Induced by the persistent and rapid economic growth, the worldwide demand for energy services is constantly increasing. The accompanying abundant use of fossil resources, however, strongly enhances green house gas emissions boosting the progress of climate change, which stresses the urgency of mitigation policies in this field. The probably biggest challenge along the path towards a more sustainable energy supply is to find a low-carbon energy technology that simultaneously guarantees energy security. For renewable energy generation, however, especially the second goal is hard to achieve as, in contrast to fossil resources, renewable resources strongly fluctuate and are often hard to predict. Consequently, the interplay of generated surpluses and shortfalls as well as limited storage possibilities complicate proper scheduling of renewable energy generation. Another major issue for renewable energy is given by the high costs. While conventional energy forms are competitive, renewable energy technology comes along with high investment costs that strongly restrict their profitability. These high costs would decline after some time in operation as experience and know-how improve the technical processes and hence foster the productivity. However, as the basis of energy planning decisions is mostly a matter of expenses and, in many cases, the planning horizon is too short to take these learning effects into account, investments for renewable energy technologies are often postponed into the future, which strongly restricts the scope of renewable energy generation. To address this issue, this thesis deals with optimal control models that consider the energy planning decision of a small country optimizing a portfolio consisting of fossil and renewable energy to cover the country's energy demand. While fossil energy is assumed to be constantly available, renewable energy is fluctuating seasonally. To include the mentioned effect of cost reduction due to the accumulation of experience and knowledge, the concept of the learning curve is applied. To investigate the differences in the outcome depending on whether the mentioned learning effects are included or not in the decision process, three different model approaches are analyzed. In the first one the high investment costs of renewable energy capital remain unchanged over time, in the second one they are reduced by a so-called one-factor learning curve, where accumulated experience reduces costs, and in the third one a so-called two-factor learning curve is considered, where additionally R&D efforts foster the cost reduction.