Modelling of efficient distributed generation porfolios using a multiobjective optimization approach

In course of the German power system transition to a higher share of renewable energy sources decentralized activities constitute a major driving force for the growth of renewable energy capacity. In this context plural activities and initiatives on the local and regional level are followed to develop concepts for an efficient and sustainable regional energy supply. To achieve these goals various objectives has to be simultaneously accomplished. Generally, these objectives contradict to each other and cannot be handled by a single optimization technique. This paper proposes a multiobjective (MO) optimization approach for identifying efficient DG generation portfolios regarding multiple objectives. The methodology presented allows the planner to decide the best trade-off between the self-supply degree, environmental impact and electricity generation cost. The proposal applies, in a study case, a MO genetic algorithm that allows identifying a set of non-inferior Pareto-optimal solutions.