In the quest for sustainable energy solutions, the transition toward renewable energy sources has become a core point of research and policy initiatives. This chapter addresses the problem of cost optimization in the generation and transmission of energy from virtual power plants to demand aggregators. By introducing various generation technologies, including variable renewable sources such as wind and solar PV plants, we explore the effects of inherent uncertainties of production as well as of the transportation cost variability to the energy mix and cost of operation. An extension of the optimal transportation (OT) problem is developed to model mid-term energy planning over a network, representing the projection of the power system to the business layer. The linear programming (LP) problem is solved for multiple scenarios under a Monte Carlo simulation framework, depicting the predefined renewable energy production and the renewable energy cost variability, due to the incomplete information that is available when decisions are made. Finally, we describe the basic statistics of the solutions viewed as random variables aiming to demonstrate how this uncertainty is inherited in the problem’s solution and how renewable energy sources and their uncertainty in production and costs can be incorporated into the problem.