Automatic risk adjustment for profit maximization in renewable dominated short‐term electricity markets. (11th December 2021)

• Record Type: Journal Article
• Authors: Bottieau, Jérémie; Bruninx, Kenneth; Sanjab, Anibal; De Grève, Zacharie; Vallée, François; Toubeau, Jean‐François
• Is Part Of: International transactions on electrical energy systems. Volume 31:Number 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-11
• Subjects: electricity markets -- imbalance settlement -- machine learning -- risk management -- stochastic optimization; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/2050-7038.13152
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: State‐of‐the‐art trading strategies in short‐term electricity markets use risk awareness for reducing, inter alia, their exposure to the volatility of electricity prices. To ensure an optimal balance between risk and profit, risk‐aversion parameters are traditionally fine‐tuned via an offline out‐of‐sample analysis. Such a computationally‐intensive analysis is typically run once, which yields time‐invariant risk policies. Instead, this paper proposes the use of machine learning to select, in an online fashion, optimal risk‐aversion parameters. This novel automatic risk‐tuning approach offers the benefit of continuously adjusting the risk policy based on the dynamically changing market operating conditions. Our approach is tested on two risk‐aversion parameters, that is, the confidence level of the conditional value‐at‐risk and the budget of uncertainty, respectively considering scenario‐based and robust optimization frameworks. A set of performed case studies—focusing on the very short‐term dispatch of a market actor participating in electricity markets—using real‐world market data from the Belgian power system demonstrate the ability of the proposed methodology to outperform traditional offline risk policies. Abstract : The integration of the proposed automatic risk adjustment tool within the decision‐making process of an actor participating in the single price imbalance settlement. This automatic approach aims at continuously adjusting the conservativeness of the decisions arising from risk‐aware optimization techniques. The decision support tool is run sequentially (96 times a day) at the start of each 15 minutes imbalance settlement period. Once the imbalances of all other market players are settled, an out‐of‐sample objective outcome can be generated in an ex‐post analysis by confronting the optimised imbalance position of the market actor at a given risk attitude with the actual realization of the system imbalance.