Abstract
This paper addresses the bidding problem faced by an electric vehicles (EV) aggregation agent when participating in the day-ahead electrical energy market. Two alternative optimization approaches, global and divided, with the same goal (i.e. solve the same problem) are described. The difference is on how information about EV is modeled. The global approach uses aggregated values of the EV variables and the optimization model determines the bids exclusively based on total values. The divided approach uses individual information from each EV. In both approaches, statistical forecasting methods are formulated for the EV variables. After the day-ahead bidding, a second phase (named operational management) is required for mitigating the deviation between accepted bids and consumed electrical energy for EV charging. A sequential linear optimization problem is formulated for minimizing the deviation costs. This chain of algorithms provides to the EV aggregation agent a pathway to move to the smart-grid paradigm where load dispatch is a possibility.

Abstract
This paper proposes a method to determine the availability of loads to provide tertiary Reserve Services (RS) in the framework of Smart Grids. The method uses Physically-Based Load Models (PBLM) and considers the remuneration paid for the RS provision. The concept of availability is also discussed in the paper and the method is applied to three different categories of domestic thermal appliances: electric water heater, air conditioners and refrigerators.

Abstract
In re-regulated markets, the maximization of profits creates the tendency to postpone investments in the network infrastructure, with negative effects on losses. In order to oppose this tendency, several countries adopt regulation directives that reward the distributors if losses are reduced and penalize them if losses increase. This is the case of Portugal which adopted loss penalty/reward scheme may be found in [1]. Given the current framework, EDP Distribuição (EDP Group), Portugal, a Distribution System Operator (DSO), has established a loss reduction program, which includes line reinforcement investments, among other actions. The main idea is to make the best investments in HV and MV network lines, considering the trade-off between benefits and costs. The ideal scenario would be, of course, to analyse all HV and MV networks and simulate possible reinforcement alternatives. However, the large number of MV feeders (about 4,000) makes this alternative unworkable. Thus, the first phase consists of developing a procedure to rank MV networks according to their potential to reduce losses. The highest scored networks are then analysed using a power system simulator. This analysis takes into account the different reinforcement alternatives and evaluates the investments costs and the saved energy over a period of 30 years - The economic time span usually considered by EDP Distribuição for this kind of operation. For the HV case, all networks were analysed. This paper synthetizes the main results obtained in these studies.

Abstract
The aim of this work is to demonstrate a novel fuzzy-based expert system for selecting renewable energy technologies (RET). Fuzzy multi-rules and fuzzy multi-sets are used to evaluate the main operational characteristics of six types of RET fuelled by biogas from municipal solid waste (MSW) landfills. The construction of the fuzzy multi-rules and fuzzy multi-sets is based on the following method: Mamdani controller using the Max-Min (inference process) and Center of Gravity (defuzzification process). Several criteria are used for the investigation: costs, efficiency, cogeneration, life-cycle and environmental impacts. The fuzzy-based expert system considers three different settings with two different constraints: costs and environmental impacts. One of the most relevant aspects presented by this work is about the previous criteria rank. 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.

Abstract
This paper presents numerical analysis of two alternative optimization approaches intended to support an EV aggregation agent in optimizing buying bids for the day-ahead electricity market. A study with market data from the Iberian electricity market is used for comparison and validation of the forecasting and optimization performance of the global and divided optimization approaches. The results show that evaluating the forecast quality separately from its impact in the optimization results is misleading, because a forecast with a low error might result in a higher cost than a forecast with higher error. Both bidding approaches were also compared with an inflexible EV load approach where the EV are not controlled by an aggregator and start charging when they plug-in. Results show that optimized bids allow a considerable cost reduction when compared to an inflexible load approach, and the computational performance of the algorithms satisfies the requirements for operational use by a future real EV aggregation agent.

Abstract
The Iberian power systems went through important changes at the legal, regulatory and organizational levels in the last 20 years. One of the most relevant ones was the increasing penetration of distributed generation, namely wind parks, together with the development of the common market involving Portugal and Spain. In Portugal, distributed generation is paid using feed in tariffs while in Spain it can choose between receiving a regulated feed in tariff or the market price plus a participation prize. The feed in scheme is now under discussion since it is argued that it represents an excessive cost that is internalized in the end user tariffs. However, this discussion is frequently conducted without complete knowledge of the real impact of wind power on the electricity market price, since it contributes to reduce the demand on the market thus inducing a price reduction. To clarify these issues we used a long term System Dynamics based model already reported in a previous publication to estimate the long term evolution of the market price. This model was applied to the Iberian generation system using different shares of wind power capacity to quantify the impact of wind power on the day-ahead electricity market price.