Yenilenebilir Enerji Üretimi ve Fiyat Belirsizliği Altında Endüstriyel Enerji Yönetimi için Hibrit Stokastik-Sağlam Optimizasyon Çerçevesi

Öz

Endüstriyel mikro şebekelerde yenilenebilir enerji kaynaklarının artan yaygınlığı, özellikle fotovoltaik (PV) üretim değişkenliği ve elektrik fiyat dalgalanmaları nedeniyle önemli operasyonel belirsizlikler ortaya çıkarmıştır. Bu belirsizlikler, enerji yönetimi kararlarını karmaşıklaştırır ve verimsiz planlamaya ve artan operasyonel risklere yol açabilir. Bu zorluğun üstesinden gelmek için, bu çalışma endüstriyel enerji yönetim sistemleri için hibrit bir stokastik-sağlam optimizasyon çerçevesi önermektedir. Önerilen model, beklenen sistem performansını ve en kötü durum sapmalarına karşı korumayı aynı anda yakalamak için senaryo tabanlı stokastik programlamayı sağlam optimizasyonla entegre eder. Günlük planlama kararlarının stokastik senaryolar altında belirlendiği, gerçek zamanlı ayarlamaların ise sağlam bir belirsizlik kümesi aracılığıyla güvence altına alındığı iki aşamalı bir karar yapısı geliştirilmiştir. Model ayrıca, endüstriyel enerji planlamasında çevresel hususları yansıtmak için batarya enerji depolama dinamiklerini ve karbon emisyon maliyetlerini de içermektedir. Önerilen yaklaşımın etkinliğini değerlendirmek için PV üretimi ve batarya depolaması bulunan şebekeye bağlı bir endüstriyel mikro şebekeye dayalı bir vaka çalışması yapılmıştır. Deterministik, stokastik ve sağlam modellerle yapılan karşılaştırmalı sonuçlar, hibrit modelin daha iyi maliyet istikrarı, belirsizliğe karşı daha yüksek direnç ve daha iyi yenilenebilir enerji kullanımı sağladığını göstermektedir. Bulgular, güvenilir ve sürdürülebilir endüstriyel enerji yönetimi için hibrit optimizasyon stratejilerinin potansiyelini vurgulamaktadır.

Anahtar Kelimeler

• Hibrit optimizasyonu
• Enerji yönetimi
• PV belirsizliği
• Pil depolama
• Endüstriyel mikro şebekeler

A Hybrid Stochastic-Robust Optimization Framework for Industrial Energy Management under Renewable Generation and Price Uncertainty

Öz

The increasing penetration of renewable energy resources in industrial microgrids has introduced significant operational uncertainties, particularly due to photovoltaic (PV) generation variability and electricity price fluctuations. These uncertainties complicate energy management decisions and may lead to inefficient scheduling and increased operational risks. To address this challenge, this study proposes a hybrid stochastic–robust optimization framework for industrial energy management systems. The proposed model integrates scenario-based stochastic programming with robust optimization to simultaneously capture expected system performance and protection against worst-case deviations. A two-stage decision structure is developed in which day-ahead scheduling decisions are determined under stochastic scenarios, while real-time adjustments are safeguarded through a robust uncertainty set. The model also incorporates battery energy storage dynamics and carbon emission costs to reflect environmental considerations in industrial energy planning. A case study based on a grid-connected industrial microgrid with PV generation and battery storage is conducted to evaluate the effectiveness of the proposed approach. Comparative results with deterministic, stochastic, and robust models demonstrate that the hybrid model achieves improved cost stability, enhanced resilience to uncertainty, and better renewable energy utilization. The findings highlight the potential of hybrid optimization strategies for reliable and sustainable industrial energy management.

Anahtar Kelimeler

• Hybrid optimization
• Energy management
• PV uncertainty
• Battery storage
• Industrial microgrids

Kaynakça

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