German Intraday Electricity Market Analysis and Modeling Based on the Limit Order Book

Henry, Martin; Otterson, Scott

doi:10.1109/eem.2018.8469829

Cited by 19 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We take great care to accurately model market mechanisms, the exact clearing algorithm, and the sequence of information. To the best of our knowledge Skajaa et al (2015); Martin and Otterson (2018); Engmark et al (2018); Bertrand and Papavasiliou (2019); Kuppelwieser and Wozabal (2020); Dideriksen et al (2019) are the only other papers that capture the realities of continuous trading in similar detail. In particular, apart from Skajaa et al (2015); Engmark et al (2018); Bertrand and Papavasiliou (2019); Dideriksen et al (2019);Koch (2021), this is the first paper that evaluates a trading strategy based on detailed order book data, which is different from the extant literature that discretizes the trading to 1 min or 15 min brackets to be able to deal with the shear amount of order data (e.g., Glas et al 2019Glas et al , 2020Kath and Ziel 2020).…”

Section: Introductionmentioning

confidence: 99%

Intraday power trading: toward an arms race in weather forecasting?

Kuppelwieser

Wozabal²

2022

OR Spectrum

View full text Add to dashboard Cite

We propose the first speculative weather-based algorithmic trading strategy on a continuous intraday power market. The strategy uses neither production assets nor power demand and generates profits purely based on superior information about aggregate output of weather-dependent renewable production. We use an optimized parametric policy based on state-of-the-art intraday updates of renewable production forecasts and evaluate the resulting decisions out-of-sample for one year of trading based on detailed order book level data for the German market. Our strategies yield significant positive profits, which suggests that intraday power markets are not semi-strong efficient. Furthermore, sizable additional profits could be made using improved forecasts of renewable output, which implies that the quality of forecasts is an important factor for profitable trading strategies. This has the potential to trigger an arms race for more frequent and more accurate forecasts, which would likely lead to increased market efficiency, more reliable price signals, and more liquidity.

show abstract

Section: Introductionmentioning

confidence: 99%

Intraday power trading: toward an arms race in weather forecasting?

Kuppelwieser

Wozabal²

2022

OR Spectrum

View full text Add to dashboard Cite

show abstract

“…In this research we solely focus on the hourly products, which we hereinafter just referred to as products. 4 The ID trade is based on the M7 trading system, thus the trade is executed through an individual Limit Order Book (LOB) for each product, see [32]. In each LOB, open buy/sell orders are collected, sorted according to their highest/lowest price and matched if a new order with a lower sell/higher buy price is submitted.…”

Section: The German Continuous Intraday Electricity Marketmentioning

confidence: 99%

“…Further, there is also a chronological order to execute similar orders. For a detailed analysis, we refer to[32] for more information.…”

mentioning

confidence: 99%

A Reinforcement Learning Approach for the Continuous Electricity Market of Germany: Trading from the Perspective of a Wind Park Operator

Lehna¹,

Hoppmann²,

Heinrich³

et al. 2021

Preprint

View full text Add to dashboard Cite

With the rising extension of renewable energies, the intraday electricity markets have recorded a growing popularity amongst traders as well as electric utilities to cope with the induced volatility of the energy supply. Through their short trading horizon and continuous nature, the intraday markets offer the ability to adjust trading decisions from the day-ahead market or reduce trading risk in a short-term notice. Producers of renewable energies utilize the intraday market to lower their forecast risk, by modifying their provided capacities based on current forecasts. However, the market dynamics are complex due to the fact that the power grids have to remain stable and electricity is only partly storable. Consequently, robust and intelligent trading strategies are required that are capable to operate in the intraday market. In this work, we propose a novel autonomous trading approach based on Deep Reinforcement Learning (DRL) algorithms as a possible solution. For this purpose, we model the intraday trade as a Markov Decision Process (MDP) and employ the Proximal Policy Optimization (PPO) algorithm as our DRL approach. A simulation framework is introduced that enables the trading of the continuous intraday price in a resolution of one minute steps. We test our framework in a case study from the perspective of a wind park operator. We include next to general trade information both price and wind forecasts. On a test scenario of German intraday trading results from 2018, we are able to outperform multiple baselines with at least 45.24% improvement, showing the advantage of the DRL algorithm. However, we also discuss limitations and enhancements of the DRL agent, in order to increase the performance in future works.

show abstract

“…The order book dataset contains all orders submitted to the intraday market from the time the intraday market opens until the market closes. We apply the algorithm by Martin et al [8] to simulate the limit order book (LOB) and reconstruct the bid and offer prices.…”

Section: Data Setsmentioning

confidence: 99%

The Economic Value of Wind Energy Nowcasting

et al. 2020

View full text Add to dashboard Cite

In recent years, environmental concerns resulted in an increase in the use of renewable resources such as wind energy. However, high penetration of the wind power is a challenge due to the intermittency of this resource. In this context, the wind energy forecasting has become a major issue. In particular, for the end users of wind energy forecasts, a critical but often neglected issue is the economic value of the forecast. In this work, we investigate the economic value of forecasting from 30 min to 3 h ahead, also known as nowcasting. Nowcasting is mainly used to inform trading decisions in the intraday market. Two sources of uncertainty affecting wind farm revenues are investigated, namely forecasting errors and price variations. The impact of these uncertainties is assessed for six wind farms and several balancing strategies using market data. Results are compared with the baseline case of no nowcasting and with the idealized case of perfect nowcast. The three settings show significant differences while the impact of the choice of a specific balancing strategy appears minor.

show abstract

German Intraday Electricity Market Analysis and Modeling Based on the Limit Order Book

Cited by 19 publications

References 6 publications

Intraday power trading: toward an arms race in weather forecasting?

Intraday power trading: toward an arms race in weather forecasting?

A Reinforcement Learning Approach for the Continuous Electricity Market of Germany: Trading from the Perspective of a Wind Park Operator

The Economic Value of Wind Energy Nowcasting

Contact Info

Product

Resources

About