• Energy Research

Optimizing and predicting the energy consumption of industrial manufacturing can increase its cost efficiency. The interaction of different aspects and components is necessary. An overarching framework is currently still missing, and establishing such is the central research approach in this paper. This paper provides an overview of the current demands on the manufacturing industry from the perspective of digitalization and sustainability. On the basis of the developed fundamentals and parameters, a superordinate framework is proposed that allows the modelling and simulation of energy-specific properties on several product and process levels. A detailed description of the individual methods concludes this work and demonstrates their application potential in an industrial context. As a result, this integrated conceptual framework offers the possibility of optimizing the production system, in relation to different energy flexibility criteria.

In electrical power systems, asset management procedures have developed into a central element of network operations and planning. Hidden Markov models (HMM) can be used to transform various data collected from substation equipment into failure probabilities. Based on these failure probabilities a mathematical decision tool can be created, which could be used in system-level simulation and experimentation. In particular, collected data utilizing the dissolved gas analysis-in-oil (DGA) field methodology for transformers are analyzed with HMM for the prediction of their deterioration level.

ICT advancements (low latency new generation networks) transform the Distributed Energy Resources (DERs) at the edge of the electrical grid gradually into grid assets and drive the energy system towards a decentralized transactive operation. Local Energy Markets (LEMs) have been proposed as a means to facilitate and orchestrate the vast penetration of DERs. We propose here a LEM operation which concludes into fair pricing with monetary gains for both prosumers and consumers participating in the LEM. For the Day-Ahead LEM operation we apply a chance-constrained optimization algorithm in order to tackle the uncertainties governing the Day-Ahead LEM operation. The proposed LEM architecture is validated upon an IEEE low voltage European test feeder benchmark.

Electricity price forecasting (EPF) has become an essential part of decision-making for energy companies to participate in power markets. As the energy mix becomes more uncertain and stochastic, this process has also become important for industrial companies, as their production schedules are greatly impacted by energy costs. Although various approaches have been tested with varying degrees of success, this study focuses on predicting day-ahead market (DAM) prices in different European markets and how this directly affects the optimal production scheduling for various industrial loads. We propose a fuzzy-based architecture that incorporates the results of two forecasting algorithms; a random forest (RF) and a long short-term memory (LSTM). To enhance the accuracy of the proposed model for a specific country, electricity market data from neighboring countries are also included. The developed DAM price forecaster can then be utilized by energy-intensive industries to optimize their production processes to reduce energy costs and improve energy-efficiency. Specifically, the tool is important for industries with multi-site production facilities in neighboring countries, which could reschedule the production processes depending on the forecasted electricity market price.