising total system cost, carbon emissions, and power balance. Experiments demonstrate that during the low-sample initial phase, SAC converges faster due to its maximum entropy policy and randomised action sampling. TD3's dual-Q structure effectively mitigates Q-value overestimation and exhibits marginally superior power balance control in the early stages. With sufficient training, all three methods converge to nearly identical operational costs and zero power imbalance, validating the efficacy of offline DRL in multi-objective rural microgrid optimisation. Sensitivity analysis indicates a cost elasticity of approximately +1.96 for carbon pricing and -0.93 for subsidies. Under a combined shock of ±20%, the cost fluctuation range spans from -42% to +62%, rather than ¥501,730.74, providing actionable policy recommendations for subsidy/carbon price design. This research provides a reusable modelling and optimisation pathway for low-carbon, low-cost, and reliable operation of rural microgrids.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1117/12.3096019first seen 2026-05-15 17:26:21
🔔 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。