Comparative Study of Posteriori Decision-Making Techniques for the Optimal Sizing and Location of Distributed Generation Units

Distributed generation units based on renewable energy sources are being increasingly integrated to the power system due to the energy crisis, the recent technological developments and the environmental considerations. However, their impacts may be two-sided. Therefore, the sizing and location of distributed generation units may be formulated as a multi-objective problem comprising of several economic and technical factors. As the optimisation of the problem generates a set of solutions, decision-making techniques are vital to choose the optimal solution. This paper aims to perform a comparative study of six multi-criteria decision-making techniques (weighed sum approach, technique for order preference similar to an ideal solution, combinative distance-based assessment, multi-attributive border approximation area comparison, complex proportional assessment of alternatives, grey relational analysis). Max-min, vector and sum normalisation techniques were applied to ensure that all the values lie in the same range. They were applied to the Pareto front generated by the multi-objective optimisation of distributed generation units in the IEEE-123 Node Test Feeder system using non-dominated sorting genetic algorithm II. The performance of the decision-making techniques was evaluated based on the agreement measure. The simulations revealed that the normalisation techniques and the assigned weights have an impact on the solution produced by the MCDM. The same rankings were found for the first 50 alternatives using WSM, CODAS, MABAC or COPRAS.