再生可能エネルギーシステムと定置型蓄電システムを備えた電気自動車充電ステーションのスマート配電網における経済的規模決定と立地選定：環境モデルを考慮して
Economic Sizing and Siting of Electric Vehicles Charging Stations with Renewable Energy Systems and Stationary Storage Systems in Smart Distribution Network considering Environmental Model (原題)
Mayoof Alshadood D, Abdulkareem Khamis H, Ayad Kiddm W, Pirouzi S
🤖 gxceed AI 要約
日本語
再エネ・バイオ廃棄物発電・蓄電池・V2Gを統合したEV充電ステーションの確率的計画手法を提案。年間の経済・環境コストと配電網のエネルギー損失を二目的最適化し、需要・再エネ出力・価格・EV挙動の不確実性をシナリオで表現する。Red Panda最適化とCuckoo探索のハイブリッドで求解し、運用条件を最大65%改善、計画コストを最大45.7%削減できることを示した。
English
This study proposes a stochastic bi-objective framework for planning an EV charging station integrating wind, PV, a bio-waste generator, battery storage, V2G, and a smart distribution network. It minimizes annualized economic-environmental cost and network energy losses under demand, renewable, price, and EV-behavior uncertainty, solved via a hybrid Red Panda Optimizer–Cuckoo Search. Results show up to 65% operating-condition improvement and 45.7% planning-cost reduction.
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
EV充電インフラと再エネ・蓄電池の統合計画は、日本のGX推進（再エネ大量導入・配電網制約・V2G実装）に直結する。SSBJ開示や有報での気候関連リスク・機会の定量化において、こうした経済性・環境性の同時評価モデルは企業の設備投資判断や自治体のインフラ計画に応用可能。
In the global GX context
As ISSB/TCFD disclosure pushes firms to quantify transition capex and grid-related climate risks, integrated EV-charging-plus-storage planning models offer a quantitative basis for transition plans and infrastructure investment cases. The bi-objective economic-environmental framing also speaks to CSRD-style double materiality by linking financial cost with environmental impact.
👥 読者別の含意
🔬研究者:EV充電・再エネ・蓄電の統合計画における確率的最適化と多目的解法の実装例として参考になる。
🏢実務担当者:充電インフラ投資の経済性と環境負荷を同時評価する際のモデル設計・V2G活用の示唆が得られる。
🏛政策担当者:配電網制約下でのEV充電網整備と再エネ統合を促す制度設計・インセンティブ検討の参考になる。
📄 Abstract（原文）
This study proposes a stochastic framework for the optimal planning and operation of an electric vehicle charging station integrated with wind turbines, photovoltaic systems, a controllable bio-waste generator, battery storage, and a smart distribution network. Vehicle-to-grid capability is incorporated to enable bidirectional energy exchange and coordinated charging. A bi-objective optimization model is formulated to simultaneously minimize the station’s annualized economic-environmental cost and the distribution network’s annual energy losses. The planning cost accounts for investment and maintenance expenditures, grid energy purchases, storage and conversion equipment, and the net environmental impacts associated with bio-waste utilization. The operational and planning constraints include AC optimal power flow and detailed models of renewable generation, storage, power converters, and charging infrastructure. Uncertainties in demand, renewable output, electricity prices, and electric vehicles behavior are represented through scenario-based stochastic optimization. The -constraint technique converts the multi-objective problem into a single-objective formulation, while fuzzy decision-making identifies the preferred compromise solution. A hybrid Red Panda Optimizer–Cuckoo Search algorithm is employed to obtain high-quality solutions with reduced computational burden. Simulation results demonstrate substantial improvements in network performance, with operating-condition enhancement of up to 65%, while coordinated EV energy management reduces station planning costs by up to 45.7%.
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.22541/authorea.15008703/v1first seen 2026-09-12 04:42:47 · last seen 2026-09-21 04:22:24
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