Optimal placement of distributed generation in micro-grids with binary and integer-encoding evolutionary algorithms

This paper discuses the performance of two different Evolutionary Algorithms (EAs) in a problem of Optimal Placement of Distributed Power Generation (OPDPG) in Micro-Grids (MGs). Specifically, the problem consists of choosing the node/nodes to locate a number of different distributed generators with different technologies (such as micro wind turbines, photovoltaic panels, etc.), in such a way that the electrical power losses along a given time period (T) in the MG are minimized. We consider a situation where the network topology is already defined and where each node can have a load with different profiles allocated. The consumption profiles are real measurements of different types (residential, industrial, etc.) and will be hourly evaluated. The generations profiles are also real measurement data from different generation technologies. We consider two different encodings the EAs: first a binary-encoding approach, where each wind generator is represented by 2 bits and each solar generator by N bits, where N is the number of nodes that form the MG; and second, an integer-encoding approach, where both wind and PV generators are represented by 1 and 4 integer elements, respectively. Experiments are performed by considering three different MG topologies, with different number of nodes, in order to test the behavior of the algorithms with search spaces of increasing size. In these experimental scenarios we show how the binary approach attains better solutions than the integer-encoding approach, tough the computational time of the former is higher.