Adaptive Robust Energy Management of Smart Grid with Renewable Integrated Energy System, Fuel Cell and Electric Vehicles Stations and Renewable Distributed Generation

This study expresses energy scheduling in intelligent distribution grid with renewable resources, charging stations and hydrogen stations for electric vehicles, and integrated energy systems. In deterministic model, objective function minimizes total operating, energy losses and environmental costs of grid. Constraints are power flow equations, network operating and voltage security limits, operating model of renewable resources, electric vehicle stations, and integrated energy systems. Scheme includes uncertainties in load, renewable resources, charging and hydrogen stations, and energy prices. Robust optimization uses to obtain an operation that is robust against the forecast error of the aforementioned uncertainties. Modeling electric vehicles station and aforementioned integrated energy systems, considering economic, operational, and environmental objectives of network operator as objective function, extracting a robust model of aforementioned uncertainties in order to extract a solution that is robust against the uncertainty prediction error, and examining ability of energy management to improve voltage security of grid are among innovations of this paper. Numerical results obtained from various cases prove the aforementioned advantages and innovations. Energy management of resources, charging and hydrogen stations, and aforementioned integrated systems lead to scheme being robust against 35% of the prediction error of various uncertainties. In these conditions, scheme has improved economic, operational, environmental, and voltage security conditions by about 33.6%, 7%- 37.4%, 44.4%, and 24.7%, respectively, compared to load flow studies. By applying optimal penalty price for energy losses and pollution, pollution and energy losses in the network are reduced by about 45.15% and 34.1%, respectively.