Abstract
Despite the benefits of precision agriculture and precision viticulture production systems, its rate of adoption in the Portuguese Douro Demarcated Region remains low. We believe that one way to raise it is to address challenging real-world problems whose solution offers a clear benefit to the viticulturist. For example, one of the most demanding tasks in wine making is harvesting. Even for humans, the environment makes grape detection difficult, especially when the grapes and leaves have a similar color, which is generally the case for white grapes. In this paper, we propose a system for the detection and location, in the natural environment, of bunches of grapes in color images. This system is able to distinguish between white and red grapes, and at the same time, it calculates the location of the bunch stem. The system achieved 97% and 91% correct classifications for red and white grapes, respectively.

Abstract
In the last decades propofol became established as an intravenous agent for the induction and maintenance of both sedation and general anesthesia procedures. In order to achieve the desired clinical effects appropriate infusion rate strategies must be designed. Moreover, it is important to avoid or minimize side effects which may be associated with adverse cardiorespiratory effects and delayed recovery. Nowadays, to attain these purposes the continuous propofol delivery is usually performed through target-controlled infusion (TCI) systems whose algorithms rely on pharmacokinetic and pharmacodynamic models (Schraag, 2001). This work presents statistical models to estimate both the infusion rate and the bolus administration. The modeling strategy relies on multivariate linear models for panel data (Wooldridge, 2002), based on patient characteristics such as age, height, weight and gender along with the desired target concentration. A clinical database collected with a RugLoopII device on 84 patients undergoing ultrasonographic endoscopy under sedation-analgesia with propofol and remifentanil, (Gambús et al., 2011), is used to estimate the models (training set with 74 cases) and assess their performance (test set with 10 cases). The results obtained in the test set comprising a broad range of characteristics are satisfactory since the models are able to predict bolus and infusion rates comparable to those of TCI. © 2012 IFAC.

Abstract
In competitive electricity markets with deep concerns at the efficiency level, demand response programs gain considerable significance. In the same way, distributed generation has gained increasing importance in the operation and planning of power systems. Grid operators and utilities are taking new initiatives, recognizing the value of demand response and of distributed generation for grid reliability and for the enhancement of organized spot markets' efficiency. Grid operators and utilities become able to act in both energy and reserve components of electricity markets. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The proposed method has been computationally implemented and its application is illustrated in this paper using a 32 bus distribution network with 32 medium voltage consumers.

Abstract
In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

Abstract
This paper presents a new iterative algorithm to compute a conjectured supply function electricity market equilibrium with DC transmission network constraints. This approach extends, to a network constrained system, a model previously developed by the authors for the single-bus case. At each iteration nodal prices are used to split the market into single prices areas. Since each area can be treated as a single-bus market from the transmission constraints' point of view, the single-bus algorithm is applied to compute the generators supply functions for each area. These new generators strategies are then cleared to determine new nodal prices and areas for the next iteration, and convergence is achieved when the network lines status and strategies of the generators do not change significantly in two consecutive iterations. The current approach has also been extended to deal with nodal elastic demands. Unlike previous approaches, the main contribution of this work is that the parameters of the first order approximation of the conjectured supply functions (intercepts and slopes) are endogenously determined, coherently with the network lines status. The algorithm has been applied to some illustrative case examples, and to a simplified version of the MIBEL market (Spain-Portugal). Results have shown to be very close to real data, and very relevant to analyze the economic impact of the capacity network constraints.

Abstract
Electricity physical and financial contracts are common instruments used by generators to reallocate their risk among other market participants. Likewise, from the regulator point of view, contracts are understood and promoted as a way to mitigate the generators market power exercise. Therefore the analysis of the impact of these derivatives on the electricity market prices and on the agents' strategic behavior is of interest for both regulator and market participants. This paper focuses on how physical and financial electricity contracts affect the generators' strategies, but does not deal with determining contracts' quantities or prices, that are assumed to be fixed and known. Strategies are computed for selected agents and different physical or financial contracted quantities, assuming inelastic demand. A CSFE methodology for endogenous conjectures computation, developed by the authors in previous works, has been applied. The examples highlight the importance of computing the conjectures when the contracted quantities vary, since the variable agents' position can significantly change the market results. The main conclusion is that the common understanding that contracts can decrease the market prices may be called into question. © 2012 IEEE.