International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 01 | Jan 2025
p-ISSN: 2395-0072
www.irjet.net
ENHANCING EQUAL PENETRATION OF RENEWABLE ENERGY INTO MULTI-GRID AND UNIT COMMITMENT CHALLENGES IN PHOTOVOLTAIC (PV) AND WIND WITH 118 BUS SYSTEM BY OPTIMIZATION TECHNIQUES 1 Assistant Professor, Dept. of Electrical & Electronics Engineering, PDA College of Engineering
Kalaburagi, Karnataka, India.
2 Assistant Professor, Dept. of Electrical & Electronics Engineering, PDA College of Engineering
Kalaburagi, Karnataka, India.
Akshay Aspalli1, Sangmesh Sakri2 ---------------------------------------------------------------------***--------------------------------------------------------------------significantly enhanced, ultimately contributing to more Abstract: This research focuses on improving the sustainable and robust energy solutions.
integration of renewable energy sources, specifically photovoltaic (PV) and wind energy, into multi-grid systems. The study aims to address the challenges of achieving consistent penetration levels for these energy sources while managing their variability and intermittency alongside fluctuating demand across interconnected grids. To achieve this, advanced optimization techniques are essential. The Risk-Adjusted Unit Commitment (RAUC) framework has been developed for power systems incorporating both solar and wind energy, applied within a 118-bus network. The primary objective of this framework is to ensure balanced and reliable grid operation while overcoming challenges posed by renewable energy variability.
Key Words: Renewable Energy, Multi-Grid, Photovoltaic (PV), 118 Bus System, and Risk-Adjusted Unit Commitment.
1. INTRODUCTION The growing global demand for energy, combined with increased awareness of environmental challenges, has driven the transition toward renewable energy sources. Among these, photovoltaic (PV) and wind energy stand out as vital contributors to reducing dependency on fossil fuels and mitigating the effects of climate change. However, their integration into existing power grids presents unique and complex challenges, requiring the development of innovative strategies and optimization techniques.
The RAUC framework begins with extensive data collection, with solar power generation modeled using Mixed Integer Quadratic Programming (MIQP) and wind power generation modeled through the AutoRegressive with Exogenous Inputs (ARO) methodology. To increase renewable energy penetration in multi-grid systems, the framework incorporates High-Penetration Renewable Integration (HPRI) strategies along with the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm. The Stochastic SecurityConstrained Unit Commitment (SCUC) method is also used, along with coordination of Battery Energy Storage Systems (BESS) in multi-area grids.
The variability and intermittency of renewable energy sources like PV and wind, which depend on factors such as weather conditions and daylight, add significant complexity to grid operations. In multi-grid systems, the objective is to interconnect regional and local grids to create an efficient, cohesive, and reliable energy network. This integration demands advanced solutions to handle fluctuating power output and align it with dynamic energy demands across different regions. Achieving this balance is critical for the stable operation of interconnected grids. Optimization techniques have emerged as indispensable tools in managing renewable energy integration. These techniques leverage mathematical models, sophisticated algorithms, and computational tools to optimize energy generation, transmission, and distribution in real time.
MATLAB is employed to develop algorithms optimizing the scheduling of BESS charging and discharging cycles based on variables such as state of charge (SOC) and time intervals. Simulation results show that after 7.9 hours of operation, the battery's SOC reaches 40.5%, indicating that the battery has discharged to 40.5% of its full capacity. Looking forward, there is considerable potential for further refining optimization algorithms for renewable energy management. By incorporating machine learning, artificial intelligence, and advanced modeling techniques, the precision and efficiency of these systems could be
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The transition toward renewable energy sources offers opportunities to enhance energy sustainability and resilience but also introduces new challenges. Ensuring equitable penetration of PV and wind energy across
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