Abstract
Marginal prices have been recognized as the core approach to the economic evaluation of generation and transmission services in an electricity market environment. In this context, this paper presents the New Fuzzy Optimal Power Flow algorithm as a model to addresses the impact of load and generation cost uncertainties in nodal marginal prices. Since loads and generation costs are represented by fuzzy numbers, nodal marginal prices will no longer be represented by deterministic values, but rather by fuzzy membership functions reflecting the specified uncertainties. The paper also presents the algorithm used for the integration of the transmission losses effect on the results. Since the proposed algorithm uses multiparametric programming techniques, it contributes to characterize in a better way the system behavior. Finally, it includes results based on the IEEE 24 bus/38 branch test system to illustrate the proposed approach.

Abstract
Modeling uncertainties in power systems has long interested researchers. Nowadays, as in 70's, the volatility associated with generation or fuel prices, for one side, and the uncertainties related with load forecasting and generation capacity, for another, places a new emphasis on this kind of problems. As a result of this renewed interest, in this paper we are enlarging the original Fuzzy Optimal Power Flow, FOPF, model in order to consider not only load uncertainties, but also uncertainties in generation or fuel prices, specified using trapezoidal fuzzy numbers. This new approach is based on multiparametric linear programming techniques that lead to the identification of a number of critical regions covering all the uncertainty space. This contributes to build more accurate membership functions of all variables, namely generations, branch flows and power not supplied.

Abstract
The scheduling of preventive maintenance actions of generators is not a new problem but gained in recent years a new interest with the advent of electricity markets. In this paper we report the research on this topic developed during the preparation of the MSc Thesis of the second author. In this paper we formulate the problem as a mixed integer optimization problem and we describe the use of Simulated Annealing to solve it. Simulated Annealing is a very appealing metaheuristic easily implemented and providing good results in numerous optimization problems. The paper includes results obtained for a Case Study based on a realistic generation system. This research work was proposed and developed with the collaboration of the third and fourth authors, from EDP Produção, Portugal.

Abstract
Photodynamic therapy is a very promising approach to inactivate pathogenic microorganisms. The photodamage of cells involves reactive oxygen species (ROS) which are generated in situ by two main mechanisms (type I and/or type II). The mechanism responsible for the photoinactivation (PI) of a bioluminescent recombinant Escherichia coli, induced by three different cationic porphyrins, was identified in this work using a rapid method based on the monitoring of the metabolic activity of this bacterium. The inhibitory effect of the photodynamic process in the presence of a singlet oxygen quencher (sodium azide) or free radical scavengers (D-mannitol and L-cysteine) was evaluated by exposing bacterial suspensions with 0.5 mu M Tri-Py+-Me-PF, 5.0 mu M Tetra-Py+-Me or 5.0 mu M Tri-SPy+-Me-PF to white light. Strong bacterial protection was observed with sodium azide (100 mM) for the three cationic porphyrins. However, in the presence of Tri-Py+-Me-PF and Tetra-Py+-Me and the free radical scavengers (L-cysteine and D-mannitol) the reduction on the bacterial bioluminescence was significantly higher and similar to that obtained in their absence (5.4-6.0 log reduction). In the case of Tri-SPy+-Me-PF two distinct behaviours were observed when L-cysteine and D-mannitol were used as free radical scavengers: while the presence of L-cysteine (100 mM) lead to a bacterial protection similar to the one observed with sodium azide, in the presence of D-mannitol only a small protection was detected. The high inhibition of the PS activity by L-cysteine is not due to its radical scavenger ability but due to the singlet oxygen quenching by the sulfanyl group (-SH). In fact, the photodecomposition of 1,3-diphenylisobenzofuran in the presence of Tri-SPy+-Me-PF is completely suppressed when L-cysteine is present. The results obtained in this study suggest that singlet oxygen (type II mechanism) plays a very important role over free radicals (type I mechanism) on the PI process of the bioluminescent E. coli by Tri-Py+-Me-PF, Tetra-Py+-Me and Tri-SPy+-Me-PF. Although the use of scavengers is an adequate and simple approach to evaluate the relative importance of the two pathways, it is important to choose scavengers which do not interfere in both PI mechanisms. Sodium azide and D-mannitol seem to be good oxygen and free radical quenchers, respectively, to study the PI mechanisms by porphyrinic photosensitizers.

Abstract
This paper presents a multiyear dynamic transmission expansion planning, TEP, model aiming at minimizing operation and investment costs along the entire planning horizon while ensuring an adequate quality of service and enforcing constraints modeling the operation of the network along the planning horizon. The developed model profits from the experience of planners when preparing a list of possible branch (lines and transformers) additions each of them associated to the corresponding investment cost. The objective of solving a TEP problem is to select a number of elements of this list and provide its scheduling along the planning horizon such that one is facing a mixed integer optimization problem. In this case, this problem was solved using a discrete evolutionary particle swarm optimization algorithm, DEPSO. based on already reported EPSO approaches but particularly suited to treat discrete problems. Apart from detailing the developed DEPSO, this paper describes the mathematical formulation of the TEP problem and the adopted solution algorithm. It also includes results of the application of the DEPSO to the TEP problem using two test networks widely used by other researchers on this area.

Abstract
The scheduling of maintenance actions of generators is not a new problem but gained in recent years a new interest with the advent of electricity markets because inadequate schedules can have a significative impact on the revenues of generation companies. In this paper we report the research on this topic developed during the preparation of the MSc Thesis of the second author. The scheduling problem of generator maintenance actions is formulated as a mixed integer optimization problem in which we aim at minimizing the operation cost along the scheduling period plus a penalty on energy not supplied. This objective function is subjected to a number of constraints detailed in the paper and it includes binary variables to indicate that a generator is in maintenance in a given week. This optimisation problem was solved using Simulated Annealing. Simulated Annealing is a very appealing metaheuristic easily implemented and providing good results in numerous optimization problems. The paper includes results obtained for a Case Study based on a realistic generation system that includes 29 generation groups. This research work was proposed and developed with the collaboration of the third and fourth authors, from EDP Producao, Portugal.