## 1 Introduction  
Electricity Markets (EM) are complex environments that are constantly adapting to the needs of society. The liberalization of its segments, namely the production, transport and distribution of energy, allows several improvements such as quality of service, reduced costs and stimulation of market models' creation, which promote competitiveness [1; 2]. As a result, EM behavior has become more unpredictable and complex due to the large number of participating entities and possible interactions between them. Currently, EM are facing a new paradigm shift following the large-scale integration of Renewable Energy Sources (RES) [3; 4]. The significant use of fossil resources for energy production is one of the major concerns of today's society. Climate change, environmental impact and the scarcity of resources have led to the need for investment in RES, which, by reducing the emission of greenhouse gases, contributes to an increase in the quality of life and sustainable development [5]. The European Union (EU) has the target of 20-20-20 by 2020 [6; 7], which establishes measures that contribute to the change of legislation in the energy sector. This program aims to achieve three goals: (i) 20% reduction in CO\({}_{2}\) emissions by 2020 compared to 1990 levels; (ii) a 20% increase in energy efficiency; and (iii) 20% representation of RES in the energy consumption in the EU. The inclusion of RES in EM is a highly complex task. The network operator needs to balance supply and demand in order to maximize the available resources [8]. Due to the unpredictability of the behavior of RES, this balance is difficult to achieve. Thus, a careful analysis of the behavior of market entities, namely the consumption and production profiles, is required in real time. The large-scale implementation of Distributed Generation (DG) plays a key role in energy management and is associated with the concepts of the Smart Grid (SG) [9], microgrid [10], microgeneration [11], prosumer [12], community microgrids [13], virtual power plants [14], integrated community energy systems [15] and Demand Response (DR) [16]. The DG consists of the decentralized production of energy, which acts at the level of consumers. These can produce energy from RES and sell it to the grid. Thus, the consumer goes by the name of prosumer [17], which is a combination of consumer and producer. In DG systems, prosumers aggregate into small networks (microgrids) in order to facilitate energy scheduling within a smaller geographic area. According to [PERSON] in [18], \"DGs contribute in the application of competitive energy policies, diversification of energy resources, reduction of on-peak operating cost, deferral of network upgrades, lower losses, lower transmission, distribution costs, and potential increase of service quality to the end-customers\". In order to enable large-scale penetration of DG, it is necessary to upgrade energy transmission networks so that they have mechanisms that are capable of dealing with the unpredictability of RES and adjusting to the energy needs of the consumer. SGs respond to these needs, being equipped with ubiquitous technology that allows the control, processing and automation of the energy supply [4]. In addition, the installed sensors along the network allow the collection of a great diversity and quantity of data. These data can be used to feed simulators that provide decision support to EM entities, through artificial intelligence techniques, as a way of dealing with their increasing complexity. There is a number of benefits that the system operator can obtain from using simulated scenarios: discover failures in power supply and study solutions to make the network more efficient, reliable and secure; study and delineate the behavioral profiles of their users, so as to be able to forecast consumption and implement appropriate DR programs; design and test new market models so that EM can adapt to the new needs of their consumers; among others.