Abstract

Abstract
The energy sector restructuring process in industrialized countries had the aim of reducing electricity prices by increasing competitiveness, and facilitate the integration of distributed energy resources. However, the complexity in market players' interactions has increased, and new problems have emerged. Decision support tools that facilitate the study and comprehension of these markets became extremely useful, providing players with competitive advantage. MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) arises in this context, modeling and simulating real electricity markets. It is crucial to MASCEM to have the ability to simulate as many market models and player types as possible, thus enhancing the ability to recreate the electricity markets reality in its maximum possible extent. This paper presents the EPEX Spot Day-Ahead market integration in MASCEM. EPEX Spot SE's mission is to lead European markets coupling in a single unified market, thus being crucial for the study of competitive electricity markets. © 2015 IEEE.

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.