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Optimization Chapter | 9 265

Optimization
Finding the best configuration of a renewable energy system is the objective
of the optimization step. HOMER simulates the performance of several con-
figurations. It selects those that meet the constraints (demand, and minimum
percentage of renewable energy contribution in a hybrid system). The best
configuration is a solution/configuration that has minimum net present cost.
Sample independent (decision) variables that can be changed by HOMER
include the number and type/size of wind turbines, the size of the storage system
(number of batteries), and the size of diesel generators. A search space that
contains all possible scenarios for a component (e.g. various battery sizes) is
provided by the user.

Sensitivity Analysis
An optimized system is calculated based on certain assumptions about a number
of uncertain parameters; examples are fuel price, grid power price, discount
rate, life time of the project, and demand. By performing sensitivity analysis
on these variables, a user can deal with uncertainty and understand how/if
the optimized system changes with these parameters. For instance, what is the
minimum lifetime for a project? What interest rates are feasible for a renewable
energy system?
HOMER has been applied in several studies to find the optimum energy
system. For instance, in a case study in a remote village in India, four energy
sources (hydropower, solar, wind, and bio-diesel generators) were combined
[37]. The optimal off-grid system was identified and compared with the
alternative of grid extension. This study showed that the hybrid off-grid system
is cost-effective, compared with grid extension.
Some studies have optimized energy systems at much larger scales. As
an example, Budischak et al. [38] considered a large grid in the eastern
United States (Fig. 9.21) with a capacity of 72 GW (PJM Interconnection;
www.pjm.com). By combining several renewable energy resources distributed
across the region (onshore wind, offshore wind, and solar), and storage systems
(batteries and fuel cells), an alternative system based on renewable energy was
simulated and optimized. This study proposed an energy system that can meet
the electricity demand of this large region based on 90% to 99.99% renewable
sources, and with a cost comparable to conventional energy systems by 2030.
Notably, Budischak et al. [38] recommended excessive energy production as the
least cost option; the renewable system should produce almost three times the
electricity demand, on average, to compensate for intermittency, and to avoid
high costs associated with energy storage. Offshore wind energy had nearly the
same contribution as onshore wind in the energy mix for the 99.99% renewable
case in 2030. Both HOMER and another tool RREEOM (Regional Renewable
Electricity Economic Optimization Model) were used, whilst PREEOM was
recommended for larger grids like PJM.