The rapidly increase in electrical energy demand from residential, commercial and industrial sectors is one of the major challenge in power system, especially in the current period of high oil prices, steadily reducing energy sources and increased concerns about environmental pollution. Renewable energy is considered as one of the solution to this increase in power demand. The conventional method of power system cannot meet the power demand for many reasons such as environmental effects, location of the consumer, price of fuel and others. This paper presents the design of an off-grid Hybrid Renewable Energy System (HRES) for electrification of a typical remote area. The designed hybrid system consists of three different configurations of PV/Battery, Wind/Battery and PV/Wind/Battery systems. The system components are modelled and the objective function is designed as a function of total annualized cost of the system subject to some constraints binding the decision variables. The total annual cost is formulated as a function of annual capital cost and annual maintenance cost of the system subject to some operational constraints. In order to determine the optimal number of the decision variables that would satisfy the load demand in the most cost effect manner, Optimal Foraging Optimization (OFA) algorithm was used. Finally, a simulation experiment shows that the total annual cost obtained by each algorithm for the PV/Battery system is $9,340.42 or N3,876,274.30, $9,446.77 or N3,920,409.55 and $10,076.34 or N4,181,681.1 for OFA, GA and PSO respectively. For the Wind/Battery configuration, the total annual cost obtained by OFA, GA and PSO are $17,508.20 or N7,265,903, $12,493.27 or N 5,184,707.05 and $16,535.93 or N6,862,410.95 respectively. Similarly, the PV/Wind/Battery configuration showed that the OFA, GA and PSO obtained an annualized cost of $15,926.07 or N6,609,319.05, $18,167.09 or N7,539,342.35 and $16,535.93 or N6,862,410,95 respectively. From the results obtained by OFA are compared with that of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. Results showed that all the algorithm can efficiently size the hybrid system with OFA obtaining the most economical design. Therefore, for economically and efficiently electrification of a remote area in Abuja using an off-grid hybrid renewable energy system, GA optimization algorithm is recommended for wind/Battery system and OFA optimization algorithm is recommended for PV/Wind/Battery system.
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