Abstract
In Germany and many other countries the energy market has been subject to significant changes. Instead of only a few large-scale producers that serve aggregated consumers, a shift towards regenerative energy sources is taking place. Energy systems are increasingly being made more flexible by decentralised producers and storage facilities, i.e. many consumers are also producers. The aggregation of producers form another type of power plants: a virtual power plant.On the basis of aggregated production and consumption, virtual power plants try to make decisions under the conditions of the electricity market or the grid condition. They are influenced by many different aspects. These include the current feed-in, weather data, or the demands of the consumers. Clearly, a virtual power plant is focusing on developing strategies to influence and optimise these factors. To accomplish this, many data sets can and should be analysed in order to interpret and create forecasts for energy systems. Time series based analytics are therefore of particular interest for virtual power plants.Classifying the different time series according to generators, consumers or customer types simplifies processes. In this way, scalable solutions for forecasts can be found. However, one has to first find the according clusters efficiently.This paper presents a method for determining clusters of time series. Models are adapted and model-based clustered using ARIMA parameters and an individual quality measure. In this way, the analysis of generic time series can be simplified and additional statements can be made with the help of graphical evaluations. To facilitate large scale virtual power plants, the presented clustering workflow is prepared to be applied on big data capable platforms, e.g. time series stored in Apache Cassandra, analysed through an Apache Spark execution framework.The procedure is shown here using the example of the Day-Ahead prices of the electricity market for 2018.
Highlights
Energy transition is a highly discussed topic
Conclusion and future work This paper presents a clustering workflow of time series that is targeting the operation of virtual power plants in which optimisation strategies for multivariate problems have to be developed
The paper provides a model-based clustering approach based on autoregressiveintegrated-moving average (ARIMA)
Summary
Energy transition is a highly discussed topic. It describes the shift from fossil energy sources to renewable energy and is an important piece for a solution to the global warming problem. Energy is produced by many small power plants that were geographically and, with respect to the grid, topologically dispersed. To address the specific situation of small scale producers, some VPPs integrate consumers, or so-called prosumers into their model. Their goal is to gain new flexibilities for either the business strategy on the energy stock exchange market or stable grid operation
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