Closed-loop simulation testing of a probabilistic DR framework for Day Ahead Market participation applied to Battery Energy Storage Systems

Closed-loop simulation testing of a probabilistic DR framework for Day Ahead Market participation applied to Battery Energy Storage Systems

van der Heijden, T.J.T. (TU Delft Water Resources)
Palensky, P. (TU Delft Electrical Sustainable Energy)
van de Giesen, N.C. (TU Delft Water Resources)
Abraham, E. (TU Delft Water Resources)

Electrical Sustainable Energy

2023

In this manuscript, we test the operational performance decrease of a probabilistic framework for Demand Response (DR). We use Day Ahead Market (DAM) price scenarios generated by a Combined Quantile Regression Deep Neural Network (CQR-DNN) and a Non-parametric Bayesian Network (NPBN) to maximise profit of a Battery Energy Storage System (BESS) participating on the DAM for energy arbitrage. We apply the generated forecast time series to a stochastic Model Predictive Control (MPC), and compare the performance using a point and perfect forecast. For the probabilistic forecasts, we test two control strategies; 1) minimising the Conditional Value at Risk (CVaR) for making costs, and 2) minimising the expected value of the cost. We apply the MPC in a closed-loop simulation setting and perform a sensitivity analysis of the profit by changing the ratio between battery capacity and the max power, the cluster reduction method, and the number of scenarios used by the MPC. We show that the proposed framework works, but the approach does not increase profit compared to a deterministic point forecast. This can possibly be explained by the deterministic forecast capturing the shape of the price curve with less noise than a probabilistic forecast without enough scenarios. We show that the value of a good forecast becomes smaller as the charging time of the battery becomes larger, due to the battery being unable to exploit small price differences optimally.

Demand Response
probabilistic forecasting
scenario generation
stochastic programming
battery energy storage systems
day ahead market

http://resolver.tudelft.nl/uuid:a9e4a3de-3ad2-4e17-8889-0d670d6f4caa

https://doi.org/10.1109/ISIE51358.2023.10227921

IEEE, Piscataway

2024-03-01

979-8-3503-9972-1

2023 IEEE 32nd International Symposium on Industrial Electronics, ISIE 2023 - Proceedings

2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE), 2023-06-19 → 2023-06-21, Otaniemi campus of Aalto University, Helsinki, Finland

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 T.J.T. van der Heijden, P. Palensky, N.C. van de Giesen, E. Abraham