Das Projekt Agent.Hygrid
Hier finden Sie relevante Veröffentlichungen der Projektpartner aus der aktiven Projektlaufzeit, sowie entsprechende Vorarbeiten.
NEIS 2017 - An Algorithm for the Temporary Acquisition of Control over Third Party Assets in Active Network Management
NEIS Conference 2017, Hamburg
Sebastian Törsleff, Tobias Linnenberg, Erik Wassermann, Alexander Fay, Institute for Automation Technology, Helmut-Schmidt-University, Hamburg, Germany
Christian Derksen, Nils Loose, Chair for Data Management Systems and Knowledge Representation, University Duisburg-Essen, Essen, Germany
Marcel Ludwig, Institute of Power Systems Engineering, University of Wuppertal, Wuppertal, Germany
Active network management (ANM) is a promising approach to cope with the proliferation of distributed generation from renewables. The distribution system operator (DSO) can utilize its own assets in this regard, e.g. by installing remotely controllable on load tap changer transformers. Additionally, the DSO can incorporate distributed energy resources from third parties into its active network management. This, however, involves specific challenges such as dealing with temporary unavailabilities of these systems and ensuring a smooth transition between autonomous control and control by the DSO. This contribution presents an extended ANM control algorithm that addresses these challenges.
Erasmus Energy Forum 2017 - Unified Energy Agents in Simulations, Testbeds and Real Systems
Erasmus Energy Forum 2017, Rotterdam, Science Day
Nils Loose, Christian Derksen, Rainer Unland
The liberalization of the energy markets, together with the ongoing energy transition, require new solutions for a flexible and adaptable energy supply infrastructure. The smart grid, i.e. equipping existing energy grids with modern information and communication technology, is regarded as a key enabler to handle this challenge. With the concepts of Unified Energy Agents and the Energy Option Model, a generic approach for a cross-domain and vendor-neutral smart grid architecture based on multi-agent systems has been proposed. In this work, the development process of an Energy Agent is described, with a special focus on the testing and evaluation phase, and the current state of its implementation is outlined.
PAAMS 2017 - Hardware Integration and Real-Time Control in an Agent-Based Distribution Grid Simulation
Loose N., Törsleff S., Derksen C., Unland R., Fay A. (2017) Hardware Integration and Real-Time Control in an Agent-Based Distribution Grid Simulation. In: Demazeau Y., Davidsson P., Bajo J., Vale Z. (eds) Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection. PAAMS 2017. Lecture Notes in Computer Science, vol 10349. Springer, Cham
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.
SmartER Europe 2017 - Testbed Application of Energy Agents
N. Loose, C. Derksen and R. Unland, “Testbed Application of Energy Agents”, SmartER Europe Conference 2017, Essen, 2017
This work introduces the concept of testbed application of energy agents, with 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.
at – Automatisierungstechnik - Einheitliches und durchgängiges Engineering von Steuerungslösungen für hybride Energiesysteme und -netze mittels Energie-Agenten
E. Wassermann, T. Linnenberg, S. Törsleff, A. Fay, C. Derksen, N. Loose, R. Unland, M. Ludwig, M. Stötzel, M. Zdrallek, W. Heldmaier: „Einheitliches und durchgängiges Engineering von Steuerungslösungen für hybride Energiesysteme und -netze mittels Energie-Agenten“, at – Automatisierungstechnik 2017; 65(1): 60–72
Zusammenfassung: Im Rahmen des vom BMWi geförderten Projekts Agent.HyGrid werden vereinheitlichte Energie-Agenten als Steuerungslösung für hybride Energiesysteme und -netze entwickelt und auf ihre Anwendbarkeit hin untersucht. Hierbei werden sowohl vereinheitlichte Daten- und Verhaltensmodelle entwickelt als auch ein Referenz- und Entwicklungsprozess entworfen. Mit diesem durchgehenden Entwicklungsprozess soll die Anwendung der Energie-Agenten, angefangen von der Planungs- und Simulationsphase bis hin zum realen Einsatz im physischen Vor-Ort-System, ermöglicht werden. Als Energie-Agent wird dabei grundsätzlich ein autonomes, dezentral operierendes Software-System verstanden, das unabhängig von der Sparte bzw. vom Energieträger auf mehreren Ebenen eines Energie-Verteilnetzes eingesetzt werden kann. Durch die Kombination unterschiedlicher Energieträger sollen insbesondere volatile Energieerzeugungsanlagen, wie Windkraft- und Solaranlagen, besser ins Energienetz integriert werden. Die Herausforderungen des Projekts liegen vor allem in der vereinheitlichten Modellierung unterschiedlicher Energieträger und -umwandlungsanlagen sowie in der Überbrückung der Lücke zwischen Simulation und der realen Anwendung vor Ort. Dieser Artikel stellt die bisherigen Erkenntnisse in Hinblick auf die Anwendung des Entwicklungsprozesses und des einheitlichen Datenmodells dar.
atp edition - Verteilte Automatisierung hybrider Energiesysteme
S. Törsleff, C. Derksen, A. Fay, W. Heldmaier, T. Linnenberg, N. Loose, M. Ludwig, M. Stötzel, R. Unland, E. Wassermann, M. Zdrallek: Verteilte Automatisierung hybrider Energiesysteme – Agentensysteme für eine dezentrale Energieversorgung. In: atp edition, Bd. 58, Ausg. 11, S. 55-64, 2016.
Im Forschungsprojekt Agent.HyGrid wird eine verteilte Automatisierungslösung für den Einsatz in hybriden Energiesystemen implementiert. Hiermit wird dem steigenden Anteil regenerativer Erzeugung sowie der Dezentralisierung der Energieversorgung Rechnung getragen, die die Praktikabilität zentral ausgelegter Architekturen mindert. Eines der Kernelemente ist der Einsatz eines Agentensystems, dessen Individuen – die Energieagenten – die Steuerung der Systemelemente verantworten. Somit kann die Automatisierungslösung den bestehenden hohen Ansprüchen an Reaktionsgeschwindigkeit und Ausfallsicherheit gerecht werden, zugleich aber eine höhere Flexibilität als zentrale Architekturen erreichen, da die Energieagenten zur Laufzeit dynamisch koalieren können. Die Energieagenten werden hierzu mit detaillierten Modellen der zu steuernden technischen Systeme ausgestattet, wodurch sie zur simulationsbasierten Betriebsoptimierung auf lokaler Ebene befähigt werden. Durch die Integration unterschiedlicher Energieträger zu Hybridnetzen wird darüber hinaus der Weg für die synergieträchtige Kopplung der Sektoren Strom, Wärme und Verkehr geebnet.
FedCSIS 2016 - The EOM: An Adaptive Energy Option, State and Assessment Model for Open Hybrid Energy Systems
C. Derksen and R. Unland, „The EOM: An adaptive energy option, state and assessment model for open hybrid energy systems,“ 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, 2016, pp. 1507-1515.
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.
PAAMS 2016 - Evaluation of Aggregated Systems in Smart Grids: An Example Use-Case for the Energy Option Model
Loose N., Nurdin Y., Ghorbani S., Derksen C., Unland R. (2016) Evaluation of Aggregated Systems in Smart Grids: An Example Use-Case for the Energy Option Model. In: Bajo J. et al. (eds) Highlights of Practical Applications of Scalable Multi-Agent Systems. The PAAMS Collection. PAAMS 2016. Communications in Computer and Information Science, vol 616. Springer, Cham
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.
IEEE EnergyCon - Design, implementation and testing of multi- energy infrastructures the multi agent way in Agent.HyGrid
Abstract— Due to a high penetration by renewables and a strong trend towards decentralization, future energy grids exhibit an increasingly emergent behavior. As a consequence new engineering approaches, based on decentralized decision making, have been proposed. The Agent.HyGrid project provides a framework for the design, development and testing of energy infrastructures. However, in contrast to other approaches it allows the designer to consider all kinds of energy, not only electrical power but also, e.g., gas or heat. It was shown in small-scale scenarios, that the developed software artifacts were reusable in on-site systems. This avoids the redundancy, which is otherwise created by re-implementing the software for different systems. By closing the gap between simulation environments, hybrid hard- and software test-beds and real on- site applications, the Agent.HyGrid project intends to demonstrate that such systematic and seamless software development processes and the according tool chain are already viable today.
Industrial Agents: Emerging Applications of Software Agents in Industry - Cross-Domain energy savings by means of unified energy agents
T. Linnenberg, C. Derksen, A. Fay, R. Unland: Cross-Domain energy savings by means of unified energy agents. In: P. Leitao (Hrsg.): Industrial Agent: Emerging Applications of Software Agents in Industry. Morgan Kaufman Publ Inc, ISBN: 978-0128003411 (Print) 2015 , S. 247-268.
Facing the impact of global climate change as well as the economic risks coming along with the scarcity of carbon based natural resources like coal, oil or natural gas, an ever growing number of states and companies is rethinking their processes and energy usage patterns (Organization for Economic Co- operation and Development 2013; United Nations Environment Programme 2013). Apart from the provision of electrical energy by means of renewable energy sources like wind, sun or water power, other forms of energy may be provided by nature as well. Most prominently used for several centuries are different forms of solar water heating or the ever growing utilization of volcanic heat for heat and electricity generation.
In modern infrastructures the energy demand for room heating and cooling still surpasses all other energy needs like fresh water heating, cooking or lighting applications (UK Department of Energy & Climate Change 2013a). For illustrative reasons Figure 1 shows the energy demand of an average European household and a modern office building. This comprises the electrical energy demand as well as all caloric energy needs, the infrastructure and its inhabitants may have. The resulting energy usage over time can be synthesized by means of aggregation to so called standard load profiles. Some distinctive peaks are observable throughout the day. They are related to the generation of thermal energy when showering in the morning, cooking at noon and in the evening.
As thermal loads – especially the provisioning of hot water for all kinds of grooming activities – may be operated in a certain temperature-range it is possible to shift the generation of heat or cold on the timeline according to some very limited external factors. Furthermore the energy source is not predefined. – This means that it is irrelevant whether the water is heated up by solar energy, gas or electricity. These facts result in two degrees of freedom, allowing to save scarce energy sources by shifting to another form of energy on the one hand and temporal adaption to a fluctuating energy provision pattern from renewable energy sources on the other. By coupling different energy networks like electricity, gas or heat grids, the peak demand in single energy domains may thus be clipped by using storage effects offered in the interconnected infrastructures.
Electrical energy generation is shifting away from large scale centralized coal and nuclear power plants towards small and medium-sized distributed power generators, often based on fluctuating energy sources like wind and sun. Due to the rise in complexity coming along with this development the controllability and manageability of the electricity grid becomes a real issue. Situations in which the stability of the grid may be endangered, will either lead to a short- time reduction in decentralized power generation or make it necessary to store the superfluous energy. Different national and international initiatives like the e-energy program of the German Federal Ministry of Economics and Technology or the European Union’s Strategic Energy Technology Plan have addressed this point with a strong focus on electrical energy (European Commission 2010; VDE 2013). Several mid-size to large-scale research projects including real life implementations were funded in this context. The Model Region Harz featuring a virtual power plant made up of different distributed renewable energy plants (Speckmann et al. 2011) or the E-DeMa project enabling a market based demand side management (Belitz et al. 2012) shall be mentioned in this context.
SmartER Europe - Agent.HyGrid: A seamless Development Process for agent- based Control Solutions in hybrid Energy Infrastructures
Decisions regarding design, implementation and needed policies for decentralized control solutions of future energy grids require profound investigations that ensure a secure and reliable grid operation. This applies to scientific computational systems serving the development of new control approaches on the one hand, but also for test-bed frameworks being used for the final verification of required functionalities before hard- and software components are deployed in real applications on the other hand. In an ideal case, the developed software artefacts are reused in various on-site systems for different purposes, which would avoid a redundant work overhead. By closing the gap between simulation environments, test-beds and real on- site applications, the Agent.HyGrid project intends to demonstrate that such systematic and seamless software development process is practicable. Based on the definition and the unifying concept of so called “Energy Agents”, a reference development process is defined. It shall be used as a blueprint for further developments of decentralized, agent-based control solutions.
MATES - Unified Energy Agents as a Base for the Systematic Development of Future Energy Grids
C. Derksen, T. Linnenberg, R. Unland, A. Fay: Unified Energy Agents as a Base for the Systematic Development of Future Energy Grids. In: 11th Conference on Multiagent System Technologies / SEN-MAS 2013 – Smart Energy Networks & Multi-Agent Systems, Koblenz, 16.-20. September 2013.
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 mi- sunderstandings and to the fact that agents are meant and utilized for very dif- ferent 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 discus- sions and the conversion of the energy supply. Considering a development cycle that consists of modeling and implementation, simulation, test-bed appli- cation 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. Fur- ther, we present a classification of Energy Agents according to their sophistica- tion and integration level and outline the need for individual but standardized energetic option models.