Energy management in distribution grids is one of the key challenges that needs to be overcome to increase the share of fluctuating renewable energies. Current control systems for energy management mainly demonstrate centralized‐ or decentralized‐hierarchical control structures. Very few systems manifest a fully decentralized multiagent‐based control structure. Multiagent‐based control systems promise to be an advantageous approach for the future distributed energy supply system because no central control entity is necessary, which eases parameterization in case of grid topology changes, and the agents are more stable against failures and changes of control topologies. Research is necessary to prove these benefits. In this study, we introduce a design of a multiagent‐based voltage control system for low‐voltage grids. In detail we introduce cooperative decision‐making processes and software solutions that allow the agents to perceive and control their environment, the agent‐discovery and localization in different types of communication networks, agent‐to‐agent communication, and the integration of the multiagent system in existing grid‐control infrastructures. Furthermore, the study proposes how different existing technologies can be combined into an applicable multiagent‐based voltage control system: the Java/OSGi‐based OpenMUC framework allows a generic field–device interaction; peer‐to‐peer discovery and session establishment functionalities are combined with the agent communication defined by the Foundation for Intelligent Physical Agents (FIPA). The ripple control‐signal technology is applied as a fallback communication between the agent and a central grid‐control center.

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The paper proposes a system architecture for charging infrastructure that serves the requirements of future fleets of shared-use electric vehicles in urban scenarios. The focus of the development is on the interfaces to central stakeholders such as mobility service providers, distribution network operators and utilities. The main concept of the proposed system is the adherence to a stringent resource-oriented design approach, following the design principles of the Representational State Transfer (REST) architectural software style for distributed systems. This design approach is used from the cloud-based services down to the implementation of the charging infrastructure's control algorithms. Focusing on the resources of the various entities simplifies the implementation of their interactions, compared to the explicit declaration of services that are available. The system design ensures that the charging infrastructure is open to all users and generates a benefit beyond basic charging operations. Integration in emerging smart markets is done via open web-based interfaces. These allow for the generation of an added value of concrete services for shared-use electric mobility. A link to the field of grid operation is proposed using the ISO/IEC 61850 telecontrol standard. The smart meter capabilities of the charging stations can be used to gain additional information on the current state of the distribution grid. As an exemplary service a load management service for a fleet of shared-use electric vehicles is going to be implemented

Multi-agent systems are a subject of continuously increasing interest in applied technical sciences. Smart grids are one evolving field of application. Numerous smart grid projects with various interpretations of multi-agent systems as new control concept arose in the last decade. Although several theoretical definitions of the term ?agent? exist, there is a lack of practical understanding that might be improved by clearly distinguishing the agent technologies from other state-of-the-art control technologies. In this paper we clarify the differences between controllers, optimizers, learning systems, and agents. Further, we review most recent smart grid projects, and contrast their interpretations with our understanding of agents and multi-agent systems. We point out that multi-agent systems applied in the smart grid can add value when they are understood as fully distributed networks of control entities embedded in dynamic grid environments; able to operate in a cooperative manner and to automatically (re-)configure themselves.