There is a worldwide interest in the use of renewable resources for the generation of electrical power. Between all renewable energy sources, ocean energy is the one that still has the furthest to go to achieve commercial maturity. As the most promising ocean energy that can be harnessed in the Basque Country is wave energy, the NEREIDA MOWC project, promoted and led by Basque Energy Board, aims to prove the viability of this technology for future commercial plants. In this paper, an Oscillating Water Column-based wave power generation plant is modeled and controlled by means of a new control scheme that takes into account the influence of turbine damping and the main requirements imposed by new Grid Codes when a grid fault occurs on the transmission system.

 This paper deals with the load shifting problem in a household equipped with smart appliances and an energy storage unit with conversion losses. The problem is faced by establishing an event driven Model Predictive Control framework aiming to meet the real life dynamics of a household and to keep low the impact of the control system on the total electric energy consumption. The proposed approach allows the consumer to minimize the daily energy cost in scenarios characterized by Time of Use tariffs and Demand Side Management, by dynamically evaluating the best time to run of the appliances and the optimal evolution of the battery level of charge. A proper set of realistic simulations validates the proposed approach, showing the relevance of the energy storage unit in the domestic load shifting architecture.

 The use of wind power has been increasing rapidly over the last few decades and according to all predictions this trend is likely to continue. At the same time, the need for better cost effectiveness of wind power plants has stimulated growth in wind turbines' size and rated power, thus making wind turbine structure very flexible. Therefore an advanced control system must be used to reduce structural loads and fatigue and enable optimal wind energy conversion in a wide range of wind speeds. One such control system, with emphasis on reduction of wind turbine structural oscillations, is developed in this paper.

 The paper describes an on-line prediction algorithm to estimate, over a determined time horizon, the solar irradiation of a specific site. The learning algorithm is based on a radial basis function network and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique. An Extended Kalman Filter (EKF) is used to update all the parameters of the network. The on-line algorithm gives the chance to avoid the initial training of the neural network. The one day forecasted irradiation is used to estimate the power production of a PhotoVoltaic (PV) plant.

 Besides retrofitting, modernization and new ways of construction of the buildings, the cheaper and recently a very popular approach how to optimize energy consumption is to employ better control algorithms for the buildings. Predictive control has proven to be a strategy useful in many industries and became a suitable option for the building sector as well. The main bottleneck of this approach is a need for a fine model.

There exist a number of building models and identification approaches. This paper provides a brief survey of the building modeling approaches and discusses their properties and applicability for the predictive control. Having a number of potential models at hand, the procedure of the model selection suitable for predictive control is presented. Finally, the performance of the model selection procedure is examined in a two zone building. The results are then presented and the conclusions drown.

 Low energy buildings have been attracting much attention lately. Most of the research is focused on the building construction or alternative energy sources. Recently, there has been an intense research in the area of Model Predictive Control (MPC) for buildings. The main principle of such a controller is a trade-off between energy savings and user welfare making use of predictions of disturbances acting on the system (ambient temperature, solar radiation, occupancy, etc.). Usually, the thermal comfort is represented by a static range for the operative temperature according to the international standards. By contrast, this paper is devoted to the optimization of the Predicted Mean Vote (PMV) index which, opposed to the static temperature range, describes user comfort directly. PMV index, however, is a nonlinear function of various quantities, which makes the problem more difficult to solve. The paper will show the main differences in MPC problem formulation, compare the control performance both to the conventional and predictive control strategies, point out that the proposed optimal control problem formulation shifts the savings potential of classical MPC by additional 11% and finally, the quality of the fulfillment of the thermal comfort will be addressed.