Planning of Distributed Generation Resources to Simultaneously Improve Reliability and Resilience of Distribution Network

Abstract

In this paper, a novel optimization framework is proposed for the assessment of distribution network reliability and resilience via distributed generation (DG) placement. In this regard, a stochastic multi-objective optimization model is introduced that utilizes optimal allocation of DG units along with an optimal service restoration strategy by using the network’s embedded remote-control switches. The model minimizes distribution network outage costs due to both reliability contingencies and resilience events while keeping DG investment costs minimum. The optimal service restoration problem is formulated as a mixed-integer linear programming (MILP) model that satisfies network technical constraints. In order to capture the uncertain nature of fault contingencies, two different scenario sets are generated. Historical data of the network’s fault-rates, and the failure probability functions of network components obtained from Monte Carlo simulation (MSC), are used for reliability and resilience scenarios, respectively. The non-dominated sorting genetic algorithm (NSGA-II) approach is applied to solve the model which provides a Pareto-optimum solution pool. A fuzzy decision-making logic tool is then applied to assist the network planners in opting the final solution from the Pareto-front. The proposed model is tested on IEEE 33-bus system and the simulation results show the effectiveness of the model.