Optimal Distributed Generation Allocation Using Quantum Inspired Particle Swarm Optimization

Abstract

Distributed Generation (DG), with respect to its ability in utilizing the alternative resources of energy, provides a promising future for power generation in electrical networks. Distributed generators contribution to the power systems includes improvement in energy efficiency and power quality to reliability and security. These benefits are only achievable with optimal allocation of distributed resources that considers the objective function, constraints, and employs a suitable optimization algorithm. In this chapter, a quantum inspired computational intelligence is exercised for the optimal allocation of distributed generators. The fact that most of power system optimization problems, when modelled accurately, are of non-convex and sometimes discrete nature has encouraged many researchers to develop optimization techniques to overcome such difficulties. The basic Particle Swarm Optimization (PSO) is one of the most favored optimization techniques with many attractive features. Early experiments of employing PSO in many applications in power systems have indicated its promising potential. Consequently, the more advanced alternatives of this algorithm such as Quantum behaved PSO (Q-PSO) may show the same or even better performance in power system problems. The aforementioned algorithm has already been employed for different optimization objectives in power systems such as: short-term non-convex economic scheduling, unit commitment problems, loss of power minimization, economic load dispatch, smart building energy management and power system operations. Nevertheless, the algorithm has never been used for optimal allocation of distributed generation units. In this chapter the above problem will be solved with a quantum behaved particle swarm optimization algorithm. The chapter will be started with an introduction to the optimal allocation of DG then the power system, including the DG units will be modeled. On the next step the Q-PSO will be adopted for the optimal allocation. Finally, the results and discussions will be presented.