Co-Simulation and Digital Twin-Based Approaches for Evaluating Grid Modernization Impacts on Load Forecasting and Operational Efficiency

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@article{1709542,
      author = {Arjun Pedapati},
      title = {Co-Simulation and Digital Twin-Based Approaches for Evaluating Grid Modernization Impacts on Load Forecasting and Operational Efficiency},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {12},
      pages = {589-598},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709542.pdf},
      abstract = {The modernization of power grids at an accelerated pace requires high-tech solutions to handle complexity, variability, and cyber-physical integration. It is a synergistic effect that co-simulation and digital twin technology have, and the impact each makes, both separately and collectively, in improving load forecasting and operational efficiency in contemporary energy systems. Co-simulation, which links together modular simulations in layers of power, communication, and market, can provide end-to-end system evaluation and stress-testing under more realistic conditions. At the same time, digital twins are dynamic data-driven models of physical assets that provide predictive capabilities, real-time diagnostics, and enhanced decision-making capabilities. This study examines the evolution of forecasting loads over the years, the development of new efficiency measures, and presents some successful case studies of real-world applications of these technologies. It also pinpoints some crucial implementation issues (data integration, concept scalability, and cybersecurity risks). In the future, new-generation inventions, such as edge computing combined with AI and quantum machine learning, hold the potential to continue exponential growth. This paper contributes to the existing discussion by offering an in-depth analysis of how co-simulation and digital twins can be utilized to promote resilient, smart, and efficient grid operation.},
      keywords = {Grid modernization; co-simulation; digital twin; load forecasting; operational efficiency; distributed energy resources; predictive analytics; cyber-physical systems; smart grid; energy systems modeling},
      month = {June},
  }