Small autonomous power systems (SAPS) that include renewable energy sources are a promising option for isolated power generation at remote locations. The optimal sizing problem of SAPS is a challenging combinatorial optimization problem, and its solution may prove a very time-consuming process. This paper initially investigates the performance of two popular metaheuristic methods, namely, simulated annealing (SA) and tabu search (TS), for the solution of SAPS optimal sizing problem. Moreover, this paper proposes a hybrid SA-TS method that combines the advantages of each one of the above-mentioned metaheuristic methods. The proposed method has been successfully applied to design an SAPS in Chania region, Greece. In the study, the objective function is the minimization of SAPS cost of energy (€/kWh), and the design variables are: 1) wind turbines size, 2) photovoltaics size, 3) diesel generator size, 4) biodiesel generator size, 5) fuel cells size, 6) batteries size, 7) converter size, and 8) dispatch strategy. The performance of the proposed hybrid optimization methodology is studied for a large number of alternative scenarios via sensitivity analysis, and the conclusion is that the proposed hybrid SA-TS improves the obtained solutions, in terms of quality and convergence, compared to the solutions provided by individual SA or individual TS methods.

The overall evaluation of small autonomous hybrid power systems (SAHPS) that contain renewable and conventional power sources depends on economic and environmental criteria, which are often conflicting objectives. The solution of this problem belongs to the field of non-linear combinatorial multiobjective optimisation. In a multiobjective optimisation problem, the target is not to find an optimal solution, but a set of non-dominated solutions called Pareto-set. The present article considers as an economic objective the minimisation of system's cost of energy (COE), whereas the environmental objective is the minimisation of the total greenhouse gas (GHG) emissions of the system during its lifetime. The main novelty of this article is that the calculation of GHG emissions is based on life cycle analysis (LCA) of each system's component. In LCA, the whole life cycle emissions of a component are taken into account, from raw materials extraction to final disposal/recycling. This article adopts the non-dominated sorting genetic algorithm (NSGA-II), which in combination with a proposed local search procedure effectively solves the multiobjective optimisation problem of SAHPS. Two main categories of SAHPS are examined with different energy storage: lead-acid batteries and hydrogen storage. The results indicate the superiority of batteries under both economic and environmental criteria.