09-05-2012, 02:05 PM
Optimal Placement and Sizing of Distributed Generator Units using Genetic Optimization Algorithms
energy_islands.pdf (Size: 649.98 KB / Downloads: 70)
Introduction
Decentralized generation will become more and more important in the future electricity dis-
tribution system. This tendency is increased by the commercial availability of small-scale
production units (e.g. fuel cells, micro-CHPs, photovoltaic panels) and the liberalization of
the energy market, putting more pressure on the system. Also the support for sustainable
development using renewable energy sources plays a key role.
General Framework
In this section the general configuration of the problem is addressed, describing the grid
topology, power production and load profiles for the different scenarios.
Grid Topology and Power Production
For this analysis the topology of an existing grid at medium-voltage is used. It consists of
3 lines and 20 nodes, with one aggregated load at each node, as shown in Figure 1. The
distributed energy sources used are PV panels and CHP units. Real measurements are used
to provide data for the illumination on PV panels and the CHP production which is based
on heat demand. The profiles of active power consumption are based on measurements taken
in a residential building for a period of one year on a 15 minute basis. Figure 2 shows the
production profiles for each generator type.
Problem Definition
The objective of this analysis is to find the optimal placement and size of CHP and PV
generation units, in order to minimize the power loss along the grid lines over a period of
24 hours. In order to assess the effect of Winter and Summer variations on different load
situations, the period of 24 hours over which the minimization takes place is defined in the
following scenarios:
Genetic Algorithm Implementation
A Genetic Algorithm (GA) is a search algorithm that is based on the hypothesis of natural
selection [6]. The GA is an evolutionary population-based search process that begins with a
very large set of initial candidate solutions. These solutions are subjected to selection pressure
based on relative fitness and other genetic operators that serve to advance in the search.