Stochastic optimization of trading strategies in sequential electricity markets

Kraft, Emil; Russo, Marianna; Keles, Dogan; Bertsch, Valentin

doi:10.1016/j.ejor.2022.10.040

Cited by 18 publications

(6 citation statements)

References 31 publications

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“…The detailed modeling of the day-ahead, intraday and balancing power market leads to a complex problem with a large scenario tree that is additionally linked to an often no less complicated energy system model. Thus, in case studies, the complexity is usually reduced to keep the problems manageable [17,24]. However, the methods mentioned remain complicated to handle, for example, due to the modeling of scenario trees, discretization of bid prices or the use of possible reduction methods.…”

Section: Contribution Of This Workmentioning

confidence: 99%

“…An even more detailed modeling of the balancing power market taking into account the relevant market rules for capacity and work can be found in [16]. In addition to the aforementioned works with the coupled marketing on the day-ahead and balancing power market, there are more complex models with the additional consideration of the intraday market [12,17,18]. On the energy system side, the developed methods are applied on hydro power plants [19,20], a virtual power plant [21,22], a portfolio with biogas power plants and photovoltaic systems [17], combined heat and power plants [13,23], a cement mill [16], energy storages [24], or a distributed multi-energy system [12].…”

Section: Introduction 11 Multi Market Participationmentioning

confidence: 99%

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Simplified Dispatching Method for Unlocking Energy Flexibilities of Decentralized Energy Systems for the Day‑ahead and Balancing Power Market

Wasserfall,

Ouso,

Kirschbaum

2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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The expansion of renewable energies in the power supply and the shutdown of conventional power plants can only succeed with an advancing flexibilization of the energy system. Decentralized energy systems, such as the energy supply system of a chemical site, can contribute to this, by unlocking the still unused potential for flexibilization. In order to lower the barriers to market participation, user-friendly and simple methods for the economic evaluation of flexibility with regard to the day-ahead and balancing power market are necessary. Accordingly, in this work a simplified dispatching method considering the day-ahead and the balancing power market in Germany for the mentioned distributed energy systems is developed using mixed-integer linear programming. Linear constraints commonly used in dispatching problems, such as energy balances, conversion efficiencies, and technical constraints, are coupled with new constraints representing the participation in the day-ahead and balancing power market. For the balancing power market the reserve of capacity and the balancing of energy with the uncertainty of calls are taken into account. As a result, the method can be used for day-ahead dispatching by considering the day-ahead energy market and the balancing power market simultaneously. Because the developed simplified method has low computation times compared to stochastic optimization, for example, it can additionally be applied for hourly resolved annual input data. These evaluations can support strategic planning by determining the economic value of flexibility in the event of market participation. Thus, the results form a starting point for further decisions to unlock unused flexibility.

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Section: Contribution Of This Workmentioning

confidence: 99%

Section: Introduction 11 Multi Market Participationmentioning

confidence: 99%

Simplified Dispatching Method for Unlocking Energy Flexibilities of Decentralized Energy Systems for the Day‑ahead and Balancing Power Market

Wasserfall,

Ouso,

Kirschbaum

2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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“…Our model allows for including large ensembles of agents, which can help to prove his assertions empirically on a larger market data set. Kraft et al [3] developed stochastic auction market modeling ideas in terms of the electricity (commodity) market and focused on the behavior of different market participants, taking into account their risk aversion. This model allows for a multi-stage stochastic optimization approach to determine optimal bids in financial commodity markets.…”

Section: Relevant Research and Progressmentioning

confidence: 99%

Model of stochastic auctions using level market index

Maksim,

Alexei,

Dmitry

et al. 2023

Financ. Stat. J.

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The following research paper is devoted to the complex topic of modeling stochastic financial markets using the example of auction markets. The presented model for market makers’ behavior on stochastic auction markets contributes practically to the field of studying portfolio optimization, risk management, market participants’ balance processes, and prediction problems via cutting-edge machine learning and statistics approaches. The reliability of the given model is proved practically with the help of modern machine learning methods of validation, namely, combinatorial splits. A client-server model for remote simulation was implemented, as well as interpreted language in C++. XGBoost, Catboost, LSTM, NN Ensemble, and H2O Auto-ML models were considered in the course of building the decision model. Hyperparameters were obtained via Optuna. Besides that, the developed model was backtested on historical data of different financial assets, starting with stocks and ending with commodity prices and foreign exchange rates. Within all models, positive Sharpe ratios have been obtained, which indicates the robustness of the model. The paper offers a valuable framework for market maker decision-making stochastic modeling, examining its pricing mechanisms and financial risk management as crucial for exchanges, funds, and other financial institutions, which makes it relevant in the context of the current dynamics of the development of financial markets and the increase in trading volumes.

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“…Path-based forecasts allow to price short-term asset flexibility using Monte-Carlo option valuation. Additionally, the explicit modelling of the volatility provides a starting point to introduce time-varying volatility to stochastic optimization models for market making, position solving and sequential bidding (von Luckner et al, 2017;Glas et al, 2020;Aïd et al, 2016;Kath and Ziel, 2020;Kraft et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution

Hirsch¹,

Ziel²

2024

The Energy Journal

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During the last years, European intraday power markets have gained importance for balancing forecast errors due to the rising volumes of intermittent renewable generation. However, compared to day-ahead markets, the drivers for the intraday price process are still sparsely researched. In this paper, we propose a modelling strategy for the location, shape and scale parameters of the return distribution in intraday markets, based on fundamental variables. We consider wind and solar forecasts and their intraday updates, outages, price information and a novel measure for the shape of the merit-order, derived from spot auction curves as explanatory variables. We validate our modelling by simulating price paths and compare the probabilistic forecasting performance of our model to benchmark models in a forecasting study for the German market. The approach yields significant improvements in the forecasting performance, especially in the tails of the distribution. At the same time, we are able to derive the contribution of the driving variables. We find that, apart from the first lag of the price changes, none of our fundamental variables have explanatory power for the expected value of the intraday returns. This implies weak-form market efficiency as renewable forecast changes and outage information seems to be priced in by the market. We find that the volatility is driven by the merit-order regime, the time to delivery and the closure of cross-border order books. The tail of the distribution is mainly influenced by past price differences and trading activity. Our approach is directly transferable to other continuous intraday markets in Europe.

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Stochastic optimization of trading strategies in sequential electricity markets

Cited by 18 publications

References 31 publications

Simplified Dispatching Method for Unlocking Energy Flexibilities of Decentralized Energy Systems for the Day‑ahead and Balancing Power Market

Simplified Dispatching Method for Unlocking Energy Flexibilities of Decentralized Energy Systems for the Day‑ahead and Balancing Power Market

Model of stochastic auctions using level market index

Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution

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