Algorithms for techno-economic assessment of configuration and size of renewable energy systems

Abstract

In this chapter, a novel isolated hybrid energy system is modeled and analyzed, with an emphasis on integrating biomass gasification technology to optimize system sizing and meet the electricity demand of a small, remote area with a peak load of 410 kW. The key components of the hybrid system include photovoltaic (PV) panels, wind turbines (WT), a biomass generators (BG), electrolyzer units, hydrogen storage tanks (HT), and a fuel cell (FC) system. To achieve the lowest possible energy cost while ensuring high reliability, measured through the loss of power supply probability (LPSP) and minimal power losses, the fata morgana algorithm (FATA), recently developed and inspired by geophysical principles, was employed. This optimization algorithm aims to meet all power demands while minimizing excess power (EXP) absorbed by the dump loads. A multi-objective function was constructed to address the optimization challenge, and the performance of the FATA algorithm was benchmarked against other techniques, including the RRT-based optimizer, artificial rabbits optimization, and hippopotamus optimization. The results demonstrated that FATA delivered the most effective system design, exhibiting superior convergence behavior compared to the other methods. Specifically, FATA achieved a fitness value of 0.064574, which corresponds to a system configuration consisting of 393 solar panels, 10 WTs, 3 biomass generators, a 399 kW electrolyzer, a hydrogen tank with a capacity of 103.59 kg, and a 145 kW FC. The system successfully provided electricity at an energy cost of 0.2620228 $/kWh, resulting in a net present cost of 6,326,140.79$, with an LPSP of 0.0183 and an EXP of 0.00608.