Profit optimized machine learning aided electricity storage; Comparison between privately owned and neighborhood shared

Abstract

Batteries are an essential tool for energy transition. They provide the ability to reduce imbalances by absorbing the forecast errors for consumers and renewable energy sources. This has the added benefit that congestions occurring due to supply and demand mismatches would be prevented. Furthermore, they allow generating a stable power consumption for a longer period by utilizing the available reserves. The electricity markets are all power markets, where the products grant an amount of power for a certain period. Therefore, the introduction of a battery would make it easier to buy power for a portfolio of consumers. Currently, it is not yet feasible to use a battery for the consumer segment as the revenues generated are simply too small to pay the battery back before it reaches the end of its lifetime.

This report aims to describe a machine learning-based battery operating algorithm that can be utilized for the consumer market. The operating algorithm will be trading on the day ahead and the voluntary aFRR market while satisfying the demand of the consumer(s). The trading is done based on the forecast generated by different machine learning models. For the day ahead market, two forecasting models are tested, namely an XGBoost algorithm and a Dense Neural Network. For the imbalance market, a simple Dense Neural Network and a Long Short-Term Memory (LSTM) are compared and for the consumer forecast, an XGBoost model is compared to a Lasso. All forecasts are combined to create a profit-maximizing algorithm. Lastly, a comparison is made between having an individual battery for every homeowner and aggregating a group of consumers together in a larger battery is compared.

The results show that forecasting an aggregated group of consumers has a significantly lower error compared to forecasting individual demand patterns. The large errors in the individual forecast result in a large opportunity cost, based on the Value of Lost Load (VoLL), which renders the benefits of owning a battery useless again. However, the aggregated group of consumers combined into a single large battery is profitable. Considering the current prices (22Q1) the battery generated enormous profits, such that the battery would be break-even in less than 2 years.