## 2 Power and Energy Systems Simulation  
Decision Support Systems (DSS) are widely used in very different applications such as e-commerce [20], bank clients' management [21], pricing negotiation [22], etc. Power and energy systems' decision support through simulation is increasingly gaining visibility due to the high uncertainty surrounding this field. Thus, DSSs have several advantages in the study of this field: understanding how different market mechanisms can affect sellers and buyers of energy; making a forecast of the network's consumption for a certain instant, to understand the appropriate scheduling of energy; exploring new possibilities for alternative market models; deepening the understanding of how DR will affect consumers; what DR tariffs are most appropriate for certain consumer profiles; network planning, etc. It is increasingly common to design energy and power systems under a multi-agent architecture, because their distributed nature allows them to deal with the complexity of the system [23]. In addition, each agent can represent an entity with its own desires, constraints, autonomy and flexibility. This way, an agent can represent a market participant, a network component or a system. On the other hand, the simulation allows the study of future changes to the real systems and their behavior, reducing the costs and time needed to perform upgrades. Some examples of multi-agent simulators developed to study this field are:  
* The Electricity Market Complex Adaptive System (EMCAS) [24]: developed to model the EM, enabling the investigation of the physical infrastructure and the economic behavior of market participants;
* The Agent-based Modeling of Electricity Systems (AMES) [25]: allows the study of wholesale EM operation and their participants' strategies over the time;
* The Multi-Agent Negotiation and Risk Management in Electricity Markets (MAN-REM) [26]: devoted to risk management in EM's bilateral contracting;
* The simulation of power generation company's behavior in electricity markets [27]: investigates the strategic behavior of power generation companies under different market-clearing mechanisms;
* The Multi-Agent Smart Grid Simulation Platform (MASGriP) [28]: designed to consider real characteristics and goals of consumers, producers, prosumers and Virtual Power Players (aggregators of small players) in SG and to allow the integration with the Multi-Agent Simulator for Competitive Electricity Markets (MASCEM);
* The Power Trading Agent Competition (Power TAC) [29]: is a competition from the perspective of brokers (energy retailers, commercial or municipal utilities, or cooperatives) in order to simulate future models and market dynamics;
* Smart Grids Information and Communication (SGiC) [30]: incorporates different incentive programs such as VPP and DR, provides support to customers and enables power routing and balancing;* Building performance simulation [31]: uses regression surrogate models to simulate energy usage profiles of the building's occupants and facility managers, estimate energy consumption and help quantify the impact of uncertainty, in human actions on the performance of the built environment;
* Simulation of electricity consumption of an office room [32]: considers different factors of impact on energy saving;
* Cooperative control of energy storage units in the microgrid [33]: manages the charging/discharging behavior of multiple storage units;
* Simulation of electric vehicle driver behavior in road transport and electric power networks [34]: developed to fully understand the consequences of electrical vehicles' integration, the behavior of the drivers and its impact on road transport and electric power networks;
* Others.