An Ant Colony Algorithm for HRES Size and Configuration Optimisation

An Ant colony algorithm (ACO) is proposed for hybrid renewable energy system size and configuration optimisation using continuous search space approach. In the proposed algorithm the pheromone distribution across search space is determined by gaussian distribution, and the probabilistic path selection is performed based on pheromone deposit value via roulette wheel principle. The ACO algorithm is implemented in the software tool MOHRES and three case studies are conducted to optimise the size and configuration of standalone hybrid renewable energy system derived from the full configuration wind-PV-battery-diesel-FC Electrolyser system. To evaluate the performance of the proposed ACO, the optimum solutions are compared with the solutions obtained by the genetic algorithm (GA) optimisation algorithm implemented in MOHRES.