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Rule-based energy management strategies for a hybrid microgrid using grey wolf optimizer

ハイブリッドマイクログリッドのためのグレイウルフ最適化器を用いたルールベースエネルギー管理戦略 (AI 翻訳)

Abdulsattar, Sarmid Shakir, Tan, Chee Wei, Ayob, Shahrin, Abdulsattar, Yasir Shakir, Dahiru, Ahmed Tijjani, Gan, Chin Kim, Lau, Kwan Yiew

Zenodoプレプリント2026-06-01#エネルギー転換Origin: Global経営インパクト: コスト削減対象セクター: power
DOI: 10.11591/ijape.v15.i2.pp858-879
原典: https://zenodo.org/records/20638128
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🤖 gxceed AI 要約

日本語

本研究は、住宅用ハイブリッドマイクログリッドのルールベースエネルギー管理にグレイウルフ最適化器（GWO）を適用し、均等化発電原価（LCOE）の低減と最適な構成要素数を達成した。GWOは他の最適化手法と比較して30～120%のコスト削減を示した。将来的には顧客満足度を考慮した高度な管理が期待される。

English

This study applies a grey wolf optimizer (GWO) to rule-based energy management of a residential hybrid microgrid (PV, wind, battery, diesel), aiming to minimize levelized cost of energy (LCOE) and determine optimal component sizing. GWO outperforms ALO, PSO, and CSA with 30-120% cost reduction. Future work envisions customer-centered management.

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

日本の再生可能エネルギー導入拡大や分散型電源管理において、LCOE低減と最適化手法の実証として参考になる。特に、需要家側マイクログリッドの導入促進に寄与する可能性がある。

In the global GX context

This paper provides a benchmark for microgrid optimization using metaheuristic algorithms, relevant to global energy transition efforts. The cost reduction results highlight the potential of rule-based management in reducing renewable energy system costs.

👥 読者別の含意

🔬研究者:Compares GWO with other optimizers for microgrid sizing and energy management, offering insights for algorithmic selection.

🏢実務担当者:The rule-based management strategy and cost analysis can guide real-world microgrid deployment for residential systems.

📄 Abstract（原文）

This study utilizes grid-connected microgrids using photovoltaics (PVs) and wind turbines (WTs) in a residential system. For improved reliability, the system uses battery storage and diesel generators (Dgen). The proposed system uses supervisory controllers (as a rule-based energy management system) for energy management strategy implementations. The essence of using the grey wolf optimizer (GWO) is to strategize the rule-based energy management system in the proposed microgrid operations. The primary objectives are to achieve a low levelized cost of energy (LCOE) and determine the optimal number of microgrid components. The performance of the GWO is compared with three other optimization algorithms, namely, antlion optimizer (ALO), particle swarm optimizer (PSO), and cuckoo search algorithm (CSA), for benchmarking purposes. The findings indicate that the proposed GWO supersedes ALO, PSO, and CSO in energy cost reduction by 30.3% (0.0448 $/kWh), 65.6% (0.0971 $/kWh), and 120% (0.1774 $/kWh), respectively. The suggested algorithm selects the optimum number of the system’s components, which is 46 PV modules, 30 wind turbines, and 10 units of batteries. An improved GWO-based algorithm based on hybridization with gradient descent algorithms is envisaged to implement a customer-centered energy management that can ensure customer satisfaction and further reduce energy cost.

🔗 Provenance — このレコードを発見したソース

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