Adaptive Preference-Based Multi-Objective Energy Management in Smart Microgrids: A Novel Hierarchical Optimization Framework with Dynamic Weight Allocation and Advanced Constraint Handling
スマートマイクログリッドにおける適応型選好ベース多目的エネルギー管理: 動的重み配分と高度な制約処理を備えた新しい階層的最適化フレームワーク (AI 翻訳)
N. Alshammari, F. Alyami, Sheeraz Iqbal, M. Shafiullah, Saleh Al Dawsari
🤖 gxceed AI 要約
日本語
本研究は、スマートマイクログリッドのエネルギー管理において、運用コストの最小化、二酸化炭素排出削減、電圧安定性向上、電力品質改善、システム信頼性最大化を同時に達成するための適応型多目的最適化フレームワークを提案する。改良型NSGA-IIと動的選好重み配分システムを組み合わせ、再生可能エネルギーの変動性や需要応答などの様々な運用条件に対応できる。IEEE 33バス試験システムでのシミュレーションにより、運用コスト23.7%削減、炭素排出31.2%削減、システム信頼性18.5%向上などの改善を確認した。
English
This paper proposes an adaptive preference-based multi-objective optimization framework for intelligent energy management in smart microgrids, simultaneously minimizing operational cost and carbon emissions while enhancing voltage stability, power quality, and system reliability. The hierarchical algorithm integrates an improved NSGA-II with dynamic preference weight distribution, handling various operational conditions including renewable intermittency and demand response. Simulations on a modified IEEE 33-bus system demonstrate 23.7% cost reduction, 31.2% emission reduction, and 18.5% reliability improvement. The framework shows robustness through sensitivity analysis and is practical for real-world smart grid applications.
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
日本では、GX戦略の一環としてスマートグリッドやマイクログリッドの導入が進んでおり、本フレームワークはコストと炭素排出の両面で最適化を図る手法を提供する。特に福島以降の地域エネルギー自立や災害時対応に資する可能性があり、日本のマイクログリッドプロジェクトへの応用が期待される。
In the global GX context
Globally, this framework addresses the critical need for multi-objective optimization in smart grids integrating high shares of renewables. It demonstrates practical trade-off handling between cost and carbon reduction, which is relevant for grid operators and utilities working towards net-zero targets under various operational scenarios.
👥 読者別の含意
🔬研究者:This paper presents a novel hierarchical optimization framework that advances multi-objective energy management techniques, particularly the integration of dynamic preference weights.
🏢実務担当者:The demonstrated performance improvements offer practical guidance for implementing adaptive energy management systems in microgrids.
🏛政策担当者:The paper underscores the potential of smart microgrid optimization to contribute to decarbonization targets while maintaining grid reliability.
📄 Abstract（原文）
The paper proposed an adaptive preference-based multi-objective optimization framework of intelligent energy management in smart microgrids that are dynamically adapted to operational priorities with regard to real-time grid conditions, stakeholder preferences, and environmental constraints. The suggested hierarchical algorithm combines an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an advanced dynamic preference weight distribution system that can trade off between minimization of operational cost. Reduction of carbon emission, enhancement of voltage stability, enhancement of power quality and maximization of system reliability and adaptability to different operational conditions, such as renewable energy intermittency, demand response schemes and emergencies. The framework presents a new multi-layered preference-learning module that represents the intricate stakeholder priorities in terms of more sophisticated fuzzy logic-based decision matrices, neural network preference prediction, and adaptive reinforcement learning methods and transforms them into dynamic optimization weights with feedback mechanisms. Large-scale simulations on a modified IEEE 33-bus test system coupled with various renewable energy sources, energy storage facilities, electric vehicle charging points, and smart appliances demonstrate superior improvements in performance: 23.7% operational costs reduction, 31.2% carbon emissions reduction, 18.5% system reliability improvement, 15.3% voltage stability increase and 12.8% reduction of deviations in power quality. The proposed system has an adaptive nature with better performance in a variety of operating conditions such as peak demand times, renewable energy intermittency events, grid-connected and islanded operations, emergency load shedding situations, and cyber–physical security risks. The framework is shown to be highly effective under different conditions of uncertainty and variation in parameters and communication delay through intense sensitivity analysis and robustness testing, thus demonstrating its practical applicability in real-world applications of smart grids.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18073591first seen 2026-05-15 17:35:51
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