Abstract
The increased integration of wind power into the grid poses challenges due to its intermittency. Besides, deregulation of the energy markets brings electricity prices uncertainty. Hence, a new hybrid intelligent approach is proposed in this paper to forecast wind power and electricity prices in the short-term. Results from real-world case studies are presented, in order to illustrate the proficiency of the proposed approach. Finally, conclusions are duly drawn.

Abstract
In this book chapter, two important applications of linear programming are presented from the electric power industry, namely short-term hydro scheduling and the development of offering strategies for wind power producers. The linear programming approach is proposed to solve the problems related with generation companies whose main goal is to maximize profits. On the one hand, the main concern of hydroelectric companies is to find the optimal scheduling of hydroelectric power plants, for a short-term period in which the electricity prices are forecasted. The actual size of hydro systems, the continuous reservoir dynamics and constraints, still pose a real challenge to the modelers. On the other hand, wind power producers are entities owning generation resources and participating in the electricity market. The challenges for wind power producers are related to two kinds of uncertainties: wind power and electricity prices. It can be concluded that linear programming represents a robust approach for these two problems.

Abstract
This paper is about environmental protection of our habitat in what regards limiting the pollutant emission due to thermal power plants, burning fossil fuels, burning coal, oil or gas to convert into electric energy. On the one hand, within the liberalized energy power markets, schedule of thermal power plants has evolved from a point of view optimisation problem of electric companies to a market level problem. On the other hand, as a consequence of growing environmental concern, the impact of conventional power plants on the environment is being considered and efforts are being made to limit this impact. Consequently, the short-term schedule for thermal power plants needs to be not only considered within the energy market, but also within preserving healthy conditions and self recovery cycles in the environment. We present a case study for thermal power plants considering pollutant emissions and compare the results with the usual approach, ignoring pollutant emissions.

Abstract
In the present day, with the deregulation of the electric power sector, business is recognized as a bid to win the best profit. In this new and competitive environment, a hydroelectric power utility has to decide the optimal management of the inflows and the water stored in its reservoirs, maximizing profit from selling energy without compromising future potential profit. This paper is on the problem of short-term hydro scheduling, concerning head-sensitive cascaded reservoirs, and the algorithmic aspects of its solution. We propose and compare optimization methods based on dynamic programming, linear and non-linear network programming. Finally, based on numerical simulation results, we report and illustrate our experience.

Abstract
This paper presents an artificial neural network approach 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. A three-layered feedforward artificial neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting the next 168 hour electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed approach, reporting the numerical results from a real-world case study based on an electricity market.

Abstract
This paper provides an approach to short-term scheduling of thermal units, designed to simultaneously address the economic issue of the fuel cost incurred on the commitment of the units and the environmental consideration due to emission allowance trading. The simultaneous address of the fuel cost with the emission is modeled by a multi-objective optimization problem, which is solved by a combination of the weighted sum method with the epsilon-constraining method. A numerical example for different values of a scaling factor is considered in order to obtain the non-dominated solutions of the trade-off curve between fuel cost and emission. Our approach presents a new parameter, ratio of change, and the corresponding gradient angle, to enable the proper selection of a compromise commitment for the units. Copyright (C) 2007 John Wiley & Sons, Ltd.