Abstract
Agent-based simulation tools have found many applications in the field of Power and Energy Systems, as they can model and analyze the complex synergies of dynamic and continuously evolving systems. While some studies have been done w.r.t. simulation and decision support for electricity markets and smart grids, there is still a generalized limitation referring to the significant lack of interoperability between independently developed systems, hindering the task of addressing all the relevant existing interrelationships. This work presents the Semantic Services Catalog (SSC), developed and implemented for the automatic registry, discovery, composition, and invocation of web and agent-based services. By adding a semantic layer to the description of different types of services, this tool supports the interaction between heterogeneous multiagent systems and web services with distinct capabilities that complement each other. The case study confirms the applicability of the developed work, wherein multiple simulation and decision-support tools work together managing a microgrid of residential and office buildings. Using SSC, besides discovering each other, agents also learn about the ontologies and languages to use to communicate with each other effectively.

Abstract
This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimization techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper details some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study based on real data. © 2009 IEEE.

Abstract
Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents' behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents. © 2011 IEEE.

Abstract
This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. © 2011 IEEE.

Abstract
The restructuring that the energy sector has suffered in industrialized countries originated a greater complexity in market players' interactions, and thus new problems and issues to be addressed. Decision support tools that facilitate the study and understanding of these markets become extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent system for simulating competitive electricity markets. To provide MASCEM with the capacity to recreate the electricity markets reality in the fullest possible extent, it is essential to make it able to simulate as many market models and player types as possible. This paper presents the development of the Complex Market in MASCEM. This module is fundamental to study competitive electricity markets, as it exhibits different characteristics from the already implemented market types. © 2011 IEEE.

Abstract
Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players' strategies to negotiate in the market. The proposed methodology is multiagent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. © 2011 IEEE.