Abstract
The penetration of micro-generation brings complex problems to the energy field. In this way, various simulators were designed to give decision support for the stakeholders, however, they intent to solve very specific problems. The proposed tool enables the interoperability between heterogeneous simulators, to simulate more complex problems.

Abstract
One of the main challenges in power & energy systems is the development of decision support tools which approach the problem as a whole. In this scope, this work contributes to the increase of the interoperability between heterogeneous agent based systems through the use of ontologies, enabling semantic communications.

Abstract
Electricity markets are not only a new reality but also a constantly evolving sector, due to the high frequency of changes in their rules. Simulation tools combined with Artificial Intelligence techniques, particularly multi-agent simulation, can result in a sophisticated and very useful tool in this context.

Abstract
This work proposes a organization-based Multi-Agent System that models Local Electricity Market (MASLEM). A bottom-up approach is implemented to manage energy in this work. In this context, agents are able to connect to each other and the power grid to transact electrical energy, and manage their inside electrical energy independently. A Demand Response Program (DRP) based on Indirect Load Control (ILC) method is also used. The performance of our work is evaluated through an Agent Based Modeling (ABM) implementation.

Abstract
Currently, it is possible to find various tools to deal with the unpredictability of electricity markets. However, they mainly focus on spot markets, disfavouring bilateral negotiations. A multi-agent decision support tool is proposed that addresses the identified gap, supporting players in the pre-negotiation and actual negotiation phases.

Abstract
This paper proposes a Building Energy Management System (BEMS) as part of an organization-based Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed BEMS consists of an Energy Management System (EMS) and a Prediction Engine (PE). The considered Smart Home Electricity System (SHES) consists of different agents, each with different tasks in the system. In this context, smart homes are able to connect to the power grid to sell/buy electrical energy to/from the Local Electricity Market (LEM), and manage electrical energy inside of the smart home. Moreover, a Modified Stochastic Predicted Bands (MSPB) interval optimization method is used to model the uncertainty in the Building Energy Management (BEM) problem. A demand response program (DRP) based on time of use (TOU) rate is also used. The performance of the proposed BEMS is evaluated using a JADE implementation of the proposed organization-based MASHES.