Future Perspectives of Co-Simulation in the Smart Grid Domain

The recent attention towards research and development in cyber-physical energy systems has introduced the necessity of emerging multi-domain co-simulation tools. Different educational, research and industrial efforts have been set to tackle the co-simulation topic from several perspectives. The majority of previous works has addressed the standardization of models and interfaces for data exchange, automation of simulation, as well as improving performance and accuracy of co-simulation setups. Furthermore, the domains of interest so far have involved communication, control, markets and the environment in addition to physical energy systems. However, the current characteristics and state of co-simulation testbeds need to be re-evaluated for future research demands. These demands vary from new domains of interest, such as human and social behavior models, to new applications of co-simulation, such as holistic prognosis and system planning. This paper aims to formulate these research demands that can then be used as a road map and guideline for future development of co-simulation in cyber-physical energy systems

Hardware-in-the-Loop Co-Simulation Based Validation of Power System Control Applications

Renewables are key enablers for the realization of a sustainable energy supply but grid operators and energy utilities have to mange their intermittent behavior and limited storage capabilities by ensuring the security of supply and power quality. Advanced control approaches, automation concepts, and communication technologies have the potential to address these challenges by providing new intelligent solutions and products. However, the validation of certain aspects of such smart grid systems, especially advanced control and automation concepts is still a challenge. The main aim of this work therefore is to introduce a hardware-in-the-loop co-simulation-based validation framework which allows the simulation of large-scale power networks and control solutions together with real-world components. The application of this concept to a selected voltage control example shows its applicability.Comment: 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE

Methods and concepts for designing and validating smart grid systems

This Editorial provides an introduction to the Special Issue “Methods and Concepts for Designing and Validating Smart Grid Systems”. Furthermore, it also provides an overview of the corresponding papers that where recently published in MDPI’s Energies journal. The Special Issue took place in 2018 and accepted a total of 19 papers from 19 different countries

Validating Intelligent Power and Energy Systems { A Discussion of Educational Needs

Traditional power systems education and training is flanked by the demand for coping with the rising complexity of energy systems, like the integration of renewable and distributed generation, communication, control and information technology. A broad understanding of these topics by the current/future researchers and engineers is becoming more and more necessary. This paper identifies educational and training needs addressing the higher complexity of intelligent energy systems. Education needs and requirements are discussed, such as the development of systems-oriented skills and cross-disciplinary learning. Education and training possibilities and necessary tools are described focusing on classroom but also on laboratory-based learning methods. In this context, experiences of using notebooks, co-simulation approaches, hardware-in-the-loop methods and remote labs experiments are discussed.Comment: 8th International Conference on Industrial Applications of Holonic and Multi-Agent Systems (HoloMAS 2017

Optimal Data Reduction of Training Data in Machine Learning-Based Modelling: A Multidimensional Bin Packing Approach

In these days, when complex, IT-controlled systems have found their way into many areas, models and the data on which they are based are playing an increasingly important role. Due to the constantly growing possibilities of collecting data through sensor technology, extensive data sets are created that need to be mastered. In concrete terms, this means extracting the information required for a specific problem from the data in a high quality. For example, in the field of condition monitoring, this includes relevant system states. Especially in the application field of machine learning, the quality of the data is of significant importance. Here, different methods already exist to reduce the size of data sets without reducing the information value. In this paper, the multidimensional binned reduction (MdBR) method is presented as an approach that has a much lower complexity in comparison on the one hand and deals with regression, instead of classification as most other approaches do, on the other. The approach merges discretization approaches with non-parametric numerosity reduction via histograms. MdBR has linear complexity and can be facilitated to reduce large multivariate data sets to smaller subsets, which could be used for model training. The evaluation, based on a dataset from the photovoltaic sector with approximately 92 million samples, aims to train a multilayer perceptron (MLP) model to estimate the output power of the system. The results show that using the approach, the number of samples for training could be reduced by more than 99%, while also increasing the model’s performance. It works best with large data sets of low-dimensional data. Although periodic data often include the most redundant samples and thus provide the best reduction capabilities, the presented approach can only handle time-invariant data and not sequences of samples, as often done in time series

Mapping of Self-organization Properties and Non-functional Requirements in Smart Grids

International audienceFuture electrical power networks will be composed of large collections of autonomous components. Self-organization is an organizational concept that promises robust systems with the ability to adapt themselves to system perturbations and failures and thus may yield highly robust systems with the ability to scale freely to almost any size. In this position paper the authors describe the well-established process of use case based derivation of non-functional requirements in energy systems and propose a mapping strategy for aligning properties of self-organizing systems with the ICT- and automation system requirements. It is the strong belief of the authors that such a mapping will be a key factor in creating acceptance of and establishing self-organization in the domain of electrical energy systems