IAI-Veranstaltungskalender

Pursuing the goal of carbon neutrality has led to an unprecedented transformation in the energy system. Generation out of thermal power plants is increasingly replaced by renewable sources. Massive installment of photovoltaic generation led to an increasingly complex regulatory and economic environment. Therein, incentives to stimulate renewable generation while preserving grid stability also entailed the use of storage systems in residential  homes. Forecast based operation strategies of these systems are deployed to integrate more renewables into the electricity system. However, solar generation as well as electrical load on site are subject to forecast uncertainties decreasing the control system’s performance.
 
Targeting this issue, we present a control scheme using a model of forecast uncertainties to control storage systems combined with photovoltaic generation and electric load. This control scheme is applied to a battery storage system and an electric vehicle charger in several economic scenarios. Depending on the specific  economic framework, stochastic control outperforms comparable heuristic and deterministic optimization-based control schemes. Simulations show that it is particularly successful when competing incentives are in place. An example is weighing self-consumption of solar generation against avoiding high power feed-in to the distribution grid. Besides the simulation studies, both application cases are demonstrated in real households as a proof of concept.
 
The studied systems were set in a residential context where the financial margins are slim. However, using stochastic model predictive control showed a  promising prospect for smart grid systems and the methods used in this work can be applied to additional systems in the future.
 

Numerous real-world systems, whether they are of natural or human origin, display complex interactions and dynamics evolving over time. As well known, transforming time series data into a complex network representation helps to gain a deep understanding of the dynamics underlying the data.  In this talk, I will provide an introduction about converting time series to complex networks and then introduce a new approach in the context of physics for converting spatial-temporal series data into a complex network. And as an example study, I will discuss its application to the air quality of various regions in Istanbul, the largest metropolitan city in Turkiye with a population of approximately 16 million.

Power grids are the biggest machines on Earth. They are also the most complex ones,
as they involve human beings and their complex correlated electricity consumption patterns.
In this talk I will give some short overview on data-driven research dealing with frequency fluctuations in power grids. Whereas variations in the dynamics of renewable energy generation are reasonably well studied, a deeper understanding of the variations in consumer consumption dynamics is still missing. I will talk about highly resolved residential electricity consumption data of Austrian, German and UK households and introduce a stochastic model to quantitatively capture the highly intermittent demand fluctuations.