Open AccessElectricity market short‐term risk management via risk‐adjusted probability measures
Author(s) -
Jovanović Nenad,
GarcíaGonzález Javier,
Barquín Julián,
Cerisola Santiago
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.1731
Subject(s) - term (time) , electricity market , risk management , electricity , risk analysis (engineering) , econometrics , computer science , business , mathematics , engineering , electrical engineering , physics , quantum mechanics , finance
This study presents an iterative algorithm for modelling the mean‐risk model with the conditional value at risk (CVaR). The algorithm is based on the Lagrangian relaxation decomposition, and its main advantage is that it allows removing the coupling between the scenarios due to the constraints used to model the risk. At each stage of the algorithm, a risk‐neutral stochastic optimisation problem is solved with the risk‐adjusted probabilities that substitute the original ones. The study presents the application of the proposed iterative CVaR algorithm to two different short‐term problems where the decision makers are exposed to a high volatility of electricity spot market prices. In the first problem, a time horizon of 1 week is taken into account and a future physical contract is employed as a hedging mechanism. The second problem includes a very detailed formulation of the unit commitment problem. The numerical application is based on realistic data of the Iberian electricity market, where the algorithm has shown a good performance in terms of accuracy and computational time. In addition, this study provides a criterion for selecting the value of the parameters used to implement the CVaR model.