Sebastian Lehnhoff

Market-based self-organized provision of active power and ancillary services: An agent-based approach for Smart Distribution Grids

Transforming the existing power generation to renewable, distributed generation implicates an increase in complexity for the control of the overall system. We propose a distributed control method to launch products of self-organized coalitions of small active units in a power grid at markets for trading active power as well as ancillary services. Our concept combines the integration of grid restrictions into proactive scheduling of active power with provision of ancillary services, and additionally provides reactive scheduling of active power, e.g. in the case of ancillary service activation.

Smart Grid Architecture Model use case management in a large European Smart Grid project

On European level the EU Mandate M/490 including the final reports of the respective working groups are gaining momentum in the community. Two of the mandates core results deal with architecture description and use case management. As a response to the mandate, among other deliverables, the Smart Grid Architecture Model (SGAM) has been developed to provide a framework for Smart Grid architectures. Additionally, use case templates as well as a comprehensive methodology for use case management have been specified. The focus of this contribution is to demonstrate how use case management and the SGAM, and thus recommended by the EU, can be applied together in practice to specific use cases provided by the DISCERN project. The very purpose of the SGAM application is to share information among projects that implement similar use cases based on different technical solutions. Finally, open issues and research gaps to be addressed in order to utilize the SGAM will be presented.

mosaik - A modular platform for the evaluation of agent-based Smart Grid control

Smart Grids rely on the use of ICT for managing large numbers of active components and sensors to keep demand and generation of electricity at equilibrium while operating multiple resources within their operational limits. Due to the distributed nature of these resources, their heterogeneity as well as their sheer number, is a challenging task. Control strategies as well as novel paradigms need to be developed and thoroughly evaluated through extensive simulations. In order to yield scientifically sound and reliable results, these simulations have to rely on valid and (ideally) established models, e.g., from industry. Therefore, it is desirable to reuse these models as often as possible by combining them into new, potentially large-scale test scenarios. The introduced mosaik framework presents a flexible architecture as well as a powerful modeling and specification language to automate the process of composing existing models and simulation platforms into large-scale simulation scenarios.

Energy Informatics - Current and Future Research Directions

Due to the increasing importance of producing and consuming energy more sustainably, Energy Informatics (EI) has evolved into a thriving research area within the CS/IS community. The article attempts to characterize this young and dynamic field of research by describing current EI research topics and methods and provides an outlook of how the field might evolve in the future. It is shown that two general research questions have received the most attention so far and are likely to dominate the EI research agenda in the coming years: How to leverage information and communication technology (ICT) to (1) improve energy efficiency, and (2) to integrate decentralized renewable energy sources into the power grid. Selected EI streams are reviewed, highlighting how the respective research questions are broken down into specific research projects and how EI researchers have made contributions based on their individual academic background.

Integrated Smart Grid simulations for generic automation architectures with RT-LAB and mosaik

Although, a variety of established tools for analysis of power systems already exists, it is in the medium term very unlikely that one of these tools alone will provide all functionalities and models that are required to simulate future Smart Grids in all its facets. This is mainly due to the high number of Smart Grid use cases, actors, and technologies to be integrated that is not known from other industries so far. Accordingly, a mixture of various different and established tools will be required. These, again, have to be composed in use cases specific to complex and system-wide scenarios. Therefore, tools such as simulation platforms and suites are required that are additionally capable of integrating software and hardware models and components. Therefore, the proposed approach is an integrated concept allowing for analyzing large-scale scenarios taking into consideration both, stationary and dynamic simulations in real-time.

Highly dynamic and scalable VANET routing for avoiding traffic congestions

Traffic congestions have become a major problem in metropolitan areas world-wide, within and between cities, in particular due to the highly dynamic character of congestion building and dissolving. Our earlier work on the novel multi-agent BeeHive routing algorithms [1,2,3] derived from the honey bee behavior exhibits high adaptability and flexibility, high throughput and fault BAtolerance at a so far unsurpassed degree. Part of the algorithm is concerned with dynamically updating the quality weights of the communication links which at the key feature for the probabilistic routing decisions. We integrated these algorithms into a distributed traffic control model where vehicles are directed under decentralized control at each road intersection. The result is the

Bottom-Up Self-Organization of Unpredictable Demand and Supply under Decentralized Power Management

In the DEZENT project we had established a distributed base model for negotiating electric power from widely distributed (renewable) power sources on multiple levels in succession. Negotiation strategies would be intelligently adjusted by the agents, through (distributed) reinforcement learning procedures. The distribution of the negotiated power quantities (under distributed control as well) occurs such that the grid stability is guaranteed, under 0.5 sec. The major objective in this paper was to deal, on the same level of granularity, with short-term power balance fluctuation, in terms of a peak demand and supply management exhibiting highly dynamic, self-organizing, autonomous yet coordinated algorithms under fine-grained distributed control. Our extensive experiments show very clearly that these short-term fluctuations could be leveled down by 70 - 75 %. In this way we have tackled, for the quickly increasing renewable power systems, a crucial problem of its stability, in a novel way that scales very easily due to the completely decentralized control.

A Decentralized Heuristic for Multiple-Choice Combinatorial Optimization Problems

We present a decentralized heuristic applicable to multi-agent systems (MAS), which is able to solve multiple-choice combinatorial optimization problems (MC-COP). First, the MC-COP problem class is introduced and subsequently a mapping to MAS is shown, in which each class of elements in MC-COP corresponds to a single agent in MAS. The proposed heuristic “COHDA” is described in detail, including evaluation results from the domain of decentralized energy management systems.

A novel class of multi-agent algorithms for highly dynamic transport planning inspired by honey bee behavior

Commercial transport planning as well as individual intra-city or inter-city traffic in densely populated regions, both in Europe and the US, increasingly suffer from congestion problems, to an extent which e.g. affects predictable transport planning substantially (except - so far - for overnight tours). Due to the highly dynamic character of congestion forming and dissolving, no static approach like shortest path finding, applied globally or individually in car navigators, is adequate here: Its use even makes things worse as can be frequently observed. In this paper we present a completely decentralized multi-agent approach (termed BeeJamA) on multiple layers where car or truck routing are handled through algorithms adapted from the BeeHive algorithms which in turn have been derived from honey bee behavior. We report on extensive distributed simulation experiments in the BeeJamA project which demonstrate a very substantial improvement over traditional congestion handling.

Multi-Agent System for Coordinated Control of Facts Devices

This chapter targets on a multi-agent approach for an automated coordination and control of power flow controllers (PFC). In comparison to chapter 10 no central instance is required for the topology analysis. The agents derive the relevant actual topology through local communication and perform coordinated control actions according to the present situation. Therefore the approach can be implemented easily under the condition that a fast communication network between all network elements is available.