Role of energy management in hybrid renewable energy systems: case study-based analysis considering varying seasonal conditions

The recent popularity of alternative energy technologies is mainly promoted by the increasing awareness of environmental concerns as well as the economic impacts of the depleting fossil fuel reserves. Among several alternative technologies, wind- and solar-based energy have been given specific importance with government-based support for providing a cost-effective structure to realize better penetration of such environmentally friendly sources in the energy market. Even these sources are advantageous over the conventional means of energy production from many aspects, a main drawback being the total dependence on the meteorological conditions (wind speed, solar radiation, temperature, etc.) of the wind and solar systems, as they are not fully reliable to satisfy a particular load demand variation at each instant. Thus, some form of backup is always required that will shift the use of the energy from the moments of renewable-based nondispatchable production to the load demand-based dispatchable production. In this study, to ensure the supply of the load in all of the cases, an electrolyzer-fuel cell-based `hydrogen regenerative' system is applied as main backup, together with a small-sized battery group to pick up transients. Thus, a hybrid structure including wind, solar, and hydrogen energy technologies is provided. The artificial neural network controller approach is selected for the hybrid system's energy management and its performance is examined and evaluated during different case studies that reflect the variations of the meteorological conditions in different seasons. It is aimed with this study to provide constructive suggestions to upcoming researchers interested in the energy management issue in hybrid systems.

Anahtar Kelimeler:

Renewable energy, neural networks, hydrogen energy, solar energy, wind energy

Role of energy management in hybrid renewable energy systems: case study-based analysis considering varying seasonal conditions

Keywords:

Renewable energy, neural networks, hydrogen energy, solar energy, wind energy,

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