Economical and environmental operation of smart networked microgrids under uncertainties using NSGA-II

The future smart grid is expected to be an interconnected network of small-scale and self-contained micro-grids (MGs), in which renewable energy sources (RESs) play significant role in generation level as well as attract special attention to the aim at a friendly environmental society. In this paper, optimal operation of distributed generations (DGs) are analyzed probabilistically due to uncertainties of loads and RESs. In addition, probability distribution function (PDF) is used to describe the fluctuation model of input data. This paper establishes smart networked Microgrids (MGs) based on NSGA-II algorithm, including the lowest operating cost and the least pollutants emission. In order to make a comparison, the problem is converted to a single-objective function and then, solved by two heuristic algorithms, namely particle swarm optimization (PSO) and Imperialist competitive algorithm (ICA). Simulation results support the capability of the proposed algorithm to minimize jointly the operating power and pollution emission as compared to the results obtained by using current heuristics.