Page 260 - Hybrid-Renewable Energy Systems in Microgrids

Solar–wind hybrid renewable energy system 237

Figure 12.6 Plot of Number of PV modules versus Number of wind turbines for a given
LPSP. LPSP, loss of power supply probability.

or control that yields the lowest total cost through the useful life of the installation.
Though, the environmental issues related to this type of installations should also be
considered during the design process.
In most of the design processes, the pollutant emissions have been calculated
optimally designing the system with minimum costs. However, in some cases like
HOMER program, it is possible to consider the pollutant emissions by evaluating
them economically and hence considering them as a part of the costs objective func-
tion. This mapping of costs to emissions conclusively influences the results of the
design. The method that HOMER uses for the multi-objective design is known as the
method of the weights [11]. Multi-Objective Evolutionary Algorithms (MOEAs) is a
popular multi-objective design task, it has been applied in numerous papers. In their
work, Pelet et al. applied MOEAs for the optimization of system cost and CO 2 emis-
sions for a standalone HRES in which three hotels and a town in the Tunisian Sahara
were thermally and electrically supplied [97].
Researcher Bernal-Agustín et al. [26] present a multi-objective optimization con-
sidering NPC versus CO 2 emissions for hybrid a solar–wind–diesel system with
battery storage using MOEAs. In their work, Dufo-López and Bernal-Agustín [98]
demonstrated a triple multi-objective optimization to minimise concurrently the total
cost throughout the useful life of the installation, pollutant emissions (CO 2 ) and unmet
load. This work uses MOEAs and GA together to find the optimum combination of
components and control strategies for the HRES.