Abstract
Due to amount of today's electricity consumption, one of the most important tasks of the energy operators is to be able to predict the consumption and be ready to control the energy generation based on the estimated consumption for the future. In this way, having a trustable forecast of the electricity consumption is essential to control the consumption and maintain the balance in energy distribution networks. This study presents a day ahead forecasting approach based on a genetic fuzzy system for fuzzy rule learning based on the MOGUL methodology (GFS.FR.MOGUL). The proposed approach is used to forecast the electricity consumption of an office building in the following 24 hours. The goal of this work is to present a more reliable profile of the electricity consumption comparing to previous works. Therefore, this paper also includes the comparison of the results of day ahead forecasting using GFS.FR.MOGUL method against other fuzzy rule based methods, as well as a set of Artificial Neural Network (ANN) approaches. This comparison shows that using the GFS.FR.MOGUL forecasting method for day-ahead electricity consumption forecasting is able to estimate a more trustable value than the other approaches.

Abstract
Smart Grid technologies enable the intelligent integration and management of distributed energy resources. Also, the advanced communication and control capabilities in smart grids facilitate the active participation of aggregators at different levels in the available electricity markets. The portfolio optimization problem consists in finding the optimal bid allocation in the different available markets. In this scenario, the aggregator should be able to provide a solution within a timeframe. Therefore, the application of metaheuristic approaches is justified, since they have proven to be an effective tool to provide near-optimal solutions in acceptable execution times. Among the vast variety of metaheuristics available in the literature, Differential Evolution (DE) is arguably one of the most popular and successful evolutionary algorithms due to its simplicity and effectiveness. In this paper, the use of DE is analyzed for solving the portfolio optimization problem in electricity markets. Moreover, the performance of DE is compared with another powerful metaheuristic, the Particle Swarm Optimization (PSO), showing that despite both algorithms provide good results for the problem, DE overcomes PSO in terms of quality of the solutions.

Abstract

Abstract
This paper proposes a model based on particle swarm optimization to aid electricity markets players in the selection of the best player(s) to trade with, to maximize their bilateral contracts outcome. This approach is integrated in a Decision Support System (DSS) for the pre-negotiation of bilateral contracts, which provides a missing feature in the state-of-art, the possible opponents analysis. The DSS determines the best action of all the actions that the supported player can take, by applying a game theory approach. However, the analysis of all actions can easily become very time-consuming in large negotiation scenarios. The proposed approach aims to provide the DSS with an alternative method with the capability of reducing the execution time while keeping the results quality as much as possible. Both approaches are tested in a realistic case study where the supported player could take almost half a million different actions. The results show that the proposed methodology is able to provide optimal and near-optimal solutions with an huge execution time reduction. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
The use of energy from renewable sources is one of the major concerns of today’s society. In recent years, the European Union has been changing legislation and implementing policies aimed at promoting its investment and encouraging its use in order to reduce the emission of greenhouse gases [1]. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
The increasing penetration of distributed energy sources, mainly based on renewable generation, calls for an urgent emergence of novel advanced methods to deal with the associated problems. The consensus behind smart grids (SGs) as one of the most promising solutions for the massive integration of renewable energy sources in power systems has led to the development of several prototypes that aim at testing and validating SG methodologies. The urgent need to accommodate such resources require alternative solutions. This chapter presents a multi-agent based SG simulation platform connected to physical resources, so that realistic scenarios can be simulated. The SG simulator is also connected to the Multi-Agent Simulator of Competitive Electricity Markets, which provides a solid framework for the simulation of electricity markets. The cooperation between the two simulation platforms provides huge studying opportunities under different perspectives, resulting in an important contribution to the fields of transactive energy, electricity markets, and SGs. A case study is presented, showing the potentialities for interaction between players of the two ecosystems: a SG operator, which manages the internal resources of a SG, is able to participate in electricity market negotiations to trade the necessary amounts of power to fulfill the needs of SG consumers. © Springer International Publishing AG, part of Springer Nature 2018.