Role of Energy Management in Hybrid Renewable Energy Systems: Case Study Based Analysis Considering Varying Seasonal Conditions

Abstract

Abstract: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.

Optimal coordinate operation control for wind–photovoltaic–battery storage power-generation units

“…6. To demonstrate the superiority of the proposed algorithm, a comparative analysis of the optimal results among EGSA, the Artificial Neural network (ANN) approach [17] and Particle Swarm Optimization (PSO) [26] were applied. The comparison results are listed in Table 6.…”

“…To ensure load supply in all cases, Yumurtaci et al selected the artificial neural network controller to examine and evaluate the performance of the hybrid renewable energy system [17]. To achieve fast charging, energy saving, power source protection, and system stability assurance, Wai et al designed an intelligent optimal energy management system for hybrid power sources, and the fuzzy control method was adopted to manipulate the system stably [18].…”

An Improved Optimization Function for Maximizing User Comfort with Minimum Energy Consumption in Smart Homes

“…However, these renewable energy sources cannot fulfill the total energy demand. A hybrid energy management system based on an artificial neural network controller is presented in [25] for automated switching from renewable energy sources to conventional energy production depending on the energy demands.…”

Utilization of energy from waste plants for microgrids

“…EfW systems should be accounted as distributed renewable energy generators for smart grids: It's generation potential depends on only MSW mass of cities and hence EfW presents advantageous of more deferrable and predictable generation pattern compared to generation profiles of solar or wind energies. Because solar and wind generation strongly depends on the local meteorological conditions [10], [11]. As a consequence, EfWI plants as distributed generator offers capability of generation suspension and controllability for future smart grids.…”