Abstract
In large scale power systems, PSS is not sufficient to damp inter-area oscillations; therefore, FACTS devices, such as SVC, which is one of the most important these devices, are typically used with PSSs. In this paper, the parameters of a power system stabiliser (PSS) and static VAR compensator (SVC) with help of a combination of a Differential Evolution algorithm (DE) and Local Search Algorithm (called the DELSA (Memetic DE algorithm)) are introduced, which are designed independently, converge to the correct and optimal solution in a small number of iterations and are attuned to damping low frequency oscillations, such as local mode oscillations, inter-area mode oscillations, other controllers modes, and modes of the generator excitation system. Suppose that the DE algorithm searches in a wide-ranging area, whereas the local search focuses on the attraction area, which probably has the optimal solution. We studied the three-area power system that was simulated in the time domain by MATLAB. © 2011 Praise Worthy Prize S.r.l. - All righs reserved.

Abstract
In this paper, economic analysis of gas-fired generator, biomass unit and wind turbine is carried out in different scenarios based on gas price, energy selling price, and load factor in Iran and the payback period and rate of return are determined. Then, the participation of the Distributed Generations (DGs) in a day-ahead energy and reserve market is simulated for scheduling from the view point of ISO. The ISO target in this model is minimizing total system producing cost considering system security constraints and technical limitation of all units. Finally, the simulation results demonstrate the reserve market as an apt place for DGs.

Abstract
This paper describes the methodology and results obtained in the studies developed for deriving loss profiles for the Portuguese electricity market. For each voltage level (LV, MV, HV and VHV) the losses were distributed by the corresponding global load diagram, proportionally to the square of the hourly consumption. Transformer losses are assigned to the consumers of voltage levels equal or smaller to the secondary voltage. Loss profiles (like load profiles) were developed for each specific year, with its calendar particularities, and the global energy balance expected for that year. A subsequent product of the adopted methodology is the set of loss factors, which are directly driven from these profiles. The methodology was developed in a project with EDP (the Portuguese distribution system operator) and the result was approved by the regulatory authority that adopted the proposed loss profiles for market use.

Abstract
Wind power forecasting is becoming an important tool in electricity markets, but the use of these forecasts in market operations and among market participants is still at an early stage. The authors discuss the current use of wind power forecasting in U.S. ISO/RTO markets, and offer recommendations for how to make efficient use of the information in state-of-the-art forecasts. © 2010 Elsevier Inc.

Abstract
This paper discusses risk management, contracting, and bidding for a wind power producer. A majority of the wind power in the United States is sold on long-term power purchase agreements, which hedge the wind power producer against future price risks. However, a significant amount is sold as merchant power and therefore is exposed to fluctuations in future electricity prices (day-ahead and real-time) and potential imbalance penalties. Wind power forecasting can serve as a tool to increase the profit and reduce the risk from participating in the wholesale electricity market. We propose a methodology to derive optimal day-ahead bids for a wind power producer under uncertainty in realized wind power and market prices. We also present an initial illustrative case study from a hypothetical wind site in the United States, where we compare the results of different day-ahead bidding strategies. The results show that the optimal day-ahead bid is highly dependent on the expected day-ahead and real-time prices, and also on the risk preferences of the wind power producer. A deviation penalty between day-ahead bid and real-time delivery tends to drive the bids closer to the expected generation for the next day.

Abstract
This paper reports new results in adopting information theoretic learning concepts in the training of neural networks to perform wind power forecasts. The forecast "goodness" is discussed under two paradigms: one is only concerned in measuring the deviation between the forecasted and realized values, the other is related with the value of the forecast in the electricity market for different agents. The results and conclusions are supported by a real case example.