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.

Abstract

Abstract
Electricity markets are complex and dynamic environments, mostly due to the large scale integration of renewable energy sources in the system. Negotiation in these markets is a significant challenge, especially when considering negotiations at the local level (e.g., between buildings and distributed energy resources). It is essential for a negotiator to be able to identify the negotiation profile of the players with whom he is negotiating. If a negotiator knows these profiles, it is possible to adapt the negotiation strategy and get better results in a negotiation. In order to identify and define such negotiation profiles, a clustering process is proposed in this paper. The clustering process is performed using the kml-k-means algorithm, in which several negotiation approaches are evaluated in order to identify and define players' negotiation profiles. A case study is presented, using as input data, information from proposals made during a set of negotiations. Results show that the proposed approach is able to identify players' negotiation profiles used in bilateral negotiations in electricity markets.

Abstract
Case-based reasoning enables solving new problems using past experience, by reusing solutions for past problems. The simplicity of this technique has made it very popular in several domains. However, the use of this type of approach to support decisions in the power and energy domain is still rather unexplored, especially regarding the flexibility of consumption in buildings in response to recent environmental concerns and consequent governmental policies that envisage the increase of energy efficiency. In order to determine the amount of consumption reduction that should be applied in a building, this article proposes a methodology that adapts the past results of similar cases in order to achieve a decision for the new case. A clustering methodology is used to identify the most similar previous cases, and an expert system is developed to refine the final solution after the combination of the similar cases results. The proposed CBR methodology is evaluated using a set of real data from a residential building. Results prove the advantages of the proposed methodology, demonstrating its applicability to enhance house energy management systems by determining the amount of reduction that should be applied in each moment, thus allowing such systems to carry out the reduction through the different loads of the building.