Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-related constraints. We propose a distributed scenario-based chance-constrained optimization formulation for this problem. To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates

Wireless Cellular Networks

When aiming for achieving high spectral efficiency in wireless cellular networks, cochannel interference (CCI) becomes the dominant performancelimiting factor. This article provides a survey of CCI mitigation techniques, where both active and passive approaches are discussed in the context of both open- and closed-loop designs.More explicitly, we considered both the family of flexible frequency-reuse (FFR)-aided and dynamic channel allocation (DCA)-aided interference avoidance techniques as well as smart antenna-aided interference mitigation techniques, which may be classified as active approach