Two evolutionary computational approaches for active power losses minimization in Smart Grids

In this paper the problem of the minimization of active power losses in a real Smart Grid located in the area of Rome is faced by defining and solving a suited multi-objective optimization problem. It is considered a portion of the Italian electric distribution network managed by the ACEA Distribuzione S.p.A. which presents backflow of active power for 20% of the annual operative time. The network taken into consideration includes about 1200 user loads, 70 km of MV lines, 6 feeders, a thyristor voltage regulator (TVR) and 6 distributed energy sources (5 generator sets and 1 photovoltaic plant). The grid has been accurately modeled and simulated in the phasor domain by Matlab/Simulink, relying on the SimPowerSystems ToolBox, following a Multi-Level Hierarchical and Modular approach. It is faced the problem of finding the optimal network parameters that minimize the total active power losses in the network, without violating operative constraints on voltages and currents. To this aim, after defining a suitable fitness function, two evolutionary computation paradigms are compared: genetic algorithms and particle swarm optimization. Tests have been performed by feeding the simulation environment with real data concerning dissipated and generated active and reactive power values. Results show that both optimization techniques can be adopted as the core of a hierarchical Smart Grid control system.