Abstract
The paper presents a method for the analysis and design of industrial production systems based on a joint assessment of the cost and the quality of service. The operation of a production system is seen as the accomplishment of a sequence of missions, each one corresponding to the cost-effective production and delivery of a specified quantity of products within a specified time frame. The paper shows that the probability of successfully accomplishing a mission is a non-linear function of the cumulative production downtime and that this time cannot be obtained using conventional Markov based techniques. The paper also introduces an analytical model and a procedure that allows the density function of the downtime to be obtained and shows how, using these tools, the production costs and the quality of service may be assessed and related to the internal design of the shop floor. The method seems to be particularly valuable in the analysis of production systems integrated in just-in-time supply chains, in which the reliability of the deliveries is an outstanding requirement.

Abstract
The recent development of the concept of microgrid (mu Grid), associated to the emergent interest in microgeneration (mu Gen), has raised a number of challenges regarding the evaluation of the technical, economical and regulatory impacts of a high penetration of this kind of solutions in the power systems. In this paper, the topic of security of supply is addressed, aiming at evaluating the influence of mu Gen and mu Grids in the medium- and long-term availability of generation to serve the forecasted load. A Monte-Carlo based methodology is used to evaluate this influence and to assess the capacity credit of those entities.

Abstract
This paper describes an approach to address the generation expansion-planning problem in order to help generation companies to decide whether to invest on new assets. This approach was developed in the scope of the implementation of electricity markets that eliminated the traditional centralized planning and lead to the creation of several generation companies competing for the delivery of power. As a result, this activity is more risky than in the past and so it is important to develop decision support tools to help generation companies to adequately analyse the available investment options in view of the possible behavior of other competitors. The developed model aims at maximizing the expected revenues of a generation company while ensuring the safe operation of the power system and incorporating uncertainties related with price volatility, with the reliability of generation units, with the demand evolution and with investment and operation costs. These uncertainties are modeled by pdf functions and the solution approach is based on Genetic Algorithms. Finally, the paper includes a Case Study to illustrate the application and interest of the developed approach.

Abstract
Generation expansion planning gained a new dimension with the advent of electricity markets. It is now an activity decoupled from transmission and there are several agents competing to generate electricity and aiming at maximizing their individual profits. In view of this, it becomes more important to develop tools to help generation agents to build their expansion plans, internalizing several uncertainties in the model, an being able to simulate different possible reactions of the other competitors, given their impact in the profits of the agent being modelled. In this paper, we present a long-term decision aid tool that uses System Dynamics to model the long run of electricity markets together with Genetic Algorithms to solve the individual expansion problem of generation agents given their mixed-integer nature. Apart from the detailed description of the developed approach, the paper also includes a Case Study based on a four generation agent system to illustrate its application.

Abstract
This paper describes a hybrid approach in which generation cost and demand uncertainties are represented by fuzzy numbers and the life cycle of system components is modeled by probabilistic models. The Monte Carlo simulation model is used to sample system states according to the failure rates of the system components and a Fuzzy DC OPF model is used to analyse each sampled state. This Fuzzy DC OPF model adopts multi-parametric optimization techniques and admits that loads, generation costs or both of them simultaneously are modeled using fuzzy numbers. At the end of this process, it is possible to compute estimates of the Power Not Supplied and also of the exposure and robustness indices that characterize the ability that the system has to accommodate the specified uncertainties. This information can then be used to characterize the ability each reinforcement has in increasing the robustness of the system. Finally, the paper includes results for two case studies. On of them is based on a 6 bus system and the second uses the IEEE 24 bus/38 branch test system to illustrate the developed approaches. © 2010 IEEE.

Abstract
Investments in new generation, specially in renewables, grew up in several countries contributing to change the generation mix. Among these new technologies, wind power became an important source in the sense that the share in installed capacity is large in countries as Germany, Denmark, Spain and Portugal namely considering the prices paid to the generated power. These subsidizing schemes are in several cases responsible for a large amount of the final end user costs meaning that in the future new ways of integrating this power in the grid have to be adopted. This means that for investors it is important to evaluate from a economic point of view the interest of new wind power projects admitting changes in current tariff schemes. For regulatory agencies it is also important to investigate the impact of changes in current schemes. This paper details an approach to characterize this type of investments in terms of the Net Present Value, NPV, and the Internal Return Rate, IRR, so that more sounded investment and policy decisions are adopted. © 2010 IEEE.