Farklı ihtiyaçlar için batarya boyutlandırma optimizasyon algoritmalarının modellenmesi

Battery energy storage systems (BESS) increase energy controllability and grid flexibility. One of the most important issues in BESS investments is optimal BESS sizing for various needs. In this thesis, it is aimed to develop an optimal battery sizing methodology for the consumer, producer and prosumer. By using Mixed-Integer Linear Programming and Mixed-Integer Quadratic Programming methods, optimal battery sizing algorithms that can be used by all end-user types for different purposes were developed. The advantage of this mathematical modeling is that it can be adapted for different scenario constraints with minor modifications. Various estimation algorithms were used to get more realistic results from the optimization algorithms for the future. Artificial neural network (ANN), deep neural network (DNN), and Long-Short Term Memory models were used to predict generation, consumption, and electricity market data. The importance of estimation algorithms in the smart grid ecosystem was emphasized and it was aimed to predict the needs for the future. Prediction methods and optimization algorithms were developed in the Python environment. Pandas, numpy, sklearn, keras, cvxpy libraries were actively used. It is hoped that it will be beneficial for the investments to be made within the scope of the smart grid concept.