Determining Cost-Efficient Controls of Electrical Energy Storages Using Dynamic Programming

Volatile electrical energy prices are a challenge and an opportunity for small and medium-size companies in energy-intensive industries. By using electrical energy storage and/or an adaptation of production processes, companies can significantly profit from time-depending energy prices and reduce their energy costs. We consider a time-discrete optimal control problem to reach a desired final state of the energy storage at a certain time step. Thereby, the energy input is discrete since only multiples of 100 kWh can be purchased at the EPEX SPOT market. We use available price estimations to minimize the total energy cost by a rounding based dynamic programming approach. With our model non-linear energy loss functions of the storage can be considered and we obtain a significant speed-up compared to the integer (linear) programming formulation

Leveraging the Potential of Novel Data in Power Line Communication of Electricity Grids

Electricity grids have become an essential part of daily life, even if they are often not noticed in everyday life. We usually only become particularly aware of this dependence by the time the electricity grid is no longer available. However, significant changes, such as the transition to renewable energy (photovoltaic, wind turbines, etc.) and an increasing number of energy consumers with complex load profiles (electric vehicles, home battery systems, etc.), pose new challenges for the electricity grid. To address these challenges, we propose two first-of-its-kind datasets based on measurements in a broadband powerline communications (PLC) infrastructure. Both datasets FiN-1 and FiN-2, were collected during real practical use in a part of the German low-voltage grid that supplies around 4.4 million people and show more than 13 billion datapoints collected by more than 5100 sensors. In addition, we present different use cases in asset management, grid state visualization, forecasting, predictive maintenance, and novelty detection to highlight the benefits of these types of data. For these applications, we particularly highlight the use of novel machine learning architectures to extract rich information from real-world data that cannot be captured using traditional approaches. By publishing the first large-scale real-world dataset, we aim to shed light on the previously largely unrecognized potential of PLC data and emphasize machine-learning-based research in low-voltage distribution networks by presenting a variety of different use cases

Development of Demand Factors for Electric Car Charging Points for Varying Charging Powers and Area Types

With the increasing number of electric vehicles, the required charging infrastructure is increasing rapidly. The lack of historical data for the charging infrastructure compromises a challenge for distribution system operators to forecast the corresponding increase in the load demand. This challenge is characterised by two main uncertainties, namely, the charging power of the charging infrastructure and its location. Expectedly, the charging infrastructure is going to include varying charging powers and is going to be installed country-wide in different area types. Hence, this contribution sets to tackle these two uncertainties by developing demand factors for the charging infrastructure according to the area type. In order to develop the demand factors, a stochastic simulation tool for the charging profiles has been run for a simulation period of 5200 weeks (100 years) for six main charging powers and seven area types for up to 500 charging points. Thus, compromising a total of over 2.1 million simulated charging profiles. The resulting demand factor curves cover the charging powers between 3.7 kW and 350 kW with 1 kW steps for a total of 348 kW steps. Furthermore, they differ according to seven area types ranging from an urban metropolis to a rural village and are developed for up to 500 charging points. Consequently, the demand factor curves serve as a base to be used for the strategic grid planning of distribution power grids while taking the future development of the charging infrastructure into account

Development of Demand Factors for Electric Car Charging Points for Varying Charging Powers and Area Types

With the increasing number of electric vehicles, the required charging infrastructure is increasing rapidly. The lack of historical data for the charging infrastructure compromises a challenge for distribution system operators to forecast the corresponding increase in the load demand. This challenge is characterised by two main uncertainties, namely, the charging power of the charging infrastructure and its location. Expectedly, the charging infrastructure is going to include varying charging powers and is going to be installed country-wide in different area types. Hence, this contribution sets to tackle these two uncertainties by developing demand factors for the charging infrastructure according to the area type. In order to develop the demand factors, a stochastic simulation tool for the charging profiles has been run for a simulation period of 5200 weeks (100 years) for six main charging powers and seven area types for up to 500 charging points. Thus, compromising a total of over 2.1 million simulated charging profiles. The resulting demand factor curves cover the charging powers between 3.7 kW and 350 kW with 1 kW steps for a total of 348 kW steps. Furthermore, they differ according to seven area types ranging from an urban metropolis to a rural village and are developed for up to 500 charging points. Consequently, the demand factor curves serve as a base to be used for the strategic grid planning of distribution power grids while taking the future development of the charging infrastructure into account