Abstract
This paper presents a study about the influence of Distributed Energy Resources' (DER) flexibility on the operation of a Medium Voltage (MV) network, in a Smart Grid (SG) environment. An AC multi-temporal Optimal Power Flow (OPF) tool was developed and used to simulate the impact of the DER flexibility (including storage devices, EVs, controllable loads and micro-generation) in distribution network operation. Some simulations are presented, demonstrating the impact that DER flexibility can have on solving operation problems namely in terms of branch loading and voltage limits.

Abstract
This paper presents a new proposal for sensor fusion in power system state estimation, analyzing the case of data sets composed of conventional measurements and phasor measurements from PMUs. The approach is based on multiple criteria decision-making concepts. The equivalence of an L-1 metric in the attribute space to the results from a Bar-Shalom-Campo fusion model is established. The paper shows that the new fusion proposal allows understanding the consequences of attributing different levels of confidence or trust to both systems. A case study provides insight into the new model.

Abstract
In this article, we present a holistic framework for the integration of electric vehicles (EVs) in electric power systems. Their charging management and control methodologies must be optimized to minimize the negative impact of the charging process on the grid and maximize the benefits that charging controllability may bring to their owners, energy retailers, and system operators. We have assessed the performance of these methods initially through steady-state computational simulations, and then we validated them in a microgrid (MG) laboratory environment.

Abstract
The inclusion of new energy management technologies in buildings and increasing integration of distributed energy resources in electricity networks will require new operation and planning approaches from system operators. Currently, distribution network planning is rather inefficient as network assets are oversized to meet the peak demand of a worst case scenario which is unlikely to occur. In addition, flexibility provided by controllable loads is not taken into account at all. This paper presents an innovative approach that integrates the potential flexibility of distributed energy resources in the planning exercise. It incorporates two optimization problems: a low-level one to optimize the operation of controllable resources; and a high-level one based on an OPF that uses the available flexibility to solve network problems. The proposed method allows obtaining costeffective solutions that make a better use of the flexible resources available, avoiding high capital investments in network reinforcement.

Abstract
The increasing integration of Distributed Energy Resources (DER) in electricity networks has required an improvement in the network management procedures. While the operation paradigm is evolving and adapting to the new network features, the planning approach is rather inefficient as network assets are usually oversized to meet the worst-case scenario. In this regard, this paper presents an innovative methodology that integrates the potential flexibility of DER into the planning process, in an attempt to bridge the gap between current network operation approaches and the planning methods. It includes an analysis of future scenarios, providing different reinforcement plans considering the realistic network operation for those scenarios. The proposed optimal design of the reinforcement plans has two complementary processes: First to optimize flexible resources in their owner's perspective and second to reschedule the flexible resources' operation when the DSO needs to solve technical problems. The model has been tested in a typical Portuguese medium voltage network using future scenarios of DER integration from ENTSO-E. The results conclude that the proposed methodology leads to cost-effective solutions, which provide a better use of flexible resources, deferring high capital investments in network reinforcement.