Bertsch, Valentin

Nitsch, Felix

Schimeczek, Christoph

Institute of Transport Research:Publications

Agent-based modelling of market competition among flexibility options using Temporal Fusion Transformer networksFelix Nitsch1,*, Christoph Schimeczek1, Valentin Bertsch2> Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Chart 11 Department of Energy Systems AnalysisInstitute of Networked Energy Systems German Aerospace Center (DLR)Curiestraße 4, 70563 Stuttgart, Germany* corresponding author: Felix.Nitsch@dlr.de +49 711 6862-88652 Chair of Energy Systems & Energy EconomicsRuhr-Universität Bochum Universitätsstraße 150, 44801 Bochum, Germany> Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Chart 2PARIS AGREEMENT – THE ENERGY TRANSITION IS SETCredits: BMU/Sascha Hilgers > Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Folie 3AMIRISAgent-based Market model for the Investigation of Renewable and Integrated energy SystemsModel− Electricity market simulation− Focus: Germany & Europe− To be Open Source in 2021Agents− Conventional Plants− Renewable Plants− Traders− Flexibilities− Markets− Policy− ForecastingCalculates− Electricity prices− Plant dispatch− Market values− Emissions− System costs> Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Folie 4Status Quo: Price forecasting in AMIRIS1. Power plant operators send future bids to forecast agent2. Forecast agent calculates forecasted price3. Forecasts are sent to one Flex-option agent4. Flex-option agent optimizes its operational strategy5. All traders send final bids to Energy Exchange 6. Energy Exchange calculates final electricity priceFinal and forecasted price difference caused by flex-option agent actions(i.e. charging → „higher price“, discharging → „lower price“)Challenge:Multiple flex-option agents mutually distort their forecasts due to their competitive actions→ Significant impacts on the accuracy of the price forecastPPOsFCFlexEEXΔ FlexFlex> Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Folie 5First attemptAimCentral forecast agent is learning bidding behaviour of flexibility options and their impacts on pricesArchitecture• 1. Feed-forward model (FF)• 2. Long-short term memory model (LSTM)• Inputs:• Previous prices• Previous residual load• Output: • Price forecast for next 3 hours> Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Folie 6First Attempt’s ResultsResult• Successful proof of concept• Accurate predictions only up to t+3 time stepsBut…1. t+3 time steps not enough to build operational strategy2. Uncertainties of prediction values unknown3. Inconvenient two-staged training process (1. FF → 2. LSTM)4. “Black box" characteristic of ML predictionNitsch, F. and Schimeczek C. (2020). Model in model: Electricity price forecasts in agent-based energy system simulations. INREC Conference. https://elib.dlr.de/136017/> Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Folie 7Temporal Fusion Transformers (TFT)• Novel approach (Lim et al. (2020))• Attention-based architecture• Significant performance improvements over existing benchmarks (see Table)Main features• Gating mechanismsskip over unused components• Variable selection networksselect relevant input variables• Static covariate encoders integrate static features• Temporal processing learn long- & short-term relationshipsLim, B., Arik, S. O., Loeff, N., & Pfister, T. (2020). Temporal fusion transformers for interpretable multi-horizon time series forecasting. arXiv preprint arXiv:1912.09363.> Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Folie 8TFT Forecasting Conceptsadapted from Lim et al. (2020)e.g. prices, loade.g. load forecast, calendar informatione.g. region, marketi.e. present timee.g. next 24 hours including pre-defined quantilese.g. previous 24 hours which where observedEncoder Decoder> Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Folie 9Workflow• 1. Hyperparameter scan• Number of hidden layers• Dropout• Learning rate• etc.• 2. Training• Based on AMIRIS simulation results• Scenario I: no flexibility options (easy to forecast, electric load  price)• Scenario II: extensive flexibility options (challenging to forecast, electric load ⇎ price)PPOsFCEEXΔScenario IPPOsFCFlexEEXΔScenario II> Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Folie 10Price Prediction – Scenario I – without flexibility optionEncoder 24h – Prediction 6h and 24hFig.: Plotting of observed prices (blue) and predictions (orange) including predicted quartiles in EUR/MWh in Scenario I> Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Folie 11Price Prediction – Scenario II – with extensive flexibility optionsEncoder 24h – Prediction 6h and 24hFig.: Plotting of observed prices (blue) and predictions (orange) including predicted quartiles in EUR/MWh in Scenario II> Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Folie 12Variable importance – Scenario IIEncoder 24h6h Prediction 24h Prediction> Agent-based modelling of market competition using TFT > Nitsch et al. (2021) > EURO 2021: Modelling of Energy Markets > 14th July 2021DLR.de  •  Folie 13Conclusion1. Accurate predictions only up to t+3 time steps► High quality forecasts for 24 time steps2. No way to consider uncertainties regarding the prediction values► Prediction uncertainty estimates3. Inconvenient two-staged training process (1. FF → 2. LSTM)► Very convenient training using pytorch, optuna and TFT implementation by Jan Beitner4. “Black box" characteristic of ML prediction► “Attention" feature: identify important input variablesOutlooko Generalize training data for scenarioso Transfer ML models from Python to Java (AMIRIS)o Develop strategies for flex-option planning to utilize uncertainty informationContact: Felix Nitsch felix.nitsch@dlr.de German Aerospace Center  | Institute of Networked Energy Systems | Energy Systems Analysis 

Agent-based modelling of market competition among flexibility options using Temporal Fusion Transformer networks

https://elib.dlr.de/143301/1/EURO21_FelixNITSCH.pdf

Agent-based modelling of market competition among flexibility options using Temporal Fusion Transformer networks

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