Active Power Losses Constrained Optimization in Smart Grids by Genetic Algorithms

Abstract

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 ACEA Distribuzione S.p.A. network which presents backflow of active power for 20% of the annual operative time. The network taken into consideration includes about 100 nodes, 25 km of MV lines, three feeders and three distributed energy sources (two biogas generators and one 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 it is adopted a genetic algorithm, defining a suited fitness function. Tests have been performed by feeding the simulation environment with real data concerning dissipated and generated active and reactive power values. First results are encouraging and show that the proposed optimization technique can be adopted as the core of a hierarchical Smart Grid control system. © Springer-Verlag Berlin Heidelberg 2013

On the impact of topological properties of smart grids in power losses optimization problems

“…In this paper we have presented an improvement of the control system first described in [28,[34][35][36]. We performed an analysis of admissible network configurations to identify undesirable configurations that may slow down the convergence speed of the optimization procedure.…”

“…In our previous works [28,34,36], we faced both the problems of PFC and DFR over a portion of the ACEA electrical grid (ACEA is the company managing the entire distribution grid of Rome, Italy), using genetic algorithms as optimization strategy. In this paper we elaborate over such studies by first analyzing how undesirable configurations affect the optimization process in terms of both quality of the optimization result and running time of the optimization procedure.…”

An Ordering Procedure for Admissible Network Configurations to Regularize DFR Optimization Problems in Smart Grids

“…In this paper an improvement of the control system described in (Storti et al, 2013a), (Possemato et al, 2013), (Storti et al, 2013b) and (Storti et al, 2014) is presented. We propose an heuristic method to compare admissible network topologies and a criteria to order the list of such topologies aiming to improve the continuity of the objective function to the variation of the configuration parameter.…”

“…In this paper it is performed the minimization of F(k) in A. Further details of the optimization procedure can be found in (Storti et al, 2013a).…”

“…Probably, the first attempt to use genetic algorithms (GAs) to solve the DFR problem with minimal losses can be found in (Nara et al, 1992). Since then, a great number of publications based on evolutionary algorithms are proposed in the literature (Storti et al, 2013a), (Possemato et al, 2013). Recently, GA is also used to solve the DFR problem with DG (Chandramohan et al, 2010), (Storti et al, 2013b).…”