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.

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.

Abstract

Abstract
This paper analyzes the impact on market prices of the policies that have been adopted in Brazil to foster electricity from renewable energy sources (RES-E), namely wind power. In recent years the Brazilian Government implemented a series of policies that enabled a strong growth of RES-E. Recently more than 14 GW of wind and solar power were contracted. However, as most of the assets are concentrated in specific regions, these policies will induce price differences among areas of the country. In this scope, this paper describes a System Dynamics based model of the Brazilian generation system to evaluate the impact on prices from the deployment of these new sources. The paper describes simulations using realistic data for the Brazilian power system and the results suggest that the difference of prices in the country tend to increase since the Northeast region of the country concentrates most of the wind parks.