積み重ね可能なサービスに向けた蓄電池システムの多目的配置・容量最適化
Multi-objective placement and sizing of battery energy storage systems for stackable services (原題)
Villanueva Panocca, Hennry Gonzalo
🤖 gxceed AI 要約
日本語
蓄電池（BESS）の初期投資回収のため、周波数調整などのアンシラリーサービスを単独または積み重ね（スタッカブル）で提供する際の最適配置・容量を、財務・技術・運用の多目的で決定する手法を提案。PJMの周波数調整市場を前提に、240ノードの実配電系統をOpenDSSとPythonで評価し、パレート最適解をk-means++でクラスタリングして代表解を抽出した。結果、最適容量はサービス依存で3000kWh/750kWが最多、配置は三相不平衡の大きいノード2016〜2018付近に集中する傾向を示した。
English
This study proposes a multi-objective method to optimally place and size battery energy storage (BESS) for ancillary services, individually or stacked, balancing financial, technical and operational criteria. Using the PJM frequency-regulation market and a real 240-node distribution feeder simulated in OpenDSS/Python, it derives Pareto-optimal solutions and applies k-means++ clustering to select representative ones. Optimal sizing is service-dependent (3000 kWh/750 kW most frequent), and placements consistently cluster near the most unbalanced three-phase nodes (2016–2018).
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
蓄電池の収益性確保は日本のGX推進（再エネ大量導入・系統柔軟性・容量市場）でも中心的論点。積み重ね型サービス設計は、日本で議論される蓄電池のアグリゲーションや需給調整市場の制度設計に示唆を与える。
In the global GX context
BESS stacking is central to global transition finance and grid-flexibility debates, where revenue certainty underpins investment. The Pareto/clustering framing offers a replicable method for regulators and developers assessing storage business models under ancillary-service markets like PJM.
👥 読者別の含意
🔬研究者:多目的最適化とパレート解クラスタリングを蓄電池配置問題に適用する手法として参考になる。
🏢実務担当者:蓄電池投資の回収設計や積み重ねサービスの収益モデル検討に活用できる。
🏛政策担当者:需給調整市場や蓄電池アグリゲーション制度設計の際、配置・容量の最適化根拠として参照可能。
📄 Abstract（原文）
Artículos en revistas ; Battery energy storage systems (BESS) support the flexibility of the energy transition through their ability to store and deliver energy when required. However, the high initial investment remains challenging for stakeholders with revenue recovery requirements. To address this, the provision of ancillary services has become a necessity. This study proposes a multi-objective approach that considers financial, technical, and operational aspects to determine the optimal placement and sizing of BESS to provide ancillary services, either individually or as stackable services. Power-quality indicators are used to assess the impact of BESS on the voltage variability at the nodes and the unbalance across the three-phase lines of the distribution system. The proposed approach utilizes a brute-force algorithm to explore a search space defined by commercially available BESS sizes and distribution system placements. This strategy generates a set of non-dominated optimal solutions based on Pareto optimality. A clustering-based approach using k">-means++ is proposed to systematically select representative solutions from the Pareto front. The model is validated on the basis of the frequency-regulation market structure of the Pennsylvania–New Jersey–Maryland Interconnection. A real 240-node distribution system is used for evaluation using OpenDSS with Python. The results indicate service-dependent optimal sizing, with the 3000 kWh/750 kW BESS as the most frequent Pareto-optimal size, while optimal placement consistently concentrates around nodes 2016, 2017, and 2018, near the most unbalanced three-phase lines, for both individual and stackable services. This pattern suggests that, across the analyzed services, Pareto-optimal placements tend to be located near unbalanced areas of the distribution system. ; info:eu-repo/semantics/publishedVersion
🔗 Provenance — このレコードを発見したソース
- base https://doi.org/10.1016/j.segan.2026.102333first seen 2026-10-06 13:40:25 · last seen 2026-10-06 13:43:18
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