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Research on Multi-Objective Optimal Scheduling of Low-Carbon Park Integrated Energy System Considering Wind-Solar-EV Coupling

風力・太陽光・EV連携を考慮した低炭素パーク統合エネルギーシステムの多目的最適スケジューリングに関する研究 (AI 翻訳)

Yuhua Zhang, Jianhui Wang, Hua Xue

Processes📚 査読済 / ジャーナル2026-04-30#エネルギー転換Origin: CN
DOI: 10.3390/pr14091464
原典: https://doi.org/10.3390/pr14091464

🤖 gxceed AI 要約

日本語

本論文は、複数の蓄エネルギーとV2Gコンポーネントを備えたパーク統合エネルギーシステム（PIES）モデルを構築し、適応型包括適応度多目的粒子群最適化アルゴリズムを提案する。風力・太陽光出力に基づくEVスケジューリング境界と動的充放電インセンティブ機構を設計し、再エネ受け入れ能力を向上させる。シミュレーションにより、提案モデルが炭素排出コスト、運用コスト、風力・太陽光出力抑制率で最適な性能を達成することを確認。

English

This paper presents a Park Integrated Energy System (PIES) model with multiple energy storage and V2G components, and proposes an adaptive comprehensive fitness multi-objective particle swarm optimization algorithm. EV scheduling boundaries based on wind and PV output and a dynamic charging-discharging incentive mechanism are designed to enhance renewable energy accommodation. Simulations verify optimal performance in carbon-emission cost, operation cost, and wind-solar curtailment rate.

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

日本の低炭素パークやマイクログリッドの運用最適化に示唆を与える。特にEVを活用した再エネ出力変動対策は、日本の需給調整市場やVPP構想とも関連する。

In the global GX context

This paper provides an operational optimization method for park-scale integrated energy systems with high renewable penetration, relevant to global microgrid design and EV-grid integration. The dynamic incentive mechanism for EV charging-discharging could inform demand response programs in various countries.

👥 読者別の含意

🔬研究者:Researchers in energy system optimization can adopt the adaptive multi-objective algorithm for similar low-carbon scheduling problems.

🏢実務担当者:Park or microgrid operators can use the model to reduce costs and carbon emissions while increasing renewable energy utilization.

📄 Abstract（原文）

To improve the operational efficiency of the park source-load-storage system and reduce operation costs and the wind-solar curtailment rate, this paper establishes a Park Integrated Energy System (PIES) model with multiple energy storage and vehicle-to-grid (V2G) components and proposes an adaptive comprehensive fitness multi-objective particle swarm optimization algorithm. First, each component of the PIES is modeled. Second, electric vehicle (EV) scheduling boundaries, determined by wind and PV output, as well as a dynamic charging-discharging incentive mechanism, are designed to enhance renewable energy accommodation. Finally, an adaptive comprehensive fitness index is defined, and convergence and particle-update strategies are improved to achieve better scheduling performance. Simulation results verify that the proposed PIES model achieves optimal performance in terms of carbon-emission cost, total operation cost, and wind-solar curtailment rate. Meanwhile, the improved algorithm also outperforms traditional multi-objective methods in PIES scheduling.

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