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
Strong adoption dynamics of private passenger electric vehicles (EV) will require adjustments in the operation and planning of electrical distribution grids. This work proposes a novel approach to assess the impact of electric vehicle charging while considering EV adoption dynamics and commuting patterns. The proposed model uses Geographic Information Systems (GIS) and is applied to a real-world case study. Results suggest that clustering of EV charging will occur and underline the relevance of accurate spatial and temporal charging pattern estimations for distribution grid planning. Overloading of distribution network elements was observed even under light EV penetration rates.

Abstract
The system operator is responsible for maintaining a constant balance between generation and load to keep frequency at the nominal value. This fundamental objective is achieved with upward (e.g., synchronized and nonsynchronized generation units) and downward (e.g., demand response, storage) reserve capacity. The system operator needs to define, in advance, the reserve capacity requirements that mitigate the risk of imbalances due to forecast errors and unplanned outages of generation units. The research trend is to apply probabilistic methodologies for setting the reserve requirements based on uncertainty forecasts for renewable generation and load, as well as a probabilistic modeling of units' outages. This chapter describes two probabilistic methods, which share a common modeling framework, for quantifying risk and reserve requirements in two types of electricity markets: (1) sequential markets with the reserves market after the energy market clearing and (2) cooptimization (or joint market clearing) of energy and reserves. Two case studies with real data are presented to illustrate the application of both methodologies.

Abstract
Reliable and smart information on the flexibility provision of Home Energy Management Systems (HEMS) represents great value for Distribution System Operators and Demand/flexibility Aggregators while market agents. However, efficiently delimiting the HEMS multi-temporal flexibility feasible domain is a complex task. The algorithm proposed in this work is able to efficiently learn and define the feasibility search space endowing DSOs and aggregators with a tool that, in a reliable and time efficient faction, provides them valuable information. That information is essential for those agents to comprehend the fully grid operation and economic benefits that can arise from the smart management of their flexible assets. House load profile, photovoltaic (PV) generation forecast, storage equipment and flexible loads as well as pre-defined costumer preferences are accounted when formulating the problem. Support Vector Data Description (SVDD) is used to build a model capable of identifying feasible HEMS flexibility offers. The proposed algorithm performs efficiently when identifying the feasibility of multi-temporal flexibility offers.

Abstract
Large scale integration of distributed generation (DG), particularly based on variable renewable energy sources (RES), in low voltage (LV) distribution networks brings significant challenges to operation. This paper presents a new methodology for mitigating voltage problems in LV networks, in a future scenario with high integration of distributed energy resources (DER), taking advantage of these resources based on a smart grid type architecture. These resources include dispersed energy storage systems, controllable loads of residential clients under demand side management (DSM) actions and microgeneration units. The algorithm developed was tested in a real Portuguese LV network and showed good performance in controlling voltage profiles while being able to integrate all energy from renewable sources and minimizing the energy not supplied.

Abstract
Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. The Home energy management system (HEMS), installed at low voltage residential clients, will play a crucial role on the flexibility provision to both system operators and market players like aggregators. Modeling and forecasting multi-period flexibility from residential prosumers, such as battery storage and electric water heater, while complying with internal constraints (comfort levels, data privacy) and uncertainty is a complex task. This papers describes a computational method that is capable of efficiently learn and define the feasibility flexibility space from controllable resources connected to a HEMS. An Evolutionary Particle Swarm Optimization (EPSO) algorithm is adopted and reshaped to derive a set of feasible temporal trajectories for the residential net-load, considering storage, flexible appliances, and predefined costumer preferences, as well as load and photovoltaic (PV) forecast uncertainty. A support vector data description (SVDD) algorithm is used to build models capable of classifying feasible and non-feasible HEMS operating trajectories upon request from an optimization/control algorithm operated by a DSO or market player.