Michael Sonnenschein

Object-oriented support for modelling and simulation of individual-oriented ecological models

Abstract Opposed to traditional mathematical methods, the technique of individual-oriented modelling chooses distinguishable individuals as the basic entities of description. An ecosystem is described by all static and dynamic properties of the individuals involved in the system as well as time varying properties of the environment. Individuals change their state over time or due to internal and external events. Using the individual-oriented approach, programming skills are indispensable. Coding individual-oriented models is a complex, tedious and error prone task, which leads to a long list of problems. Many, although not all, problems may be solved using object-oriented software libraries. E co S im is a C++-class library especially designed to support individual-oriented modelling and simulation of ecological systems. E co S im brings together new advances in object-oriented discrete-event simulation and ecology. The process of implementing individual-oriented models is facilitated by providing classes for those parts, that are common to all such models. This covers among others the specification of static and dynamic properties of ‘individuals’, the specification of dynamically changing environments as well as support for ‘on the fly’ analysis and animation of generated data. Using E co S im ecologists may therefore concentrate on the unique parts of their models.

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.

Encoding distributed search spaces for virtual power plants

The optimization task in many virtual power plant (VPP) scenarios comprises the search for appropriate schedules in search spaces from distributed energy resources. In scenarios with a decoupling of plant modeling and plant control, these search spaces are distributed as well. If merely the controller unit of a plant knows about the subset of operable schedules that are allowed to be considered by the central scheduling unit, then these sets have to be effectively communicated. We discuss an approach of learning the envelope that separates operable from non-operable schedules inside the space of all schedules by means of support vector data description. Then, only the comparatively small set of support vectors has to be transmitted as a classifier for distinguishing schedules during optimization. We applied this approach to simulated VPP.

Efficient Simulation of THOR Nets

The aim of our project is to develop and implement an efficient modeling technique and methods for a fast sequential and distributed simulation of real-time systems, such as assembly lines, industrial control systems, or communication protocols. Our simulation language developed to build models of these systems is a class of high-level Petri nets which allows complex objects for token values and provides different kinds of timing aspects as well as an appropriate structuring mechanism for nets. We call this kind of high-level Petri nets Thorns (Timed Hierarchical Object-Related Nets).

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.

Designing dependable and sustainable Smart Grids – How to apply Algorithm Engineering to distributed control in power systems ☆

Abstract In this work, we present the Smart Grid Algorithm Engineering (SGAE) process model for application-oriented research and development in information and communication technology (ICT) for power systems. The SGAE process model is motivated by the main objective of contributing application-oriented research results for distributed control concepts on a sound methodological background. With this process model, we strive for an engineering aspiration within the domain of Smart Grids. The process model is set up with an initial conceptualisation phase followed by an iterable cycle of five phases with both analytical and experimental parts, giving detailed information on inputs and results for each phase and identifying the needed actors for each phase. Simulation of large-scale Smart Grid scenarios is a core component of SGAE. We therefore elaborate on tooling and techniques needed in that context and illustrate the whole process model using an application example from a finished research and development project.

Distributed hybrid constraint handling in large scale virtual power plants

In many virtual power plant (VPP) scenarios, numerous individually configured units within a VPP have to be scheduled regarding both global constraints (i.e. external market demands) and local constraints (i.e. technical, economical or ecological aspects for each unit). Approaches for global and local constraint handling have been discussed in the relevant literature independently. A hybrid approach is proposed that combines a decentralized combinatorial optimization heuristic with the encoding of individually constrained search spaces into unconstrained representations by means of support vector data description. The approach is applied to simulated VPP.

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.

On the Influence of Inter-Agent Variation on Multi-Agent Algorithms Solving a Dynamic Task Allocation Problem under Uncertainty

Multi-agent systems often consist of heterogeneous agents with different capabilities and objectives. While some agents might try to maximize their systems utility, others might be self-interested and thus only act for their own good. However, because of their limited capabilities and resources, it is often necessary that agents cooperate to be able to satisfy given tasks. To work together on such a task, the agents have to solve a task allocation problem, e.g., by teaming up in groups like coalitions or distributing the task among themselves on electronic markets. In this paper, we introduce two algorithms that allow agents to cooperatively solve a dynamic task allocation problem in uncertain environments. Based on these algorithms, we investigate the influence of inter-agent variation on the systems behavior. One of these algorithms explicitly exploits inter-agent variation to solve the task without communication between the agents, while the other builds upon a fixed overlay network in which agents exchange information. Throughout the paper, the frequency stabilization problem from the domain of decentralized power management serves as a running example to illustrate our algorithms and results.

Support vector based encoding of distributed energy resources' feasible load spaces

The sets of feasible load schedules that distributed energy resources are able to operate, jointly define the search space within many virtual power plant optimization tasks. If a centralized approach is considered, a central, single scheduling unit needs to know for each energy resource what schedules comply with all given constraints, because only these are operable and might be taken into account for optimization. As many constraints depend on state or time, sets of currently operable alternatives have repeatedly to be communicated to the scheduler in order to avoid central modeling of each single resource. We here present a support vector based approach for learning a highly efficient geometric representation of the space of feasible alternatives for operable schedules. This description is communicated to the scheduler and the encoded information implicitly contains all constraints and therefore makes their modeling dispensable at scheduler side.