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.

Abstract
The emergence of self-consumption in low voltage consumers has technical and regulatory consequences, due to the changes in the network flows. In this paper these impacts are analysed through a small case study where different production/storage arrangements are considered. The technical analysis shows the voltage profiles' variation and identifies possible overvoltages and the effect of mitigation measures like storage. The regulatory analysis shows that, in order to maintain the remuneration of the Distribution System Operator (DSO) and Transmission System Operator (TSO), network tariffs parameters would change dramatically for the ordinary consumers, unless new rules are defined for the contribution of LV consumers.

Abstract
This paper aims at presenting a Home Energy Management System ( HEMS) module capable of scheduling electric water heater ( EWH) appliances in order to optimize the PV self-consumption. A multi-period optimization model is presented. Laboratory tests were conducted to validate the model and to demonstrate the capability of this HEMS module to address recent challenges of self-consumption in a domestic environment. A commercial EWH device developed by Bosch communicating with the HEMS module is used to perform the tests.

Abstract
This paper addresses the bidding problem faced by an electric vehicles (EV) aggregation agent when participating in the day-ahead electrical energy market. Two alternative optimization approaches, global and divided, with the same goal (i.e. solve the same problem) are described. The difference is on how information about EV is modeled. The global approach uses aggregated values of the EV variables and the optimization model determines the bids exclusively based on total values. The divided approach uses individual information from each EV. In both approaches, statistical forecasting methods are formulated for the EV variables. After the day-ahead bidding, a second phase (named operational management) is required for mitigating the deviation between accepted bids and consumed electrical energy for EV charging. A sequential linear optimization problem is formulated for minimizing the deviation costs. This chain of algorithms provides to the EV aggregation agent a pathway to move to the smart-grid paradigm where load dispatch is a possibility.

Abstract
In order to reduce the curtailment of renewable generation in periods of low load, operators can limit the import net transfer capacity (NTC) of interconnections. This paper presents a probabilistic approach to support the operator in setting the maximum import NTC value in a way that the risk of curtailment remains below a pre-specified threshold. Main inputs are the probabilistic forecasts of wind power and solar PV generation, and special care is taken regarding the tails of the global margin distribution (all generation all loads and pumping), since the accepted thresholds are generally very low. Two techniques are used for this purpose: interpolation with exponential functions and nonparametric estimation of extreme conditional quantiles using extreme value theory. The methodology is applied to five representative days, where situations ranging from high maximum NTC values to NTC=0 are addressed. Comparison of the two techniques for modeling tails is also comprised.

Abstract
This paper proposes a method to determine the availability of loads to provide tertiary Reserve Services (RS) in the framework of Smart Grids. The method uses Physically-Based Load Models (PBLM) and considers the remuneration paid for the RS provision. The concept of availability is also discussed in the paper and the method is applied to three different categories of domestic thermal appliances: electric water heater, air conditioners and refrigerators.