Abstract
The Brazilian Power System is mostly supplied by hydro-generation. In this context there is a strong connection between rain-fall regimes and the Electricity Prices in the short term market. This work describes the main features, developments and functioning of a System Dynamics model that simulates the four Brazilian short term electricity submarkets. Based on studies reporting the change in rainfall regimes in Brazil due to Climate Change, we analyze the impacts of these changes in each specific region and in the electricity markets as a whole. The results provide good insights on the impacts of Global Warming in the Brazilian Power System, indicating for instance that the Southeast/Center-West Electricity Submarket is the one that will be most affected by the global issue in terms of rise in the electricity prices.

Abstract
While in the past the installation of distributed generation systems was oftentimes motivated by attractive feed-in tariffs, many consumers now install such systems to increase their self-sufficiency in order to avoid rising electricity cost. However, due to the intermittency of photovoltaic systems and the dependency of cogeneration units on the thermal demand, there remains a significant mismatch between local generation and demand. Electrical storage devices can align generation and demand, minimizing the mentioned gap and hence the amount of energy that needs to be taken from the grid. This paper describes a model to determine the optimal dispatch of such a system, taking the thermal system into account in order to account for the dependency of cogeneration units. A mixed integer program is presented, which identifies the cost minimizing operation schedule. This model is tested using a case study considering realistic values taken from the German case.

Abstract
The unbundling of the electricity sector in several activities, some of them provided in a regulated way and some others under competition, poses a number of challenging problems namely because in several areas there are conflicting objectives associated to different stakeholders. These different views and objectives paved the way to the development of new multiobjective tools able to represent this new paradigm. In this scope, this paper presents a multiobjective (MO) formulation for the Transmission Expansion Planning (TEP) problem using a new solution approach that combines concepts of evolutionary computation and multi agent population algorithms. The new proposed tool is termed as Multi-Population and Multiobjective Evolutionary Particle Swarm Optimization - MEPSO-II. The TEP problem is handled in a realistic way preserving the holistic view over the entire planning horizon and the true grid behavior because it considers the multi-stage nature of the problem and we use an AC Optimal Power Flow (AC-OPF) model to gain insight on the operation conditions of the network. The multi objective formulation considers the total system cost, on one side, and the Expected Power Not Supplied (EPNS), on the other. The total system cost comprises the investment cost in new equipment and the operation costs while the EPNS takes into account the uncertainties related to the non- ideal behavior of system components using a non-chronological Monte Carlo simulation. Numerical simulations are conducted using the IEEE 24 and the 118 Bus Test Systems in order to compare the proposed MO tool against other algorithms through performance evaluation indices. Although being a higher time-consuming tool, the MEPSO-II enables improving the Pareto-Front and therefore it gives more insight to transmission network planners when compared with other consolidated algorithms described in the literature.

Abstract
This paper addresses the impact of the penetration of Electric Vehicles, EV's, in the electricity market prices and on the load curves of the Iberian Electricity Market, established between Portugal and Spain. These impacts are evaluated taking the year of 2011 as reference. For every day of this year, the hourly market data was downloaded from the web site of the Iberian Market Operator and then the bid curves were adequately altered considering the commissioning of new stations under construction or planned till 2020, the planned increasing presence of wind parks and PV stations and the demand increase from end consumers. Then, the hourly demand was further increased to simulate the charging of EV's, admitting several scenarios for the charging periods and for the number of EV's in 2020. Using this information, the hourly selling and buying curves were altered and the hourly market price was then estimated. The paper extensively reports the results obtained for these different scenarios for the horizon of 2020. For the analysed scenarios, the results indicate that the impact of EV's on electricity prices and load curves will depend largely on the number of EV's and in a less significant way on the charging policy that is adopted. © 2013 IEEE.

Abstract
The restructuring of power systems induced new challenges to generation companies in terms of adequately planning the operation of power stations in order to maximize their profits. In this scope, hydro resources are becoming extremely valuable given the revenues that their operation can generate. In this paper we describe the application of the Matlab® Linprog optimization function to solve the Short Term Hydro Scheduling Problem, HSP, admitting that some stations are installed in the same cascade and that some of them have pumping capabilities. The optimization module to solve the HSP problem is then integrated in an iterative process to take into account the impact that the operation decisions regarding the hydro stations under analysis have on the market prices. The updated market prices are then used to run again the HSP problem thus enabling considering the hydro stations as price makers. The developed approach is illustrated using a system based on the Portuguese Douro River cascade that includes 9 hydro stations (4 of them are pumping stations) and a total installed capacity of 1485 MW.

Abstract
With the increase of electricity tariffs and the decreasing costs for distributed generation technologies, more and more residential consumers are deploying local generation systems to satisfy their electricity demand in order to reduce overall cost. Typically, however, a mismatch between electricity generation and demand remains. Storage systems enable consumers to reduce this mismatch by storing locally generated electricity for later consumption, instead of feeding excess generation into the grid. This paper analyzes the economics of storage installations in a residential consumer context. A linear program is presented to determine the optimal dispatch, and Simulated Annealing is used to identify the cost minimizing system configuration. The developed approach is tested for a multi-family house in Germany.