Nils Löhndorf | Searcharxiv

arXiv2026

High-frequency intraday trading for battery storages

Maximizing revenue for grid-scale battery energy storage systems in continuous intraday electricity markets requires strategies that are able to seize trading opportunities as soon as new information arrives. This paper introduces and evaluates a computationally efficient, high-frequency implementation of the rolling intrinsic trading strategy for battery energy storage systems on the intraday market for power. By combining the established rolling intrinsic logic with a full limit order book representation and a fast dynamic programming approximation, our method explicitly considers the continuously updated list of buy and sell offers, market rules, a linear approximation of degradation, and other technical parameters at a millisecond resolution. The standard rolling intrinsic strategy is adapted for continuous intraday electricity markets and solved using a dynamic programming approximation that is two to three orders of magnitude faster than an exact mixed-integer linear programming solution. A detailed backtest over a full year of German order book data demonstrates that the proposed dynamic programming formulation does not reduce trading profits and enables the policy to react to every relevant order book update, enabling realistic rapid backtesting. Our results show the significant revenue potential of high-frequency trading: our policy earns 58% more than when re-optimizing only once every hour and 14% more than when re-optimizing once per minute, highlighting that profits critically depend on trading speed. Furthermore, we leverage the speed of our algorithm to train a parametric extension of the rolling intrinsic, increasing yearly revenue by 8.4% out of sample.

q-fin.TR↗

arXiv2024

Valuation of Power Purchase Agreements for Corporate Renewable Energy Procurement

Corporate renewable power purchase agreements (PPAs) are long-term contracts that enable companies to source renewable energy without having to develop and operate their own capacities. Typically, producers and consumers agree on a fixed per-unit price at which power is purchased. The value of the PPA to the buyer depends on the so called capture price defined as the difference between this fixed price and the market value of the produced volume during the duration of the contract. To model the capture price, practitioners often use either fundamental or statistical approaches to model future market prices, which both have their inherent limitations. We propose a new approach that blends the logic of fundamental electricity market models with statistical learning techniques. In particular, we use regularized inverse optimization in a quadratic fundamental bottom-up model of the power market to estimate the marginal costs of different technologies as a parametric function of exogenous factors. We compare the out-of-sample performance in forecasting the capture price using market data from three European countries and demonstrate that our approach outperforms established statistical learning benchmarks. We then discuss the case of a photovoltaic plant in Spain to illustrate how to use the model to value a PPA from the buyer's perspective.

econ.GN↗