Smart Grid Cost Optimization: Comparing Bellman and Genetic Algorithms

Classical and heuristic algorithms are developed for solving shortest path problems and are widely used in cost optimization and bill reduction in Smart Micro Grids. This paper aims to compare two optimization algorithms with a multi-objective cost function: i) reduce the daily energy bill and ii) optimize the CO2 emissions in the smart grid SG. In this study, from the classical algorithms, the Bellman-Ford algorithm is used and from heuristic algorithms, Genetic algorithms are chosen. The complexities of the algorithms were investigated and a comparison of the results was made. For both algorithms, MATLAB environment is used to simulate the proposed Energy Management System for the grid-connected. These results will be presented with graphs.