Abstract
This paper proposes a method to quickly and accurately predict the dynamic response of a power system during an under-frequency load shedding scenario. Emergency actions in a power system due to loss of generation typically calls for under-frequency load shedding measures to avoid potential collapse due to the lack of time in which to correct the imbalance via other means. Due to the slow and repetitious use of dynamic simulators the need for a fast and accurate procedure is evident when calculating optimal bad-shedding strategies A neural network (NN) seems to he an ideal solution for a quick and accurate way to replace standard dynamic simulations. The steps taken to produce a viable NN and corresponding results will he discussed.

Abstract
It is foreseeable that electricity retailers for electrical mobility will be market agents. These retailers are electric vehicle (EV) aggregation agents, which operate as a commercial middleman between electricity market and EV owners. Furthermore, with the foreseen evolution of the smart-grid concept, these agents will be able to control the EV charging rates and offer several ancillary services. This paper formulates an optimization problem for the EV aggregation agent participation in the day-ahead and secondary reserve market sessions. Forecasting issues are also discussed. The methodology was tested for two years (2009 and 2010) of the Iberian market, considering perfect and naïve forecast for all variables of the problem. © 2011 IEEE.

Abstract
An electric vehicle (EV) aggregation agent, as a commercial middleman between electricity market and EV owners, participates with bids for purchasing electrical energy and selling secondary reserve. This paper presents an optimization approach to support the aggregation agent participating in the day-ahead and secondary reserve sessions, and identifies the input variables that need to be forecasted or estimated. Results are presented for two years (2009 and 2010) of the Iberian market, and considering perfect and naive forecast for all variables of the problem.

Abstract

Abstract

Abstract
This paper presents an application of probabilistic methodologies to evaluate the reserve requirements of generating systems with a large penetration of renewable energy sources. The idea is to investigate the behavior of reliability indices, including those from the well-being analysis, when the major portion of the renewable sources comes from wind power and other intermittent sources. A new simulation process to address operating reserve adequacy is introduced, and the correspondent reliability indices are observed. Case Studies on the Portuguese and Spanish generating systems are presented and discussed.