Data-Driven Stochastic Optimal Control for Intraday Electricity Trading by Renewable Producers

TL;DR

This paper introduces a data-driven stochastic control framework for intraday electricity trading by renewable producers, incorporating realistic market features and advanced numerical methods.

Contribution

It develops a novel continuous-time stochastic control model with jump-diffusions and path-dependent costs, solved efficiently using a monotone finite-difference scheme.

Findings

The strategy outperforms the TWAP benchmark in numerical experiments.

Jump intensity and delivery window length significantly influence trading policies.

The approach approaches perfect foresight performance under certain conditions.

Abstract

The rapid growth of weather-dependent renewable generation increases price volatility and imbalance penalty risk in power markets, creating the need for advanced quantitative trading strategies. We develop a data-driven continuous-time stochastic optimal control framework for intraday electricity trading using stochastic differential equations with drift terms ensuring mean reversion to deterministic forecast trajectories. Production follows a Jacobi diffusion, while prices follow an asymmetric jump-diffusion to reflect the heavy-tailed behavior observed in intraday markets. The framework accounts for realistic market features by incorporating gate closure and energy-based imbalance settlement over the delivery window, where the path-dependent imbalance cost is handled by state augmentation to preserve the Markovian structure. The value function is characterized via the dynamic programming principle by a three-stage sequence of two linear Kolmogorov backward equations and a nonlinear Hamilton-Jacobi-Bellman partial integro-differential equation. To solve this problem efficiently, we propose a monotone IMEX finite-difference scheme with operator splitting, semi-implicit linearization, and a differential formulation for the jump operator. Numerical experiments based on German market data indicate that, under the provided forecasts, the computed strategy outperforms the TWAP benchmark and approaches the perfect-foresight benchmark. Sensitivity experiments further show how jump intensity, delivery-window length, and trading horizon affect the trading policy and the resulting profit-and-loss distribution.