Abstract
Due to its combinatorial nature, the Unit Commitment problem has for long been an important research challenge, with several optimization techniques, from exact to heuristic methods, having been proposed to deal with it. In line with one current trend of research, metaheuristic approaches have been studied and some interesting results have already been achieved and published. However, a successful utilization of these methodologies in practice, when embedded in Energy Management Systems, is still constrained by the reluctance of industrial partners in using techniques whose performance highly depends on a correct parameter tuning. Therefore, the application of metaheuristics to the Unit Commitment problem does still justify further research. In this paper we propose a new search strategy, for Local Search based metaheuristics, that tries to overcome this issue. The approach has been tested in a set of instances, leading to very good results in terms of solution cost, when compared either to the classical Lagrangian Relaxation or to other metaheuristics. It also drastically reduced the computation times. Furthermore, the approach proved to be robust, always leading to good results independently of the metaheuristic parameters used.

Abstract
The concept of microgrid (mu Grid) has been emerging as a way to integrate microgeneration (mu G) in low-voltage (LV) networks and simultaneously improve its potential benefits. Technical requirements to connect mu grids to LV networks have been studied in order to make this concept technologically feasible and safe to operate. However, the regulatory framework for economic integration of mu G and mu Grids on distribution systems, despite being crucial, is still an open issue. The main purpose of this paper is to contribute for the development of an appropriate economic regulation framework that removes the barriers to mu G and mu Grid development. To do so, the relevant costs and benefits resulting from the establishment of mu G and mu Grid are identified and a methodology for sharing those costs and benefits among the involved economic agents is presented. The only prerequisite of such a methodology is the existence of a net benefit to all economic agents.

Abstract
This chapter presents some case studies using fuzzy equivalent uniform annual worth analysis. It includes three case studies and each case is studied for both crisp and fuzzy cases. Trapezoidal fuzzy numbers and correlated, uncorrelated, and partially correlated cash flows are handled in the cases. © 2008 Springer-Verlag Berlin Heidelberg.

Abstract
This chapter first presents arithmetic operations over independent fuzzy numbers and economic concepts review. Then, it gives the techniques for comparing and ordering fuzzy numbers of independent numbers and dependent numbers. It examines fuzzy case with partial correlation. The chapter also includes many numerical applications. © 2008 Springer-Verlag Berlin Heidelberg.

Abstract
Even in a liberalized environment, managing the security of supply associated to the generating system continues to be a major task of the System Operators. The increased use of renewable energy, in particular wind power, adds new challenges to the process, namely in countries like Portugal and Spain, where strong investments in wind power have been done and are foreseen for the next years. In order to tackle this issue, REN (the Portuguese TSO), REE (the Spanish TSO) and INESC Porto (a RandD institute) joined together to develop a project where Monte Carlo simulation is used to evaluate the risk associated with specific future configurations of the generating system, until the horizon of 2025, in the framework of medium and long term generation planning of MIBEL (the Iberian electricity market). Probabilistic simulation was chosen because deterministic approaches to this problem, although simple to understand and easy to implement, are unable to tackle the complex relations between different uncertain variables. In this project, simulation is organized chronologically, in order to preserve the relations between load and the different variables associated to generation and produce meaningful risk indices of generation adequacy. A new feature is the analysis of the operational reserve, through a process that estimates, in each simulated state, the unforeseen change in load and wind generation. These unexpected changes are then compared with the total available operational reserve, defined by the secondary reserve plus the tertiary reserve units with lead time up to one hour. It is important to point out that the proposed simulation is a tool that is able to quantify the adequacy of different reserve requirements, solutions for reserve enhancement, etc. , in order to support the decision making process. This paper describes the concepts and assumptions of the simulation model and presents results and conclusions of some of the case studies carried out in the project.

Abstract
Loads estimation is becoming each time more fundamental for an efficient management and planning of electric distribution systems. Among the factors that contribute to this need of more efficiency are the increasing complexity of these networks, the deregulation process and the competition in an open energy market, and environment preservation requirements. However, the only information generally available at MV and LV levels is essentially of commercial nature, i.e., monthly energy consumption, hired power contracts and activity codes. In consequence, distribution utilities face the problem of estimating load diagrams to be used in planning and operation studies. The typical procedure uses measurements in typical classes of consumers defined by experts to construct inference engines that, most of the times, only estimate peak loads. In this paper, the definition of classes was performed by clustering the collected load diagrams. Artificial Neural Networks (ANN) were then used for load Curve estimation. This article describes the adopted methodology and presents some representative results. Performance attained is discussed as well as a method to achieve confidence intervals of the main predicted diagrams.