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

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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.
Original languageEnglish
Title of host publication2023 IEEE 32nd International Symposium on Industrial Electronics, ISIE 2023 - Proceedings
Place of PublicationPiscataway
Number of pages6
ISBN (Electronic)979-8-3503-9971-4
ISBN (Print)979-8-3503-9972-1
Publication statusPublished - 2023
Event2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE) - Otaniemi campus of Aalto University, Helsinki, Finland
Duration: 19 Jun 202321 Jun 2023
Conference number: 32


Conference2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE)
OtherThe IEEE ISIE 2023 is the 32nd International Symposium on Industrial Electronics (ISIE), focusing on frontier technologies for industries, applications of electronics, controls, communications, instrumentation and computational intelligence. The objectives of the conference are to provide high quality research and professional interactions for the advancement of science, technology, and fellowship.
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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.


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

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