Battery valuation on electricity intraday markets with liquidity costs

TL;DR

This paper introduces a comprehensive stochastic framework for valuing batteries in electricity intraday markets, emphasizing the importance of liquidity costs and dynamic pricing models to optimize profits and reduce losses.

Contribution

It presents a novel combined stochastic and deterministic model for market prices and liquidity costs, improving battery valuation accuracy in intraday trading.

Findings

Accounting for liquidity costs significantly increases profit estimates.

Stochastic mid-price modeling outperforms deterministic forecasts.

Modeling liquidity costs prevents large losses in battery trading.

Abstract

In this paper, we propose a complete modelling framework to value several batteries in the electricity intraday market at the trading session scale. The model consists of a stochastic model for the 24 mid-prices (one price per delivery hour) combined with a deterministic model for the liquidity costs (representing the cost of going deeper in the order book). A stochastic optimisation framework based on dynamic programming is used to calculate the value of the batteries. We carry out a back test for the years 2021, 2022 and 2023 for the German market and for the French market. We show that it is essential to take liquidity into account, especially when the number of batteries is large: it allows much higher profits and avoids high losses using our liquidity model. The use of our stochastic model for the mid-price also significantly improves the results (compared to a deterministic framework where the mid-price forecast is the spot price).