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.

Abstract
In recent years, power systems have been watching important advancements related with Plug-in-Electrical Vehicles (PEVs), Demand Side Management (DSM), Distributed Generation (DG), Microgrid and Smart Grid installations that directly affect distribution networks while impacting indirectly on Transmission studies. These changes will lead to an extra flexibility on the transmission-distribution boundary and to a significant modification of the load patterns, that are an essential input to planning studies. In this scope, this paper describes a multiyear Transmission Expansion Planning (TEP) solved by Evolutionary Particle Swarm Optimization (EPSO) and incorporating the impact of solar DG penetration. The primary substation load profiles and the solar generation profiles are taken into account on the planning problem. The numerical simulations were conducted using the IEEE 24 bus reliability test system in which the planning horizon is 3 years and the load growth is 2.5 % per year. If demand and solar DG peaks are coincident, then the liquid demand seen by the transmission network gets reduced enabling a reduction of investment costs. In the tested cases, these peaks were not coincident so that the optimal expansion plan remains unchanged even though the injected power from DG is large. This stresses the fact that solar DG may not on an isolated way contribute to alleviate the demand seen by transmission networks but should be associated with storage devices or demand side management programs.

Abstract
Hydrothermal systems should be characterized by a transmission-intensive nature in order to deal with climatic phenomena which, for example, can determine dry conditions in one region while there are large rainfalls in another one. Thus, the grid must be robust to deal with the different export/import patterns among regions and accommodate several economic dispatches. This paper describes a multiyear probabilistic Transmission Expansion Planning, TEP, model that uses Evolutionary Particle Swarm Optimization (EPSO) to deal with the uncertainties present in hydrothermal systems. The numerical simulations were conducted using the IEEE 24 bus reliability test system in which the planning horizon is 10 years and the load growth is 2,5% per year. The results highlight the importance of adopting expansion strategies to reduce the risk and consider the inflow variations in this type of systems.

Abstract
This paper describes an enhanced model for the Short Term Hydro Scheduling Problem, HSP, that includes the impact of operation decisions on the market prices and the possibility of adjusting the tailwater level and the generation and pumping efficiencies as a function of the flow. The solution approach uses an iterative procedure that solves in each iteration a linearized HSP problem using the linprog function of the MATLAB (R) Optimization Toolbox and that updates the value of the head to be used in the next iteration. The paper also includes results from a realistic Case Study based on the cascade of 9 hydro stations (4 of them with pumping) installed in the Portuguese section of the Douro River.