A sustainable and climate-friendly energy supply needs flexible and efficient distribution systems. Key factors to implement this kind of systems are intelligent coordination (smart grid approaches) and the integration of different energy sectors. This article introduces the unified energy agent as an agent-based approach for a comprehensive modelling and control of energy conversion systems. This approach enables both the simulation and optimization of coupled energy networks, and then in a next step, the development of corresponding smart grid solutions to be applied in the field. Its applicability for the simulation of coupled networks is presented by a real-world use-case of an innovative combined heat and electrical network, which was implemented for the city of Lemgo, Germany. Preliminary results from the project are discussed and an outlook on future work is given.

In this contribution, the experiences and results from the tests under real field conditions are described, which are concerned with grid automation systems in the research project Agent.HyGrid. Furthermore, the deployment of the grid automation system in a real-world distribution grid is discussed.
 

This work introduces the concept of testbed application of energy agents, which is the intermediate step between testing agents in pure simulation environment and deploying them in real energy distribution systems. In the testbed application case, the energy agent is taken from the simulation environment and deployed to dedicated hardware, where it controls a simulated or real technical system, while still working against a simulated environment. Compared to a pure simulation environment, this application case raises a number of new challenges, mainly resulting from inter-platform agent communication. In this work these challenges are discussed and an implementation handling them is presented and evaluated.
 

This volume consists of revised selected papers presented at the 3rd and 4th International Conference on Smart Energy Research, SmartER Europe 2016 and 2017, held in Essen, Germany, in February 2016 and 2017. The 13 full papers included in this volume were carefully reviewed and selected from 25 submissions. The papers discuss recent advances and experiences in building and using new IT-based solutions for Smart Grids and Smart Markets combining the knowledge of different disciplines such as engineering, business management and economics as well as computer science. They reflect the versatility and the complexity of the transformation process in the energy sector and also show the great need for research that is required to achieve the high targets for a digitized and sustainable energy landscape.
 

The current transformation process of how energy is supplied attracts great interest from many different market players. As a consequence, many proprietary solutions for ``smart'' energy applications are flooding the market. This turns out to be rather a problem than part of the solution for the systematic development of future energy grids. Additionally, the absence of necessary standards blocks further developments that enable the creation of novel, market-driven and hybrid control solutions. To overcome these problems, we suggest a standardized control approach for hybrid energy systems by means of a so called Energy Option Model (EOM). This unifying model and the therewith developed decision support system provides the necessary technical understanding and the economic assessment options for network-connected energy conversion systems. Thus, it can be used for single on-site systems as well as for aggregated systems that are controlled in centralized or decentralized manner. This paper presents and discusses exemplary use cases for our EOM that illustrate the centralized as well as the decentralized use of our approach within hybrid energy systems. Overall, we believe that the EOM represents the key approach for a further systematic development of an open hybrid energy grid.

In recent years, several developments in the energy sector have been imposing major challenges on our energy supply infrastructure. Due to the liberalization of the energy markets that started in the 1990s, longstanding monopolies are being broken up and new actors enter the stage. An increasing awareness regarding the environmental impacts of fossil fuel-based electricity generation put renewable energy sources like wind and solar on a lasting growth path. The volatility inherent to these sources and the shift from centralized to decentralized generation necessitate new approaches as to how energy is marketed, distributed and consumed. The smart grid, i.e. equipping the energy infrastructure with modern information and communication technology, is widely considered essential in addressing the challenges outlined.
 

As a result of fast growing share of renewable energy production in the energy market the management of power and its distribution becomes more and more complex. The here presented Energy Option Model (EOM) seems to be a promising solution to handle this newly arisen complexity. This paper will present the EOM and analyze its capabilities in centralized evaluation of aggregated systems. The example use-case will be the charging process of a fleet of electric vehicles. While the results support the potential of the EOM to implement coordination strategies for aggregations of systems, they also show the general limitations of centralized control solutions for larger groups of systems in the context of smart grids.

Adaptation models; Agent.HyGrid project; Computational modeling; Context; Control Engineering; decentralized decision making; electrical power; energy markets; future energy grids exhibit; hybrid hardware test-beds; hybrid power systems; hybrid software test-beds; multiagent systems; multi-agent systems; multienergy infrastructure testing; power engineering computing; power markets; program testing; Simulation; Software; software artifacts; software development processes; software reusability; Systematics; Testing

The ongoing conversion of our energy supply encounters a great interest of many different market players that were originally located in different industries. As a consequence, a vast amount of proprietary solutions for “smart” energy applications is flooding the market. This tends to be rather a problem than part of the solution for the systematic development of future energy grids. Here, the absence of necessary unifications and standards blocks further developments that would enable the creation of novel, market-driven and hybrid control solutions for various types of technical systems. To overcome these problems, we present in this article our notion and the definition of a unified autonomous software entity that we call Energy Agent. Based on the energy conservation law and a generalized energy option model, we claim that our Energy Agent approach has the capabilities to enable cross domain interactions between different types of energy systems and networks. Further we will outline a systematic development process for Energy Agents that considers implementation, simulation, test-bed application and a real on-site usage. By taking into account these development stages, we expect to concurrently develop a novel laboratory that enables to competitively test and validate new and hybrid control solutions before they are applied in real systems.
 

This chapter presents a novel approach to dynamic resource allocation in hybrid energy grid scenarios, called energy-agents. This concept allows the easy control of inter-domain energy exchanges, such as heat generation from electricity or gas, the production of gas from electricity, or vice versa, based on the first law of thermodynamics. In a first hardware and software implementation, we were able to showcase the functionality and usability of this approach, enabling the developers of smart grid solutions to use the same code throughout the entire development process. This reusability will help to keep down development time and costs. On top of this, we were able to realize energy and cost savings by dynamically allocating the energy sources, as required by the consumer processes.
 

The need for the application of software agents and agent-technologies in highly diversified future energy grids is widely accepted today. Nevertheless, the very general concept of the agent paradigm still leads to misunderstandings and to the fact that agents are meant and utilized for very different tasks. Accordingly, the approaches that were presented in the Smart Gird area have major weaknesses in terms of comparability and a subsequently large-scale use. We claim that the introduction of a unified definition of an Energy Agent will help to create a coherent picture that can accelerate further discussions and the conversion of the energy supply. Considering a development cycle that consists of modeling and implementation, simulation, test-bed application and the deployment to real systems, we present here our definition of an Energy Agent that takes into account the law of conservation of energy. Further, we present a classification of Energy Agents according to their sophistication and integration level and outline the need for individual but standardized energetic option models.

Das Paradigma des Agenten findet zunehmend Anwendung in hochdynamischen und komplexen Bereichen, welche koordinierte oder koordinierende Prozesse erfordern. In diesem Beitrag werden neue Anforderungen an die Systeme der Energieversorgung und des Netzbetriebes vorgestellt und diskutiert, inwieweit das Agenten-Paradigma diesen gerecht werden kann.

The conversion of our energy supply systems is practically just at the beginning. Technically distributed, hybrid and individual energy producer and consumer have to be coupled to form individual small(er) smart markets. Thus, it can be expected that the interactions and dependencies between technical and economical systems will increase. A sound understanding of the limits in the interaction between smart grids and smart markets will be a key point for the further development of energy grids. This paper argues that agents, multi-agent system and agent based simulations are a promising approach to deal with many of the new challenges connected to smart grids. For the time being appropriate, standardized simulation tools for the new and maybe hybrid smart energy systems are not yet available. However, their existence would accelerate the development of intelligent energy networks substantially.

In many countries, future energy supply, management, and consumption change completely compared to today’s status. One reason is that sustainable energy production will rely on a large number of small and medium sized, de-centralized energy production units. The aim is to replace a small number of large power plants. The energy supply strategy change puts completely different demands on the energy grid as well as on the business models to run those energy grids efficiently and, especially, securely. The future energy grid will have to deal with an autonomous, communication-driven, highly diversified and dynamic, and continuously changing environment. Additionally, while today’s energy grids are run as independent systems the interaction between different energy networks, like natural gas or district heat, will strongly increase in the future. Inspired by ideas and techniques like Mini-CHP (combined heat and power) or Power-to-Gas, it is expected that future energy networks have to be seen as an entire hybrid energy network, where quantities of energy and energy forms will be dynamically exchanged and substituted as required. As a consequence, a lot of expertise that was gained in the past is no longer valid. On the other hand, little experience exists when it comes to shaping and managing the future energy grid. Due to the nature of multi-agent based simulation this class of simulation tools is well-suited for the simulation of future energy networks. The main advantages are the autonomy of agents, their highly developed interaction skills, the inherent distributed/decentralized approach of control in such a system and the high flexibility in the execution of tasks. This article presents Agent.GUI, which can be seen as a simulation toolkit and framework for complex and diversified energy systems. Based on the well-known JADE platform, the toolkit provides a wealth of specific features tailored to the needs for simulation of hybrid energy networks.

The objective of service-oriented computing (SOC) is to construct software applications out of appropriate services available and executing in place anywhere across the Web. To achieve this objective requires that techniques for discovering and engaging services be developed and used across the lifetime of the service-based applications. Succeeding with SOC in this broader sense presupposes that additional techniques be developed for ensuring desired quality of service metrics and service-level agreements. The crucial aspect of using services is thus their governance. In this paper, we describe a conceptual modelling approach that integrates organisational and coordination theories to achieve contextualised service governance. The approach allows for the development and analysis of dynamic, flexible, and robust service-oriented business applications.