A Multiobjective Evolutionary Algorithm for the Sizing and Siting of Distributed Generation

TLDR

The restructured electricity industry requires planning that simultaneously satisfies multiple, often conflicting objectives, which single‑objective techniques cannot handle. This study proposes a multiobjective formulation for siting and sizing distributed generation in existing distribution networks. The approach uses a genetic algorithm with an ε‑constrained method to balance network upgrade, loss, energy‑not‑supplied, and customer‑required energy costs, yielding a set of non‑inferior solutions. Case studies show the method effectively identifies optimal DG configurations.

Abstract

In the restructured electricity industry, the engineering aspects of planning need to be reformulated even though the goal to attain remains substantially the same, requiring various objectives to be simultaneously accomplished to achieve the optimality of the power system development and operation. In many cases, these objectives contradict each other and cannot be handled by conventional single optimization techniques. In this paper, a multiobjective formulation for the siting and sizing of DG resources into existing distribution networks is proposed. The methodology adopted permits the planner to decide the best compromise between cost of network upgrading, cost of power losses, cost of energy not supplied, and cost of energy required by the served customers. The implemented technique is based on a genetic algorithm and an /spl epsiv/-constrained method that allows obtaining a set of noninferior solutions. Application examples are presented to demonstrate the effectiveness of the proposed procedure.