Abstract
In the last decades power systems witnessed the implementation of an organizational and operational restructuring that lead to the introduction of competitive mechanisms in some activities of the value chain. This is the case of generation and retailing with the development of wholesale and retail markets. These developments together with a renewed emphasis on the adoption of more sustainable solutions while maintaining adequate security of supply levels contributed to increase the interest of generation companies for models enabling the optimization of the use of generation assets or for models and tools to help them to prepare and test bidding strategies to the day-ahead markets. Having in mind the increased complexity of the operation of power systems, Agent-Based Models, ABM, are been used to complement the traditional optimization and equilibrium models, taking advantage of the interaction between agents acting in a simulation environment. In this scope, this paper describes an ABM model that uses Q-learning to provide knowledge for the agents to behave in an optimal way. This model is designed to mimic the main features of the common electricity market between Portugal and Spain, the MIBEL. Apart from describing the developed model, this paper also includes preliminary results from its application to the MIBEL case.

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
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. Additionally, the efficiency of hydraulic turbines is treated as a variable dependent on the discharged flows. The developed solution algorithm uses an iterative approach that solves in each iteration a linearized HSP problem using the linprog function of the MATLAB (R) Optimization Toolbox. In each iteration, the value of the head to use in the next iteration is updated. The paper reports 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.

Abstract
A vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24 h period of a summer day was simulated a three-level tariff scheme was used. The new contribution of the proposal is a modulation scheme of a two-level Model Predictive Control's control signal as an interface block between the Model Predictive Control output and the domestic appliance that functions as a two-state power switch, thus reducing the Model Predictive Control implementation costs in home appliances with thermal regulation requirements.

Abstract
This paper describes a new current control method that enhances the dynamic performance of a singlephase bidirectional AC-DC battery charger to provide a high-frequency link between the grid and electric vehicle. The single-stage structure and the bidirectional power flow make the matrix converter an attractive solution for electric vehicle (EV) battery charging applications in the context of smart grids. The operating principles and modulation method are analyzed and discussed in detail. Furthermore, a current controller improved with a Smith predictor is proposed to decrease the phase delay in the measurement of the average current in the battery pack. The SP reduces the rise time to around a third and the settling time to half when compared with a PI controller. Simulations and experimental results from a laboratory prototype are shown to verify the feasibility of the proposed control scheme.