Abstract
In this paper, a proposal for an ancillary services (AS) market framework addressing voltage control in multi-microgrid systems is presented. This var market proposal for MV distribution systems can be adopted to involve Distributed Generation (DG) units and microgrids in AS provision. In the approach that was developed each player is given the opportunity to submit its bid to the var market and the market settlement is performed using an Optimal Power Flow (OPF) formulation in order to minimize the price of reactive power purchased by the Distribution System Operator (DSO). This market is based on var capacity use and runs daily after the scheduling of the generation units for a period of operation of one clay.

Abstract

Abstract
The spread and globalization of distributed generation (DG) in recent years has should highly influence the changes that occur in Electricity Markets (EMs). DG has brought a large number of new players to take action in the EMs, therefore increasing the complexity of these markets. Simulation based on multi-agent systems appears as a good way of analyzing players' behavior and interactions, especially in a coalition perspective, and the effects these players have on the markets. MASCEM - Multi-Agent System for Competitive Electricity Markets was created to permit the study of the market operation with several different players and market mechanisms. MASGriP - Multi-Agent Smart Grid Platform is being developed to facilitate the simulation of micro grid (MG) and smart grid (SG) concepts with multiple different scenarios. This paper presents an intelligent management method for MG and SG. The simulation of different methods of control provides an advantage in comparing different possible approaches to respond to market events. Players utilize electric vehicles' batteries and participate in Demand Response (DR) contracts, taking advantage on the best opportunities brought by the use of all resources, to improve their actions in response to MG and/or SG requests.

Abstract
Real-time monitoring of the fabrication process of tapering down a multimode-interference-based fiber structure is presented. The device is composed of a pure silica multimode fiber ( MMF) with an initial 125 mu m diameter spliced between two single-mode fibers. The process allows a thin MMF with adjustable parameters to obtain a high signal transmittance, arising from constructive interference among the guided modes at the output end of the MMF. Tapered structures with waist diameters as low as 55 mu m were easily fabricated without the limitation of fragile splices or difficulty in controlling lateral fiber alignments. The sensing device is shown to be sensitive to the external environment, and a maximum sensitivity of 2946 nm/refractive index unit in the refractive index range of 1.42-1.43 was attained. (c) 2012 Optical Society of America

Abstract
The coordination between wind farms and pumping storage units increases the wind farm's controllability and maximizes the profit. In literature, several optimization algorithms were proposed for deriving the optimal coordination between wind farms and storage units. However, no attention has been given to operational management strategies for following the strategy that results from the optimization phase. This paper presents three possible heuristic strategies for managing the wind-hydro system during the operational day according to a day-ahead optimized strategy. Moreover, a chance-constrained based optimization algorithm, that includes wind power uncertainty, is also described. The algorithms are tested in a real case-study.

Abstract
This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network's execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network's integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). © 2012 IEEE.