A Comparative Analysis of Optimization Techniques Used in the Sizing of a Residential Nanogrid

Economic use of resources and technologies is mainly achieved using optimization frameworks. Optimizations for renewable energy system planning and operations are mainly implemented using either or combination of classical, intelligent-based, or off-the-shelf software techniques (tools). Each of the homogenous technique implementations exhibits a form or more of its opportunities and limitations. Classical techniques are opaque and loaded with computational burdens, however, they tend to be fast in achieving global solutions without branching at local solutions. The intelligent-based techniques such as metaheuristics are simple and transparent, however, the results they produce are not always optimal, even though acceptable. The software tools are inflexible for customized applications, but the results they produce are always optimal and fast implementations. This paper considers the constraints of Nigerian semiarid weather data, renewable energy components and residential demands to configure a nanogrid using Nested Integer Linear Programming, Particle Swarm Optimization and Hybrid Optimization of Multiple Energy Resources optimization software as samples of the foregoing classification of optimization tools. The objective is to compare the performance of each of the tools’ homogeneity, to derive their benefits and drawbacks. Results show that Hybrid Optimization of Multiple Energy Resources has the highest configurational capacity of 46.5 kW with the lowest Levelized Cost of Energy of 0.2220 $/kWh. The Nested Integer Linear Programming having the lowest capacity of 21.26 kW derived 0.3375 $/kWh as Levelized Cost of Energy, while Particle Swarm Optimization with an average capacity of 27.05 kW obtained the Levelized Cost of Energy of 0.4777 $/kWh. This implies that higher investment derives lower energy costs. Hence, future use of the techniques’ homogeneity or otherwise may be subject to achieving balances between the benefits and drawbacks exhibited by the techniques and underlying renewable energy-based objectives.