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
This paper addresses the bidding problem faced by an aggregator of small prosumers, when participating in the day ahead energy market. A two-stage stochastic optimization model is proposed to support the aggregator in the definition of optimal and robust demand and supply bids. Stochastic programing is used to deal with uncertainty of end-users' behavior, outdoor temperature, electricity demand and PV generation. The proposed approach was compared to other benchmark strategy, using a case study of 1000 prosumers from the Iberian market.

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Abstract
Last decade has witnessed the birth and dissemination of microgeneration (MG) in most EU countries. MG growth and diffusion in LV networks are expected to continue in the next decade. At the same time, the interest on energy storage systems (ESS) applications to power systems has been intensifying in the last years, following some major technological achievements that improved ESS abilities and decreased their price. This article analyzes the impacts of MG and ESS dissemination in LV networks' losses. The central goal is to estimate the global impact on the Portuguese LV distribution system. For that purpose, a set of empirical studies was carried out over a set of representative networks, in which different MG and ESS scenarios were considered. The extrapolation of the results to the global LV points out to a loss reduction potential of more than 15%.

Abstract
In the beginning of the Iberian Electricity Market (MIBEL), in 2006, the Portuguese regulator created a new tariff scheme, aiming at responding to the new market competition environment. At the same time, the regulator intended to improve consumers' awareness and incentivize renewables generation. After one decade, this policy may be considered successful, as it led to a good level of transparency (all tariff costs are clear and public) and renewables production had increased considerably. However, this strategy has brought other less positive aspects. One of them is the attractiveness of the tariff system in terms of energy savings. In fact, the test cases present in this article demonstrate that the current tariff scheme does not stimulate energy efficiency. Other complementary studies are performed to illustrate the impact of the tariff structure design on the potential energy savings.

Abstract