Multi-figure of merit optimization for global scale sustainable power systems

Abstract

The main goal in this paper is to supply a national load by multiple renewable power connections to utility grid at different geographic locations. The national load demand of the country of Jordan is supplied in this case study by finding the optimal configurations and locations (near cities). Cities of higher potential for wind and/or photovoltaic (PV) energy location are selected for connecting to renewable power. The annual system cost of energy (ASCE) for this optimized renewable energy system is shown to be 32.57% less than the conventional grid energy price. Further, the Carbon Dioxide (CO2) emissions are reduced by 80.13%. These are excellent indications for the feasibility and environmental benefits of retrofitting conventional grid with renewable power in developing countries. Multi-figure of merits (MFOM) based on a Nondominated Sorting Genetic Algorithms (NSGA) optimization cases are investigated which include annual emission indicator (AEI), ASCE, levelized cost of energy (LCOE) and renewable penetration (RP). Results are either two dimensions (2D) or three dimensions (3D) Pareto frontier, where various competitive non-dominant solutions exist. A sweet spot selection (triple-S) procedure is proposed to help select the best point in the two (figure of merits) FOMs Pareto frontier to have both environmental and feasible solutions.