Optimal configuration of power distribution networks with variable renewable energy resources

An attractive way to reduce losses in electric power distribution networks is addressing the network reconfiguration problem, which should give a topology for the primary distribution network that minimizes the total losses due to the electrical resistances in the lines and complementary equipment's (technical losses). Distributed energy resources and additional innovations associated to smart grids allow enhancing the benefits of finding better network topologies. On the other hand, the integration of renewable energy sources with variable random outputs requires expanding the perspective in modeling the network reconfiguration problem and in the shaping of appropriate solution techniques. These issues are the object of this paper. The main new features of the problem are explored with a maquette designed to highlight the consequences of random generation sources in the networks. Following, the paper proposes a formulation for the problem that explicitly considers random energy sources. A state of the art genetic algorithm build under the biased random-key evolution framework (BRKGA) is developed to address this hard combinatorial optimization problem. Case studies with benchmark networks put into perspective the proposed methodology. Results show that random energy values should be explicitly modeled in contemporary approaches to the network reconfiguration problem. The paper provides the grounds for addressing this new problem and points to additional research paths in the area.