The dataset research paper presented a detailed collection of simulation results generated using HOMER software for analyzing grid-connected hybrid renewable systems. It focused on a smart grid setup combining solar PV, wind turbines, and the main grid through a bidirectional converter. The base model supplied a daily load of 3950 kWh with a peak of 479.38 kW. Two random variability scenarios were designed — 5% (low) and 20% (high) for both day-to-day and timestep fluctuations — to study how renewable intermittency affected grid stability and optimization. A total of 4000 simulations were performed, recording parameters such as power generation, grid exchange, efficiency, renewable penetration, and cost. The dataset served as a benchmark for testing machine learning models and nature-inspired optimization algorithms like PSO, GWO, and Adaptive GA. It helped researchers explore grid resilience and develop adaptive control and forecasting strategies for improved smart grid performance.
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