The Multi-objective Optimization Or Distribution Generation(DG)Based On Quantum Particle Swarms Optimization(QPSO)

It is widely accepted that renewable energy sources are the key to a sustainable energy supply infrastructure, since they are both inexhaustible and nonpolluting. A number of renewable energy technologies are now commercially available, the most notable being wind power, photovoltaic, micro turbine, etc. However, intermittent and renewable distributed generation (DG) has the feature of dispersion and randomness, which means its large access to distribution network, will affect the whole system's safety and stability, such as power loss, voltage stability power quality and power supply reliability.Based on analysis of the characteristics and basic theory of distributed power components, this paper has established a probabilistic generation-load model, which combines all possible operating conditions of the renewable DG units with their probabilities, hence accommodating this model in a deterministic planning problem. Taking the affect of DG on network into account, a multi-objective model is proposed in this paper. The model contains DG construction investment and operation fee, network loss, reliability, as well as environmental factor. Take a certain district as an example, we have applied Quantum Particle Swarms Optimization (QPSO) on DG optimization and drawn a detailed analysis on the result. The result proves that QPSO has advantages of speedy searching for the optimum and keeping the population diversity. Compared to Particle Swarms Optimization (PSO), QSPO shows high efficiency and robustness.