Abstract
Due to the high penetration of renewable energy into the energy matrix of today's power networks, the design of generating systems based only on static reserve assessment does not seem to be enough to guarantee the security of power system operation. From the wind power integration perspective, this energy source imposes additional requirements, mainly due to the inherent unpredictable characteristic of the wind. Besides the uncertainties in load and generating unit availabilities, the operating reserve needs also to deal with the fluctuation characteristic of the wind power. Therefore, more flexibility of the conventional generators (hydro and thermal) is required to provide system support services. This paper discusses a new methodology based on chronological Monte Carlo simulation to evaluate the operating reserve requirements of generating systems with large amounts of renewable energy sources, in particular, wind power.

Abstract
In response to the soaring energy crisis and the related pollution problems worldwide, it is essential to apply new technologies that use renewable energy sources in both an efficient and environmentally friendly manner. In this way, biomass offers one of the largest potential among renewable energy sources. The aim of this work is to demonstrate a novel fuzzy-based methodology for selecting hybrid energy systems fuelled by biogas. Fuzzy multi-rules and fuzzy multi-sets are used to evaluate the main operational characteristics of five types of renewable sources fuelled by biogas. The possibility of using the methodology for energy storage system evaluation is also assessed. The construction of the fuzzy multirules and fuzzy multi-sets is based on the following methods: Mamdani (fuzzification process), Max-Min (inference process), and Center of Gravity (defuzzification process). Several criteria are used: costs, efficiency, cogeneration, life-cycle, technical maturity, power application range, and environmental impacts. The methodology considers three different settings with two different constraints: costs and environment. One of the most relevant aspects presented by this work is about the previous classification of the criteria. It was created according to the different relevance observed among the attributes. The purpose of the proposed arrangement is to facilitate the understanding of the methodology and to increase the possibility of incorporating the decision makers' preferences on the decision-aid process. These aspects are essential to strengthen the final decision. © Springer-Verlag Berlin Heidelberg 2011.

Abstract
Energy policies in the European Union (EU) and its 27 member states respond to three main concerns namely energy security, economic development, and environmental sustainability. All the three "Es'' are pursued simultaneously with some slight differences in emphasizing the mutual importance of these, in particular the cost factors. The legislation of the EU (e. g., ETS-Emission Trading Scheme, directives) increasingly guides the member states' energy policies. However, energy policy directions are still made domestically, for example, on the support on renewable energy technologies. In this work, we look into distributed generation (DG), since it has been grown considerable in the past few years and can be used to partially fulfill renewable energy targets. The policy makers have to make decisions about regulation directives, more specifically they have to change the current regulation in order to incentive the increase in DG. However, these decisions have not only economic impacts but also technical impacts that must be accounted for. In this regard, a decision aid tool would help the policy makers in estimating producer economic impacts, as well as power network technical impacts, of various possible regulation directives. Here, we propose an interactive decision aid tool that models the aforementioned impacts and thus, can be used by policy makers to experiment with different regulation directives before deciding on the ones to set.

Abstract
This paper proposes a new methodology for dynamic security assessment and preventive control. In the first phase, an artificial neural network (ANN) is trained to provide the security status. ANN inputs are settled by a feature selection approach that takes into account the requisites of the control algorithm, to be applied in the second phase. The adaptive control methodology is based on the steepest descent method, where the usual explicit math functions to be dealt with are emulated by the trained ANN. To illustrate the developed approach, the methodology was applied to the control of dynamic security of Madeira island power system. Results attained so far show that the proposed approach was able to find the optimal control actions.

Abstract
fischeri Background: Photodynamic antimicrobial chemotherapy (PACT) combines light, a light-absorbing molecule that initiates a photochemical or photophysical reaction, and oxygen. The combined action of these three components originates reactive oxygen species that lead to microorganisms' destruction. The aim was to evaluate the efficiency of PACT on Vibrio fischeri: 1) with buffer solution, varying temperature, pH, salinity and oxygen concentration values; 2) with aquaculture water, to reproduce photoinactivation (PI) conditions in situ. Methodology/Principal Findings: To monitor the PI kinetics, the bioluminescence of V. fischeri was measured during the experiments. A tricationic meso-substituted porphyrin (Tri-Py(+)-Me-PF) was used as photosensitizer (5 mu M in the studies with buffer solution and 10-50 mu M in the studies with aquaculture water); artificial white light (4 mW cm(-2)) and solar irradiation (40 mW cm(-2)) were used as light sources; and the bacterial concentration used for all experiments was approximate to 10(7) CFU mL(-1) (corresponding to a bioluminescence level of 10(5) relative light units - RLU). The variations in pH (6.5-8.5), temperature (10-25 degrees C), salinity (20-40 g L(-1)) and oxygen concentration did not significantly affect the PI of V. fischeri, once in all tested conditions the bioluminescent signal decreased to the detection limit of the method (approximate to 7 log reduction). The assays using aquaculture water showed that the efficiency of the process is affected by the suspended matter. Total PI of V. fischeri in aquaculture water was achieved under solar light in the presence of 20 mu M of Tri-Py(+)-Me-PF. Conclusions/Significance: If PACT is to be used in environmental applications, the matrix containing target microbial communities should be previously characterized in order to establish an efficient protocol having into account the photosensitizer concentration, the light source and the total light dose delivered. The possibility of using solar light in PACT to treat aquaculture water makes this technology cost-effective and attractive.

Abstract
This paper presents a discrete approach, based on an improved integer version of the evolutionary particle swarm optimization (EPSO) algorithm, to solve the dynamic transmission expansion planning (TEP) problem. TEP corresponds to a mixed integer optimization problem that typically aims at identifying a schedule for transmission additions along an extended planning horizon considering operation and investment costs as well as a reliability index to measure the ability the system has to convey electricity from generation to consumers. After detailing the mathematical formulation of the TEP problem, this paper describes the enhanced EPSO algorithm and details its application to the TEP problem. The paper also includes a Case Study based on the IEEE 24 bus / 38 branch system to illustrate the application of the developed procedure.