Probabilistic Management of Renewable Energy Resources in Presence of Uncertainties using Water Cycle and Grey Wolf Optimization Algorithms

Abstract

In recent years, the apprehensions about ecological pollution derived from the use of fossil fuels for electricity generation, as well as the concerns about using up all the reserved resources have been increased. On the other hand, the growth of electricity consumption has been noticeable in domestic and manufacturing parts. Therefore, employing renewable energy resources such as wind and solar can be an adequate substitution. Stochastic nature accompanied by the uncertainty existent in these renewable resources can be accounted as the issues which make planning and forecasting complex. In this study, the micro-grid optimal planning in presence of renewable energy resources is evaluated employing water cycle and grey wolf optimization algorithms. Actually, to control the load variations, in addition to the considered renewable resources, a diesel generator is employed. Battery energy storage is also considered in the micro-grid for charging and discharging operations in the demanded time. Besides, to model wind and solar renewable resources uncertainty, stochastic programming is used and probabilistic scenarios are defined, as well. Solar power intervals are anticipated to be between the hour 6th to the hour 19th. Additionally, utility grid is able to sell sufficient amount of power to the main grid so as to meet the load demand at hours 8th to 12th and also 19th to 22th. Lastly, the micro-grid operation cost gained via the proposed methods are compared against particle swarm optimization (PSO) and mixed-integer linear programming (MILP). The simulation results represent that, through application of the presented techniques, the operational cost of the system is lesser than PSO and MILP, which verifies the effectiveness of the methods.