Comprehensive Evaluation of Hybrid Utilization of DT and Virtual Power Plant Platforms for Distributed Energy Resource Management
International Journal of Energy Research, vol.2026, no.1, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Review
- Volume: 2026 Issue: 1
- Publication Date: 2026
- Doi Number: 10.1155/er/3899054
- Journal Name: International Journal of Energy Research
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Compendex, Environment Index, INSPEC, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Earth, Atmospheric, & Aquatic Science Collection (ProQuest), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Keywords: asset management, digital twin, optimization, renewable energy resources, virtual power plant
- Ankara Yıldırım Beyazıt University Affiliated: Yes
Abstract
As the global energy sector seeks to reduce dependence on fossil fuel–based generation and mitigate carbon emissions, there is an increasing need to evaluate advanced enabling platforms that support sustainable energy integration. Technologies, such as digital twin (DT) and virtual power plant (VPP), provide enhanced capabilities for detailed modeling, monitoring, and optimization of modern power systems with high penetration of renewable energy sources (RESs). This study proposes an integrated DT–VPP framework for effective management of distributed energy resources (DERs), particularly photovoltaic (PV) systems, while also presenting a wind turbine (WT) simulation case using distributionally robust optimization (DRO) to improve grid stability under uncertainty. The proposed framework enables real-time asset monitoring, operational optimization, and enhanced decision-making to mitigate technical constraints such as power losses, capacity limitations, and renewable intermittency. Furthermore, the study highlights the role of artificial intelligence (AI)-driven strategies in improving predictive analytics, system reliability, and resilience of future smart grids. The findings demonstrate that the integration of DT, VPP, and AI-based optimization techniques can significantly enhance grid stability, operational flexibility, and sustainable energy management.