Wolfgang Süß

Data processing of high-rate low-voltage distribution grid recordings for smart grid monitoring and analysis

Power networks will change from a rigid hierarchic architecture to dynamic interconnected smart grids. In traditional power grids, the frequency is the controlled quantity to maintain supply and load power balance. Thereby, high rotating mass inertia ensures for stability. In the future, system stability will have to rely more on real-time measurements and sophisticated control, especially when integrating fluctuating renewable power sources or high-load consumers like electrical vehicles to the low-voltage distribution grid.In the present contribution, we describe a data processing network for the in-house developed low-voltage, high-rate measurement devices called electrical data recorder (EDR). These capture units are capable of sending the full high-rate acquisition data for permanent storage in a large-scale database. The EDR network is specifically designed to serve for reliable and secured transport of large data, live performance monitoring, and deep data mining. We integrate dedicated different interfaces for statistical evaluation, big data queries, comparative analysis, and data integrity tests in order to provide a wide range of useful post-processing methods for smart grid analysis.We implemented the developed EDR network architecture for high-rate measurement data processing and management at different locations in the power grid of our Institute. The system runs stable and successfully collects data since several years. The results of the implemented evaluation functionalities show the feasibility of the implemented methods for signal processing, in view of enhanced smart grid operation.

Solving scheduling problems in grid resource management using an evolutionary algorithm

Evolutionary Algorithms (EA) are well suited for solving optimisation problems, especially NP-complete problems This paper presents the application of the Evolutionary Algorithm GLEAM (General Learning and Evolutionary Algorithm and Method) in the field of grid computing Here, grid resources like computing power, software, or storage have to be allocated to jobs that are running in heterogeneous computing environments The problem is similar to industrial resource scheduling, but has additional characteristics like co-scheduling and high dynamics within the resource pool and the set of requesting jobs The paper describes the deployment of GLEAM in the global optimising grid resource broker GORBA (Global Optimising Resource Broker and Allocator) and the first promising results in a grid simulation environment.

Fast Multi-objective Scheduling of Jobs to Constrained Resources Using a Hybrid Evolutionary Algorithm

The problem tackled here combines three properties of scheduling tasks, each of which makes the basic task more challenging: job scheduling with precedence rules, co-allocation of restricted resources of different performances and costs, and a multi-objective fitness function. As the algorithm must come up with results within a few minutes runtime, EA techniques must be tuned to this limitation. The paper describes how this was achieved and compares the results with a common scheduling algorithm, the Giffler-Thompson procedure.

Optimised scheduling of grid resources using hybrid evolutionary algorithms

The present contribution shall illustrate the necessity of planning and optimising resource allocation in a grid. Requirements to be met by a resource management system will be defined. These requirements are comparable with the requirements on planning systems in other fields, e.g. production planning systems. Here, various methods have already been developed for optimised planning. Suitable methods are Evolutionary Algorithms. Based on an example from the field of production planning, the performance of these methods is demonstrated and use in the GORBA resource broker shall be described.

Fast Rescheduling of Multiple Workflows to Constrained Heterogeneous Resources Using Multi-Criteria Memetic Computing

This paper is motivated by, but not limited to, the task of scheduling jobs organized in workflows to a computational grid. Due to the dynamic nature of grid computing, more or less permanent replanning is required so that only very limited time is available to come up with a revised plan. To meet the requirements of both users and resource owners, a multi-objective optimization comprising execution time and costs is needed. This paper summarizes our work over the last six years in this field, and reports new results obtained by the combination of heuristics and evolutionary search in an adaptive Memetic Algorithm. We will show how different heuristics contribute to solving varying replanning scenarios and investigate the question of the maximum manageable work load for a grid of growing size starting with a load of 200 jobs and 20 resources up to 7000 jobs and 700 resources. Furthermore, the effect of four different local searchers incorporated into the evolutionary search is studied. We will also report briefly on approaches that failed within the short time frame given for planning.

A Grid Environment for Simulation and Optimization and a First Implementation of a Biomedical Application

Simulation and optimization tools are used to design and develop complex systems e.g. in technical or medical fields and generally require a huge hardware and software resources. Therefore grid computing offers a chance to use distributed resources. This paper describes an overall concept of a heterogeneous grid environment for simulation and optimization problems. The basic idea of this concept is that the application is described as an instantiation of a workflow. The grid middleware, especially resource management, has to be adapted to this workflow-based concept. Another aspect of our concept is a modular and recursive structure of the grid environment to achieve flexibility and scalability. The first reference application of this concept is a biomedical system for planning refractive surgery of a human eye and for simulating in ophthalmologic research. It is called “Virtual Eye”. Here, a prototype of this grid system shall be discussed.

An Evolutionary Algorithm for Design Optimization of Microsystems

The concept of an evolutionary algorithm for improving the design optimization process is presented. A first application, the design optimization of a micropump, is used for both, the description of the concept being especially tailored for multicriteria applications and the presentation of very promising results.

Tackling the grid job planning and resource allocation problem using a hybrid evolutionary algorithm

This paper presents results of new experiments with the Global Optimising Resource Broker and Allocator GORBA for grid systems. The scheduling algorithm is based on the Evolutionary Algorithm GLEAM (General Learning Evolutionary Algorithm and Method) and several heuristics. The task of planning grid resource allocation is compared to pure NP-complete job shop scheduling and it is shown in which way it is of greater complexity. Two different gene models and two repair methods are described in detail and assessed by the experimental results. Based on the analysis of the experimental results, directions of further work and improvements will be outlined.

Security in Large-Scale Data Management and Distributed Data Acquisition

The internet is about to change from a pure network of computers to a network of more or less intelligent devices, the computer being just one of them. Examples of this change are the concepts of smart applications like smart homes, smart traffic control and guidance systems, smart power grids, or smart buildings. These systems require among others a high degree of robustness, reliability, scalability, safety, and security. In this paper, we concentrate on the data exchange and management aspect and introduce a security concept for scalable and easy-to-use Generic Data Services, called SeGDS. It covers application scenarios from embedded field devices for data acquisition to large-scale generic data applications and data management. The concept is based largely on proven standard enterprise hardware and standard solutions. As a first application, we report about transport and management of mass data originating from high-resolution electrical data devices, which measure parameters of the electrical grid with a high sample rate. The shown solution is intended to be a contribution to concepts of a secure, flexible, but comparably inexpensive management of large amounts of data coming from modern smart power grids or other comparable smart applications.

Security Issues in Distributed Data Acquisition and Management of Large Data Volumes

The internet is faced with new application scenarios like smart homes, smart traffic control and guidance systems, smart power grids, or smart buildings. They all have in common that they require a high degree of robustness, reliability, scalability, safety, and security. This paper provides a list of criteria for these properties and focuses on the aspect of data exchange and management. It introduces a security concept for scalable and easy-to-use Secure Generic Data Services, called SeGDS, which covers application scenarios extending from embedded field devices for data acquisition to large-scale generic data applications and data management. Our concept is based largely on proven standard solutions and uses standard enterprise hardware. The first application deals with transport and management of mass data originating from high-resolution electrical data devices, which measure parameters of the electrical grid with a high sample rate.