Multi-objective dynamic optimization of hybrid renewable energy systems

Renewable energy resources are often suffered from the challenges such as non-uniform power generation as a result of weather and seasonal variations. Hybrid renewable energy systems (HRES) is a solution for efficient energy intensification of renewable resources in which several of them are combined to overcome the challenges arising in operating these in a stand-alone mode. This study proposes a multi-objective dynamic optimization of a candidate HRES by using a genetic algorithm in which the operating cost of HRES, use of nonrenewable power, and fuel emission are minimized simultaneously over a finite time length, subject to operational constraints. In optimization, three strategies are evaluated by considering the wind, solar, and load profiles for 24 h ahead (strategy 1), past and 1 h ahead (strategy 2), and 1 h ahead (strategy 3). Comparison of results illustrates that the power needs to be bought from the grid for strategy 1 is lower by 8.7% and 10.7% compared to strategy 2 and strategy 3 and also the power sold to the grid is 19% and 22% higher than strategy 2 and strategy 3, respectively while meeting the given load profile of 100 households.