Unified Energy Agents as a Base for the Systematic Development of Future Energy Grids

Derksen, Christian; Linnenberg, Tobias; Unland, Rainer; Fay, Alexander

doi:10.1007/978-3-642-40776-5_21

Cited by 16 publications

(14 citation statements)

References 10 publications

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“…In particular, a power plant that is capable of regulating down to 50% of its maximum output may not want to have this information public in order to avoid having to run at this low level. On the other hand, such explicit models of the underlying technical system (including predictive aspects and dynamics) might simply not be available due to a low level of maturity [Derksen et al 2013]. Certainly, some agents might not be reticent to offer basic information such as the minimum and maximum contribution and can offer a control model to their superordinate.…”

Section: Creating Control Models Through Observationmentioning

confidence: 99%

Cooperative Resource Allocation in Open Systems of Systems

Anders

Schiendorfer

Siefert

et al. 2015

ACM Trans. Auton. Adapt. Syst.

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Resource allocation is a common problem in many technical systems. In multi-agent systems, the decentralized or regionalized solution of this problem usually requires the agents to cooperate due to their limited resources and knowledge. At the same time, if these systems are of large scale, scalability issues can be addressed by a self-organizing hierarchical system structure that enables problem decomposition and compartmentalization. In open systems, various uncertainties-introduced by the environment as well as the agents' possibly self-interested or even malicious behavior-have to be taken into account to be able to allocate the resources according to the actual demand.In this article, we present a trust-and cooperation-based algorithm that solves a dynamic resource allocation problem in open systems of systems. To measure and deal with uncertainties imposed by the environment and the agents at runtime, the algorithm uses the social concept of trust. In a hierarchical setting, we additionally show how agents create constraint models by learning the capabilities of subordinate agents if these are not able or willing to disclose this information. Throughout the article, the creation of power plant schedules in decentralized autonomous power management systems serves as a running example.

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Section: Creating Control Models Through Observationmentioning

confidence: 99%

Cooperative Resource Allocation in Open Systems of Systems

Anders

Schiendorfer

Siefert

et al. 2015

ACM Trans. Auton. Adapt. Syst.

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“…According to our classification in Derksen et al (2013), taking into account the above described problems and considering different operational phases of technical systems, our model currently distinguishes energy flows as follows:…”

Section: σ σmentioning

confidence: 99%

“…The subsequent Section 6 will present an exemplary application of our energy option model for a battery and a mCHP, while the article concludes with the discussion and a final conclusion in Sections 7 and 8. This article represents an extension of our previous publication on the MATES conference 2013 (Derksen et al, 2013).…”

Section: Introductionmentioning

confidence: 97%

Structure and classification of unified energy agents as a base for the systematic development of future energy grids

Derksen

Linnenberg

Unland

et al. 2015

Engineering Applications of Artificial Intelligence

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“…Here we introduced the notion of 'integration level' (IL) that describes the level of sophistication with respect to a scenario and thus also to the inherent complexity and the abilities of an energy agent [13]. In a rough classification, we differentiated between an IL0 that describes the initial construction state of the energy infrastructure for a time, where no decentralized computational entities could be found.…”

Section: Energy Agentsmentioning

confidence: 99%

The EOM: An Adaptive Energy Option, State and Assessment Model for Open Hybrid Energy Systems

Derksen

Unland

2016

Annals of Computer Science and Information Systems

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Abstract-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 onsite 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.

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Unified Energy Agents as a Base for the Systematic Development of Future Energy Grids

Cited by 16 publications

References 10 publications

Cooperative Resource Allocation in Open Systems of Systems

Cooperative Resource Allocation in Open Systems of Systems

Structure and classification of unified energy agents as a base for the systematic development of future energy grids

The EOM: An Adaptive Energy Option, State and Assessment Model for Open Hybrid Energy Systems

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