Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.02830

On BESS-Backed Trading on the Continuous Intraday Electricity Market

Leo Semmelmann·Runyao Yu·Joseph Cary·Derek Bunn
View original source ↗Read source document ↗Export metadata ↓

Abstract

Battery energy storage systems (BESS) on the continuous intraday market (IDC) often trade according to a rolling intrinsic algorithm, a myopic strategy which repeatedly opens and unwinds positions according to current prices throughout the trading session, before dispatching as delivery approaches. This paper introduces a novel trading strategy for storage, in which forecast-driven round trip trading takes priority and the BESS supplies a second route for closing out positions that the market would otherwise close at a distressed price. The agent uses price quantile forecasts to open positions on the IDC, and closes these positions closer to delivery. The market can move against these trades, and the BESS intervenes only when a position would have to be settled at a price that the forecast predicts to be highly improbable, absorbing or serving that volume physically and restoring its state of charge at ordinary prices afterwards. The position itself remains loss-making; what the asset changes is the price at which it is closed, by transferring the terminal settlement price through time. Four quantile forecasting models of increasing complexity supply the thresholds, and all strategies are backtested on realised EPEX SPOT transactions for the German market area in 2024. A 40 MWh BESS in this role earns EUR 2.06m against EUR 1.77m for a rolling intrinsic benchmark and EUR 1.37m for a perfect-foresight day-ahead benchmark, while consuming 167 instead of 365 available equivalent full cycles. Hence, the novel backstop strategy yields higher profits, while using the underlying BESS less, leaving capacity for other trading opportunities.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Leo Semmelmann, Runyao Yu, Joseph Cary, Derek Bunn. 2026-10-02. On BESS-Backed Trading on the Continuous Intraday Electricity Market. https://arxiv.org/abs/2610.02830

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Residual Bias Compensation Dual Extended Kalman Filter for Physics-Based SOC Estimation in Lithium Iron Phosphate Batteries

This paper addresses state of charge (SOC) estimation for lithium iron phosphate (LFP) batteries, where the relatively flat open-circuit voltage (OCV-SOC) characteristic reduces observability. A residual bias compensation dual extended Kalman filter (RBC-DEKF) is developed. Unlike conventional bias compensation methods that treat the bias as an augmented state within a single filter, the proposed dual-filter structure decouples residual bias estimation from electrochemical state estimation. One EKF estimates the system states of a control-oriented parameter-grouped single particle model with thermal effects, while the other EKF estimates a residual bias that continuously corrects the voltage observation equation, thereby refining the model-predicted voltage in real time. Unlike bias-augmented single-filter schemes that enlarge the covariance coupling, the decoupled bias estimator refines the voltage observation without perturbing electrochemical state dynamics. Validation is conducted on an LFP cell from a public dataset under three representative operating conditions: US06 at 0 degC, DST at 25 degC, and FUDS at 50 degC. Compared with a conventional EKF using the same model and identical state filter settings, the proposed method reduces the average SOC RMSE from 3.75% to 0.20% and the voltage RMSE between the filtered model voltage and the measured voltage from 32.8 mV to 0.8 mV. The improvement is most evident in the mid-SOC range where the OCV-SOC curve is flat, confirming that residual bias compensation significantly enhances accuracy for model-based SOC estimation of LFP batteries across a wide temperature range.

eess.SY↗
arXiv2026

Distributed Coordination Algorithms with Efficient Communication for Open Multi-Agent Systems with Dynamic Communication Links and Processing Delays: Extended Version

In this paper we focus on the distributed quantized average consensus problem in open multi-agent systems consisting of dynamic directed communication links among active nodes. We propose three communication-efficient distributed algorithms designed for different scenarios. Our first algorithm solves the quantized averaging problem over the currently active node set under finite network openness (i.e., when the active set eventually stabilizes). Our second algorithm extends the aforementioned approach for the case where nodes suffer from arbitrary bounded processing delays. Our third algorithm operates over indefinitely open multi-agent networks with dynamic communication links (i.e., with continuous node arrivals and departures), computing the average that incorporates both active and historically active nodes. We analyze our algorithms' operation, establish their correctness, and present novel necessary and sufficient topological conditions ensuring their finite-time convergence. Numerical simulations on distributed sensor fusion for environmental monitoring demonstrate fast finite-time convergence and robustness across varying network sizes, departure/arrival rates, and processing delays. Finally, it is shown that our proposed algorithms compare favorably to algorithms in the existing literature.

eess.SY↗
arXiv2026

Toward Single-Step MPPI via Differentiable Predictive Control

Model predictive path integral (MPPI) is a sampling-based method for solving complex model predictive control (MPC) problems, but its real-time implementation is challenged by computational and sample requirements that grow with the prediction horizon, as well as sensitivity to manually tuned sampling parameters. To address these issues, we propose Step-MPPI, a framework that learns a sampling distribution and MPPI parameters for efficient single-step lookahead MPPI. Specifically, a neural network parameterizes the MPPI sampling mean and covariance at each time step, while the single-step cost weights and temperature are jointly learned in a self-supervised manner over long horizons using the MPC cost, constraint penalties, and maximum-entropy regularization. By embedding long-horizon objectives into the learned cost and sampling policy, Step-MPPI achieves the foresight of multi-step optimization with the millisecond-level latency of single-step lookahead. We demonstrate its efficiency across challenging tasks involving high-dimensional systems and/or long control horizons.

eess.SY↗