Hybridized Energy Management and Power Forecasting in Grid-tied Solar Photovoltaic and Wind Turbine With Electric Vehicles

Strategic Insights -

Research on hybridized energy management and power forecasting for grid-tied solar photovoltaic and wind turbines with electric vehicles has been conducted by a team of researchers from the School of Computer Science. The study highlighted the challenges posed by the integration of renewable energy sources and electric vehicles into grid systems, including energy forecasting, power management, and system stability. To address these challenges, the researchers proposed a novel hybridized energy management and power forecasting scheme that involves a two-way communication model to facilitate real-time electricity price and power coordination among solar photovoltaic, wind turbines, energy storage devices, electric vehicles, and industrial loads.

Key Takeaways:

  • The research emphasized the need for novel energy management and power forecasting schemes to enhance grid resilience, sustainability, and efficiency in the face of increasing uncertainty from renewable energy sources and dynamic load behavior of electric vehicles.
  • The proposed hybridized energy management and power forecasting scheme involves a two-way communication model to facilitate real-time electricity price and power coordination among solar photovoltaic, wind turbines, energy storage devices, electric vehicles, and industrial loads.
  • The research team developed a kernel-based nonparametric energy mode optimizer model, which enhances the accuracy of power forecasting and system response by training on historical energy and weather patterns.
  • The experimental outcomes confirmed peak PV generation of 17 kW at noon with inverter output of 16 kW, and voltage across microgrids (MG1: 0.94-1.06 V, MG2: 0.90-1.025 V) within the stable range.
  • The battery state of charge varied between 180 and 200%, providing stable energy supply and load balancing.
  • The research concluded that the proposed scheme fills core energy forecasting and grid interfacing gaps and provides a scalable solution for future smart grid and EV-integrated renewable systems.

Statistics:

  • Peak PV generation of 17 kW
  • Inverter output of 16 kW
  • Voltage across microgrids (MG1: 0.94-1.06 V, MG2: 0.90-1.025 V)
  • Battery state of charge: 180-200%
  • Accuracy of power forecasting: enhanced by training on historical energy and weather patterns

Sources:

  • Hybridized Energy Management and Power Forecasting In Grid-tied Solar Photovoltaic and Wind Turbine With Electric Vehicles (Energy Technology, 2025)
  • School of Computer Science, Shandong Xiehe Univ, Jinan, People's Republic of China
  • Muhammad Shafiq, research author
  • Additional authors: Prasad Yadav Kurikyala, Sivakumar Selvaraj, Kutikuppala Durga Syam Prasad, Saravanan Siddhan, and Jayarama Pradeep
  • NewsRx LLC, publisher of Energy Weekly News.