Demand response and distributed generation are key components of power systems. Several challenges are raised at both technical and business model levels for integration of those resources in smart grids and microgrids. The implementation of a distribution network as a test bed can be difficult and not cost-effective; using computational modeling is not sufficient for producing realistic results. Real-time simulation allows us to validate the business model's impact at the technical level. This paper comprises a platform supporting the real-time simulation of a microgrid connected to a larger distribution network. The implemented platform allows us to use both centralized and distributed energy resource management. Using an optimization model for the energy resource operation, a virtual power player manages all the available resources. Then, the simulation platform allows us to technically validate the actual implementation of the requested demand reduction in the scope of demand response programs. The case study has 33 buses, 220 consumers, and 68 distributed generators. It demonstrates the impact of demand response events, also performing resource management in the presence of an energy shortage.

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Smart grids concept benefits and leverage distributed management systems while allowing its players to actively participate in the smart grid. This paper merges the concepts of microgrid and transactive energy. The proposed model is tested in an office building with multiple tenants. An agentbased platform, running in single-board computers, for microgrid intelligent management with a peer-topeer energy transaction model is proposed in this paper. This paper describes the peer-to-peer transaction auction model and the deployment of the platform in an office building. The results regard a one-week period where the use of peer-to-peer transactions is compared with a scenario where no transactions among agents are performed. The results are promising, showing the energy price inside the microgrid dropping for the majority of players/agents. The presented work demonstrates how smart grid players can decrease their energy costs using simple approaches that do not require load shifting, consumption optimization nor the acquisition of new equipment.

The massive dissemination of smart devices in current markets provides innovative technologies that can be used in energy management systems. Particularly, smart plugs enable efficient remote monitoring and control capabilities of electrical resources at a low cost. However, smart plugs, besides their enabling capabilities, are not able to acquire and communicate information regarding the resource’s context. This paper proposes the EnAPlug, a new environmental awareness smart plug with knowledge capabilities concerning the context of where and how users utilize a controllable resource. This paper will focus on the abilities to learn and to share knowledge between different EnAPlugs. The EnAPlug is tested in two different case studies where user habits and consumption profiles are learned. A case study for distributed resource optimization is also shown, where a central heater is optimized according to the shared knowledge of five EnAPlugs.

Intelligent systems have a great potential for addressing decision-making problems, because they can model the involved players and produce good results with low computational time. However, intelligent systems are still globally underutilized due to their isolation from the outside world.