Financial Risk Constrained Remote Controlled Switch Deployment in Distribution Networks

Recently, diverse technologies and apparatus are launched to enhance service reliability in the distribution level. In this regard, remote controlled switches (RCSs) can play a prominent role in declining interruption durations and costs. The stochastic nature of contingencies, however, imposes considerable uncertainty on the achievements of RCS deployment. This study presents a model to consider the financial risk induced by the uncertainty in an RCS placement problem. The risk awareness of distribution companies is scrutinised through an effective risk measure, i.e. conditional value at risk. In the model, the number and location of RCSs are determined such that the system expected cost is minimised and the risk is managed. The model is treated as a multi-objective problem with two conflicting objectives. The problem is then solved via a non-dominated sorting genetic algorithm II. This algorithm results in a set of non-dominated solutions. Finally, a fuzzy decision making method is applied to select the best placement strategy from the set. The presented model is applied on RBTS-Bus4, where the findings reveal the importance of considering the risk in an RCS placement problem as well as the capability of the method in doing so.