In this paper we investigate the effects of marginal cost pricing and multi-criteria routing as an addition of our distributed real-time vehicle guidance protocol BeeJamA. BeeJamA is is honey bee-inspired swarm intelligence approach for minimizing individual travel times based on a vehicle-to-infrastructure architecture. We propose changes to the swarm behavior allowing for efficient dissemination of marginal costs and multi-criteria routing information. In literature, marginal cost pricing is known as a possibility for reducing global travel times, potentially to the disadvantage of individual travel times. Although we can confirm an improvement over the plain BeeJamA protocol for a microscopic simulation setup, marginal cost pricing could not outperform a path reservation variant of BeeJamA. Distributed routing protocols for computer networks and particularly for vehicle routing often do not provide for multi-criteria decisions and BeeJamA was no exception so far. So, as a second contribution of this paper, the protocol is complemented by multi-criteria routing concepts.

Die Betriebsführung elektrischer Transport- und Verteilnetze wird zunehmend dynamischer. Gründe hierfür sind einerseits die Herausforderungen durch hoch ausgelastete Netze sowie volatile Einspeisung, auf die die Netzbetreiber rechtzeitig reagieren müssen, andererseits der zunehmende Einsatz schnell regelbarer Betriebsmittel und leistungsstarker Kommunikations- und Informationstechnik. Um intelligente Entscheidungsverfahren für dynamische Systemeingriffe zu entwickeln, müssen solche Applikationen zunächst in eine dynamische Simulation des elektrischen Energiesystems eingebunden werden. In diesem Beitrag werden zwei Realisierungsmöglichkeiten zur Einbindung externer Softwaretools in die elektromechanische Simulation des verbreiteten kommerziellen Energiesystemsimulators DIgSILENT PowerFactory vorgestellt. Hierbei wird exemplarisch die Einbindung eines Multiagentensystems zur Koordination von Leistungsflussreglern gezeigt. Neben der Implementierung werden vergleichende Simulationsergebnisse bei Nutzung der beiden Schnittstellen und ermittelte Leistungswerte präsentiert.

We present and evaluate our distributed and self-adaptive vehicle routing guidance approach, termed BeeJamA, which provides drivers safely with routing directions well before each intersection. Our approach is based on a multiagent system, which is inspired by the honey bee foraging behavior. It relies on a distributed vehicle-to-infrastructure architecture. On the basis of microscopic traffic simulations under varying penetration rates, we show that BeeJamA outperforms dynamic shortest path algorithms with respect to average (global) travel times and regarding congestion avoidance.

The European electrical transmission network is operated increasingly close to its operational limits due to market integration and increased feed-in by renewable energies. For this reason, innovative solutions for a reliable, secure and efficient network operation are requested. The application of self-organizing systems promises significant potential in real-time control. This paper outlines the challenges of power system operation, gives a brief overview of relevant system characteristics and discusses the applicability of self-organizing systems for different fields of power system control. As a result of current research, the application of an agent-based decentralized power flow control system is presented and discussed in comparison to current practice based on central decision making.

We present and evaluate our adaptive and distributed vehicle routing approach, termed BeeJamA, which provides drivers safely with routing directions well before each intersection. Our approach is based on a multi-agent system which is inspired by the honey bee behavior and relies on a V2I architecture. We report on our extensive simulation experiments verifying for very large systems that BeeJamA substantially outperforms all A*-based algorithms relying on global information systems, in particular under all degrees of penetration rates as well as considering reactive flexibility and easy scalability.

We present and evaluate our self-adaptive and distributed vehicle route guidance approach, termed BeeJamA, which provides drivers safely with routing directions well before each intersection. Our approach is based on a multi-agent system which is inspired by the honey bee behavior and relies on a decentralized vehicle-to-infrastructure architecture. On the basis of microscopic traffic simulations under varying penetration rates it shows that BeeJamA has the tendency to outperform dynamic shortest path algorithms with respect to (global) travel times.

Physical parts of a system to interacting software components. In many cyber-physical systems considered nowadays, the system's parts form a network structure with concurrent entities and the need of seamless scaling and fault-tolerant information dissemination and decentralized methods. In this contribution we point out that swarm intelligence approaches may be well suited for CPS with networked components. As an example, we present our self-adaptive and distributed vehicle route guidance approach, termed BeeJamA, which provides drivers safely with routing directions well before each intersection. Our approach is based on a multi-agent system which is inspired by the honey bee behavior and relies on a decentralized vehicle-to-infrastructure architecture.

This contribution is a short introduction into the Special Session “Software Engineering Problems and Solutions in Self-Organizing Systems”. Computer Technology, as it has developed over the past 60 years, has undergone a tremendous development ending up in invading, or being indispensable for, most areas of scientific, technical, social, economic, political, and even cultural life. There is no other example in History comparable to the speed and the paradigmatic change that have come about under the influence of this technological novelty which is both a scientific and engineering effort. (No divergence like between Physics and Mechanical or Electrical Engineering has yet occurred.)

Since the past 4-7 years the paradigm of Cyber-Physical Systems has gained growing attention, starting from initiatives taken by US government institutions such as NSF, NIST and other funding agencies. Such software systems were then requested to realize a widest possible correspondence, if not congruence between software system and real-world structures, the latter to be controlled by the software system. We will, in this paper, describe the implications of this comprehensive objective, for the modeling, analyzing, testing and evaluating under a cyberphysical perspective. This will be done by sketching the major development steps within the DEZENT project over the past 7 years, from the early de-sign steps until a large real-world field study in a Southern German region which is already covered with renewable energy by nearly 100%. The emphasis will be on the mutual inspiration between applicational and software-technical constraints and insights, gained by the partners from Computer Science, Electrical Engineering and practitioners who altogether benefitted considerably with respect to their successful cooperation.

The potential of adjusting the demand of certain appliances with time-flexible duty cycles is currently used for load balancing purposes only (e. g. Demand Side Management). However, a coordinated operation scheme for consumers and producers of electrical energy may also be used for grid stabilization or in general for a more efficient utilization of existing distribution grids, directly influencing line currents and voltage profiles along the network.

Traffic congestions have been a major problem in metropolitan areas worldwide, causing enormous economical as well as ecological damage. At the same time, in densely populated areas with high vehicle traffic, central information gathering and distribution to vehicles takes too long for providing accurate, let alone optimal routing directions (which would have to be available in due time before vehicles arrive at road intersections). Accurate information provided too late may even add to congestion problems. In this paper we present a bottom-up, multi-agent online approach termed BeeJamA (Bee-Inspired Traffic Jam Avoidance) for individual vehicle routing which, on its network communication layer, is taking advantage of our novel self - organizing network routing algorithm BeeHive/BeeAdhoc. This Swarm Intelligence based method has been largely inspired by the behavior of honey bees. As a distributed algorithm BeeJamA does not rely on global information, and scalability is not a critical issue. BeeJamA features dynamic deadlines. The quality of the algorithm has a strong impact on the acceptance rate through the drivers, for installing and operating communication features (navigators and routing-related software) as well as on driver adherence to routing directions. This, in turn, requires a high amount of flexibility for routing algorithms considering (unpredictable) resetting of destinations by drivers, making dynamic real-time reactions a critical issue. For a comprehensive and comparative realistic evaluation reflecting the aforementioned aspects/parameters we have developed a generic routing framework (GRF) which allows to run BeeJamA and other routing algorithms on different scientific or commercial traffic simulators. (Each of them serves different purposes and is therefore considerably abstracting from reality.) While we (briefly) report on extensive simulation experiments on the MAT-Sim simulator which verify BeeJamA's superior performance compared to existing models we will also outline - as part of our current research - how to create an incremental procedure for performing realistic field studies where in ever larger areas the abstract simulation is replaced, and observed, through real traffic. This imposes very strict requirements for the real-time, or online, performance of the simulator. Comprehensive results results of these altogether novel experimental investigations will be subject of upcoming publications.

Traffic congestions have been a major problem in metropolitan areas worldwide, causing enormous economical as well as ecological damage. We argue that, due to the highly dynamic character of congestion forming and dissolving, any adequate solution for individual online vehicle routing in large traffic systems will require distributed, adaptive coordination of local navigators in order to transmit directions in due time before any road intersection which would still be valid when carried out. In this paper a completely distributed and adaptive swarm intelligence based multi-layered approach termed BeeJamA is presented. It features dynamic deadlines. There is no need of global or centralized information. We report on extensive simulation experiments with the MATSim simulator verifying BeeJamA’s superior performance compared to existing models.

Elektrische Energieubertragungsnetze stosen immer haufiger an ihre Kapazitatsgrenzen. Eine Moglichkeit zur Erhohung der Ubertragungskapazitat und damit zur Vermeidung von Netzengpassen im elektrischen Energietransportnetz stellt der Einsatz von Leistungsflussreglern (LFR) dar. Diese Regler ermoglichen eine Verschiebung des Leistungsflusses von belasteten Leitungen auf weniger ausgelastete parallel verlaufende Pfade und damit eine effizientere Betriebsmittelauslastung der zur Verfugung stehenden Netze. Zur Ausregelung von weitraumigen Netzengpassen werden mehrere solcher LFR benotigt, die fur einen optimalen Einsatz koordiniert betrieben werden mussen. Da die LFR oftmals jedoch in unterschiedlichen Regelzonen installiert sind und damit von wirtschaftlich getrennten Netzbetreibern verwaltet werden, ist eine vollstandige Systembeobachtbarkeit nicht moglich. Statische Koordinierungsverfahren sind aufgrund der hohen und unvorhersehbaren Systemdynamik auserst ineffizient und konnen sogar selbst Uberlastsituationen verursachen. Dieser Beitrag stellt einen agentenbasierten verteilten Losungsund Koordinationsansatzes vor, bei dem LFR weitestgehend autonom und unter Verzicht auf globale vollstandige Informationen kritische Belastungssituationen rechtzeitig erkennen und durch geeignete Regelungsaktionen (weitraumig) entscharfen.

The liberalization of the power market, an overall increase in power demand and the integration of high capacity unpredictable renewable resources (e.g. wind power) pose a challenge to transmission network operators that have to guarantee a stable and efficient operating of the grid. A way to improve the stability and efficiency of the existing network – aside from expensive reconstruction – is the integration of fast power flow controllers in order to dynamically redirect power flows away from critically loaded resources that may be threatened by an overload. In this paper we outline our current work in progress on developing a multi-agent model that allows for an autonomous distributed coordination of fast power flow controllers without the need for global information.

Both the coordination of international energy transfer and the integration of a rapidly growing number of decentralized energy resources (DER) throughout most countries causes novel problems for avoiding voltage band violations and line overloads. Traditional approaches are typically based on global off-line scheduling under globally available information and rely on iterative procedures that can guarantee neither convergence nor execution time. In this paper we focus on stability problems in power grids based on widely dispersed (renewable) energy sources. In this paper we will introduce an extension of the DEZENT algorithm, a multi-agent based coordination system for DER, that allows for the feasibility verification in constant and predetermined time. We give a numerical example showing the legitimacy of our approach and mention ongoing and future work regarding the implementation and utilization.

The increasing integration of controllable energy resources into distribution networks introduces novel challenges and opportunities in terms of distributed voltage control. As these devices are mainly controlling their active power injection, and do not provide voltage control capabilities, a regional coordinator has to determine favorable combinations of power injections and consumption with respect to the voltage profile of the particular network segment. The main challenge is to approximate voltage profiles from the complex nodal power balance of all nodes in a particular network. Standard algorithms calculate exact voltage profiles from combinations of complex nodal net power, but fail to provide information about the network conditions in the surrounding of this particular point of operation. In this paper we present a novel approach basing on the geometrical interpretation of a modified nodal admittance matrix. It allows for the determination of combinations of power injections in the surrounding of a point of operation that have a similar corresponding voltage profiles. Our approach takes into account reactive as well as active power injections.

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.