Abstract
Smart home devices currently available on the market can be used for remote monitoring and control. Energy management systems can take advantage of this and deploy solutions that can be implemented in our homes. One of the big enablers is smart plugs that allow the control of electrical resources while providing a retrofitting solution, hence avoiding the need for replacing the electrical devices. However, current so-called smart plugs lack the ability to understand the environment they are in, or the electrical appliance/resource they are controlling. This paper applies environment awareness smart plugs (EnAPlugs) able to provide enough data for energy management systems or act on its own, via a multi-agent approach. A case study is presented, which shows the application of the proposed approach in a house where 17 EnAPlugs are deployed. Results show the ability to shared knowledge and perform individual resource optimizations. This paper evidences that by integrating artificial intelligence on devices, energy advantages can be observed and used in favor of users, providing comfort and savings.

Abstract
The continuous changes in electricity markets' mechanisms and operations turn this environment into a challenging domain for the participating entities. Simulation tools are increasingly being used for decision support purposes of such entities. In particular, multi-agent based simulation, which facilitates the modeling of different types of mechanisms and players, is being fruitfully applied to the study of worldwide electricity markets. An effective decision support to market players' negotiations is, however, still not properly reached due to the uncertainty that results from the increasing penetration of renewable generation and the complexity of market mechanisms themselves. In this scope, this paper proposes a novel metalearner that provides decision support to market players in their negotiations. The proposed metalearner uses as input the output of several other market negotiation strategies, which are used to create a new, enhanced response. The final result is achieved through the combination and evolution of the strategies' learning results by applying a genetic algorithm. © 2015 IEEE.

Abstract
Worldwide electricity markets have been evolving into regional and even continental scales. The aim at an efficient use of renewable based generation in places where it exceeds the local needs is one of the main reasons. A reference case of this evolution is the European electricity market, where countries are connected, and several regional markets were created, each one grouping several countries, and supporting transactions of huge amounts of electrical energy. However, physical constraints, such as the grid limited capacity, are major setbacks, which make the full European market unification a more distant goal. This paper presents a study that aims at analyzing the capability of the existing European transmission network in accommodating a full unification of the European electricity markets. This study considers the real European transmission network capacities, supporting a unified Pan-European electricity market scenario, which is simulated using the Multi-Agent System for Competitive Electricity Markets.

Abstract
The increasing use of renewable energy sources and distributed generation brought several changes to the power system operation, with huge implications to the competitive electricity markets. With the eminent implementation of microgrids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a new type of player, which allows aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players benefits. The contribution of this paper is a clustering methodology regarding the remuneration and tariff of VPP. It proposes a model to implement fair and strategic remuneration and tariff methodologies, using a clustering algorithm, which creates sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data subgroups that brings the most added value for the decision making process is found, according to the players characteristics. The proposed clustering methodology has been tested in a real distribution network with 16 bus, including residential and commercial consumers, PV generation and storage units.

Abstract
This paper presents an enhanced simulation platform composed by the integration of two distinct multi-agent based simulators. The two simulators are: (i) the Multi-Agent Simulator of Competitive Electricity Markets (MASCEM), which provides a simulation platform for electricity markets participation, considering scenarios based on real data from several distinct market operators; and (ii) the Multi-Agent Smart Grid Platform (MASGriP), which facilitates the simulation of smart grids and microgrids, by modeling the power network at the distribution level, and representing the main entities that act in this scope. With the cooperation between the two simulation platforms, huge studying opportunities under different perspectives are provided, resulting in an important contribution in the fields of transactive energy, electricity markets, and smart grids. A case study is presented, showing the potentialities for interaction between players of the two ecosystems, namely by demonstrating a case in which a smart grid operator, which manages the internal resources of a smart grid, is able to participate in electricity market negotiations to sell the surplus of generation in some periods of the day, and buy the necessary power to satisfy the demand of smart grid consumers in other periods of the day. © 2015 IEEE.

Abstract
Energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and of massive electric vehicle is envisaged. The present paper proposes a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and Vehicle-to-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with their owners. It takes into account these contracts, the users' requirements subjected to the VPP, and several discharge price steps. The full AC power flow calculation included in the model takes into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33-bus distribution network and V2G is used to illustrate the good performance of the proposed method. © 2015 IEEE.