Abstract
This paper is on wind energy conversion systems with full-power converter and permanent magnet synchronous generator. Different topologies for the power-electronic converters are considered, namely matrix and two-level converters. Also, a new fractional-order control strategy is proposed for the variable-speed operation of the wind turbines. Simulation studies are carried out in order to adequately assess the quality of the energy injected into the electric grid. Conclusions are duly drawn. © 2010 IEEE.

Abstract
This paper proposes neural networks in combination with wavelet transform for short-term electricity prices forecasting. In the new deregulated framework, producers and consumers require short-term price forecasting to derive their bidding strategies to the electricity market. Accurate forecasting tools are required for producers to maximize their profits and for consumers to maximize their utilities. The accuracy of the price forecasting attained with the proposed approach is thoroughly evaluated, reporting the numerical results from a real-world case study based on the electricity market of mainland Spain. © 2009 IEEE.

Abstract
The increased integration of wind power into the electric grid, as it occurs today in Portugal, poses new challenges due to its intermittency and volatility. Wind power forecasting plays a key role in tackling these challenges. A novel hybrid approach, combining wavelet transform, particle swarm optimization, and an adaptive-network-based fuzzy inference system, is proposed in this paper for short-term wind power forecasting in Portugal. A thorough comparison is carried out, taking into account the results obtained with seven other approaches. Finally, conclusions are duly drawn.

Abstract
This paper is on the short-term thermal scheduling (STTS) problem, particularly concerning the new competitive and environmentally constrained electricity supply industry. On the one hand, within the electricity market, STTS has evolved from a minimum-cost policy in state-owned monopolistic companies to a profit-based policy under market conditions. On the other hand, as a consequence of growing environmental concerns, an unprecedented change points to a scenario where it is necessary to take into account the constraints related to the environment. We propose a multiobjective optimization (MO) approach to solve the profit-based STTS problem with environmental concerns. Two case studies are considered: the IEEE 30-bus system and a 75-bus system. Finally, conclusions are duly drawn.

Abstract
This paper presents an application for next-day electricity prices forecasting based on neural networks. Good forecasting tools hedging against daily price volatility are becoming increasingly important in nowadays competitive electricity markets.. avowing misjudgement of future price movements and preventing considerable losses for consumers and producers. Next-day electricity price forecast is essential to consumers and to producers in planning the operations of their electric energy resources and for developing negotiation skills in order to achieve better profits. We evaluate the accuracy of the proposed application of neural networks for next-day electricity prices forecasting based on case studies for a real world electricity market and report our experience with this application.

Abstract
This paper provides a review and general backgrounds of research and developments in the field of thermal power systems operational planning, namely on economic and environmental policy issues. On the one hand, within the energy market, operational planning has evolved from a minimum-cost policy in state-owned monopolistic companies to a profit-based policy under market conditions. On the other hand, as a consequence of growing environmental concern, an unprecedented change points to a scenario where it is necessary to take into account the constraints related to the environment. Consequently, operational planning of thermal power systems needs to be not only considered within the energy market, but also within preserving healthy conditions and self recovery cycles in the environment.