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

2026-04-30

AI Summary

This study addresses the challenges of price volatility and imbalance penalty risk faced by renewable energy producers in intraday electricity markets by proposing a data-driven, continuous-time stochastic dynamic control framework. The approach integrates a mean-reverting drift term, an asymmetric jump-diffusion price model, and path-dependent imbalance costs, preserving Markovian structure through state augmentation. For the first time, a three-stage Kolmogorov–Hamilton–Jacobi–Bellman (HJB) equation system is formulated to derive optimal trading strategies. A monotone IMEX finite difference scheme, combined with operator splitting and a differential representation of the jump operator, is employed to efficiently solve the resulting nonlinear HJB partial integro-differential equation. Numerical experiments using German market data demonstrate that the proposed strategy significantly outperforms the TWAP benchmark and closely approaches the performance of perfect foresight. Sensitivity analyses further reveal the critical influence of jump intensity, delivery window length, and trading horizon on strategy efficacy.

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.

Problem

• intraday electricity trading
• renewable energy
• price volatility
• imbalance penalty
• stochastic optimal control

Innovation

• stochastic optimal control
• jump-diffusion process
• state augmentation
• Hamilton-Jacobi-Bellman equation
• IMEX finite-difference scheme