In recent times, we are witnessing an increasing number of solar power plants and battery storage systems, indicating a consumer inclination towards self-sufficiency. With the growing interest in self-sufficiency, questions arise about its impact on the grid. The aim of this thesis is to develop a model that allows for the simulation of self-sufficiency and its effects on the grid.
The developed self-sufficiency model is written in Python using the open-source library pandapower. The model has three major components, these being, the consumer, the solar power plant, and the battery. Simulations are conducted using real measurement data of consumer power and generated data of solar power production based on real weather data.
With the help of the model, we analyze the impact of self-sufficiency on the grid. With the parameters considered, a 23.3% reduction in grid load is achieved by connecting a solar power plant to a facility, and a 35.26% reduction in grid load is achieved by connecting both a solar power plant and an energy storage battery.
Thus, self-sufficiency represents significant potential for reducing grid load and enabling better grid performance in the long term, however, not without challenges. With a higher rates of self-sufficiency, the system operator will face challenges in grid stability and energy quality due to the variability and unpredictability of self-sufficient production. Through improvements and research in this field, we will contribute to a more sustainable and environmentally friendly energy future.
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