Karl-Uwe Stucky

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.

First Evaluation Results Using the New Electrical Data Recorder for Power Grid Analysis

Today, power systems are subject to fundamental changes concerning functionality and dynamics due to new energy sources and increasing demand. However, detailed information of the system and the system state is the property of the suppliers or distributors, and is not comprehensively available to researchers at present. We propose to capture easily accessible, high-rate, low-voltage (LV) time series at different locations, and to store the whole data for subsequent usage in a large database. A system state simulation shall use these data and provide for load information without knowing the currents. For this purpose, we develop the electrical data recorder (EDR) and perform measurements in our first starting test site, the island network like Karlsruhe Institute of Technology campus. We first present comparison results between captured voltage characteristics and campus smart meter measurements that we use as an indication of the load status. We develop the Electrical Grid Analysis Simulation Modeling and Visualization Tool (eASiMoV) and show feasibility in a simple simulation. We measure the storage transfer to the large-scale database facility and give evaluation results. The EDR device, the eASiMoV software, and data handling methods are exhibited as valuable components of a promising new approach.

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.

Introducing the Electrical Data Recorder as a new capturing device for power grid analysis

Power systems are facing fundamental changes concerning functionality and dynamics today. However, detailed data of the system are property of the suppliers or distributors and are not easily available to researchers at present. In order to understand the system dependencies we propose to derive state information by combining high-rate low-voltage time series captures at different locations together with a simulation model of the grid. We take the island network like KIT campus as our starting investigation site. In a first step we developed the Electrical Data Recorder (EDR), which is capable of recording three phase voltage time series at up to 25 kHz synchronously. All data are stored in a large scale database facility (LSDF) for subsequent usage. We intend to derive a simulation model from the comparison of the voltage characteristics to periodic smart meter measurements as the indication of the load status. In this article we introduce the new recording device and present first results.

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 Concept for the Control, Monitoring and Visualization Center in Energy Lab 2.0

Energy Lab 2.0 is designed as a large experimental test and simulation field for multi-scale and multi-mode energy system facilities at KIT. A Smart Energy System Simulation and Control Center SEnSSiCC is the core component in terms of information and communication technology. The present article introduces basic concepts for the Control, Monitoring and Visualization Center CMVC of SEnSSiCC. The CMVC bundles all communication channels and real facilities, simulation environments, and data repositories into an integrated research environment for planning, control, monitoring, analyzing and visualization of smart grids and their components, and furthermore for evaluating future concepts for smart grid utility operation. Special emphasis is placed on the distributed computing operating system environment setup for the CMVC, the intended use of Big Data technologies, the polyglot approach for data management and analysis, and first concepts for implementing a hybrid agent based simulation environment. Also, the usage of web technologies and microservices are considered as key aspects of the overall architecture.

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.

A Generic Microservice Architecture for Environmental Data Management

The growing popularity of Web applications and the Internet of Things cause an urgent need for modern scalable data management to cope with large amounts of data. In the environmental domain these problems also need a solution because of big data coming from a large amount of sensors or users (e.g. crowdsourcing applications). This paper presents an architecture that uses a microservice approach to create a data management backend for the mentioned applications. The main concept shows that microservices can be used to define separate services for different data types and management tasks. This separation leads to many advantages such as better scalability and low coupling between different features. Two prototypes, which are already implemented, are evaluated in this paper.

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.