Hybrid Energy System (HES)s areincreasingly becoming popular for standalone
electrification due to global concerns on GHG emissions and higher depletion of fossil fuel
resources. Simultaneously research work on optimal design of HESs has also made much
progess based on progress with numerous optimization techniques while giving special
focus to Pareto optimization considering conflicting objectives.
This study comes up with a novel evolutionary algorithm to optimize HESs based on ε-
dominance technique. Mathematical modeling of energy flows, cash flows, GHG emissions
were carried out in order to support the optimization. Pareto analysis was conducted for two
different cases where former analyzes a novel design of a HES and latter analyzes a
conversion of existing Internal Combustion Generator (ICG) into a HES in the expansion
process. The Levelized Energy Cost (LEC), annual fuel consumption and Initial Capital
Cost (ICC) were considered to be objective functions in the first analysis. A sensitivity
analysis was followed the mathematical optimization in order to evaluate the impact of
power supply reliability on the Pareto front. Furthermore, sensitivity of fuel cost and
renewable energy component cost on Pareto front was also investigated considering the
present dynamic condition of energy market. LEC, power supply reliability and added
renewable energy capacity were taken as objectives to be optimized in the second case.
Sensitivity of ICG capacity on the Pareto front was also taken into discussion. Pareto
analysis clearly elements such as LEC, power supply reliability and fuel consumption are
conflicting to each other. Therefore it is essential to perform multi criterion analysis in order
to assist decision making.
In order to assist decision making, Fuzzy-TOPSIS (a multi criterion decision making
technique) was combined with Pareto optimization. For that, multi objective optimization
was carried out considering Levelized Energy Cost (LEC), unmet load fraction, Wasted
Renewable Energy (WRE) and fuel consumption as elements in the objective functions to
generate non-dominant set of alternative solutions. Pareto front obtained from the
optimization was ranked using Fuzzy-TOPSIS technique and Level Diagrams were used to
support this proces