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.

Abstract
This paper describes an approach to the short term operation planning of hydro stations in market environment. The developed approach is based on the solution of an optimization problem to maximize the profit of a generation agent along a planning period discretized in hourly steps using a Genetic Algorithm. This problem includes the possibility of pumping since this is an important resource in the scope of electricity markets. The scheduling problem was developed starting with an initial simplified version in which the head loss is neglected and the head is assumed constant. Then, it was implemented a second model in which the nonlinear relation between the head, the hydro power and the water discharge is retained and finally an approach in which the hydro schedule obtained in a given step is used to update the hourly electricity prices used to compute the profit of the generation agent. The short term hydro scheduling problem is illustrated using two Case Studies - the first one was designed to run a set of initial tests to the developed algorithm and the second one refers to a set of hydro stations that mirrors a cascade of 8 stations in Portugal. © 2013 IEEE.

Abstract
This paper describes a multiyear dynamic Transmission Expansion Planning, TEP, model to select and schedule along the planning horizon transmission expansion projects taken from a list supplied by the planner. The selection of the most adequate set of projects from this list is driven by the minimization of the investment plus operation costs while enforcing a number of constraints related with technical, financial and reliability issues. The developed approach also admits that nodal loads are modeled by triangular fuzzy numbers as a way to ensure obtaining more robust plans that is plans not only adequate for a deterministic set of future loads but plans that can accommodate load uncertainty. Finally, given the discrete nature of the problem, it was adopted a discrete version of the Evolutionary Particle Swarm Optimization algorithm, DEPSO, that proved very effective and shows good performance on several tests ran with the IEEE RTS system.

Abstract
This paper presents a multiyear dynamic model to the Transmission Expansion Planning, TEP, problem to identify the most suitable set of projects as well as their scheduling along the planning horizon. The candidate plans are evaluated using a fitness function that incorporates operation and investment costs plus a set of penalty terms. These terms are associated with the level of losses, non-zero values for the power not supplied namely for the entire system and for n - 1 contingencies, financial limits, maximum number of projects to implement in each year or all along the horizon and the capability to accommodate not only the expected demand, but also uncertainties affecting the demand forecasts. Given the discrete nature of the problem, we adopted an enhanced approach of the PSO algorithm to solve it. This includes an evolutionary adaptation of the PSO movement rule as well as several modifications to ensure that along the iterative process each candidate solution is technically feasible given its discrete nature. The paper also reports the results of a set of tests to evaluate several design decisions related with the development of the Discrete Evolutionary PSO, DEPSO, as well as to compare the results of its application to the TEP with results reported by other researchers.