Abstract
The restructuring of power systems with the introduction of electricity markets and decentralized structures increased the number of participating entities. This is particularly true in generation and retailing which are now provided under competition. Accordingly, it is important to develop models to simulate the behavior of these agents and to optimize their participation in electricity markets. Among them, it is essential to adequately model generation agents namely in countries having a large share of hydro stations. This paper describes an agent-based approach to model the day-ahead electricity market having a particular emphasis on hydro generation. Apart from the characterization of the agents, the paper details the introduction of the Q-Learning algorithm in the model as a way to enhance the performance of generation agents. This paper also presents some preliminary results taking the Portuguese generation system as an example.

Abstract
Power industry has been deeply restructured in recent decades. Along this period vertically integrated structures evolved to more disaggregated models in which some activities are now provided under competition as generation and retailing while some others are provided under regulated monopolies as network transmission and distribution. Simultaneously, most countries started to encourage the use of renewable sources, namely to cope with environmental issues and to enlarge the energy independency. Having in mind these ideas and given the development of the Iberian Electricity Market and the increase of the installed capacity in units paid by feed-in tariffs, it becomes important to estimate the impact of this feed-in generation in the market prices. This paper describes the approach that was developed to estimate this impact and the implemented software application. This application was then used to evaluate the impact on prices on all trading hours of 2013 of the Portuguese photovoltaic generation, the Portuguese wind generation, all the Portuguese feed-in generation and also all the Iberian feed-in generation.

Abstract
In order to overcome a number of difficulties detected on the Brazilian electricity market this paper proposes and tests a bid based short-term market. To simulate the behavior of the hydros in this new market, it was implemented an Agent-Based Model - ABM using the reinforcement QLearning algorithm, Simulated Annealing, and linear programming. In the simulations we used real data from the Brazilian power system encompassing more than 98% of the total hydro installed capacity and three years of market data. The results indicate that the management of (virtual) reservoirs can be under the responsibility of each hydro, which could save water according to their own risk perception, while it is maintained the current efficiency and security levels. Results also suggest that the final monthly short-term market prices can substantially decrease in comparison with the current prices.

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.

Abstract
Transmission Expansion Planning (TEP) is a complex optimization problem that has the purpose of determining how the transmission capacity of a network should be enlarged, satisfying the increasing demand. This problem has combinatorial nature and different alternative plans can be designed so that many algorithms can converge towards local optima. This feature drives the development of tools that combine high robustness and low computational effort. This paper presents a comparative analysis and a detailed review of the main Constructive Heuristic Algorithms (CHA) used in the TEP problem. This kind of tools combine low computational effort with reasonable quality solutions and can be associated with other tools to use in a subsequent step in order to improve the final solution. CHAs proved to be very effective and showed good performance as the test results will illustrate.