An integrated model for risk management in electricity trade
Athanasios S. Dagoumas,
Nikolasos E. Koltsaklis and
Ioannis P. Panapakidis
Energy, 2017, vol. 124, issue C, 350-363
This paper presents an integrated model for risk management of electricity traders. It integrates the Unit Commitment (UC) problem, which provides the power generation units' dispatch and the electricity price forecasting of a power system, with artificial neural network (ANN) models, which provide electricity price forecasting of a neighbouring power system by incorporating a clustering algorithm. The integrated model is further extended to estimate the traders' profitability and risk, incorporating risk provisions. The integrated model is applied in bi-directional trading between the Italian and Greek day-ahead electricity markets. The UC and neural network models provide forecasts of the wholesale electricity price in Greece and Italy respectively. The model attributes a confidence level of the price forecasts, depending on the data clustering and the forecasting performance of each model. The integrated model identifies periods with high price margins for trading for each power flow, aligned with a forecasting confidence and a risk level. The integrated model can provide price signals on the profitability of traders and useful insights into the risk of traders.
Keywords: Electricity trade; Electricity price forecasting; Risk; Unit commitment problem; Artificial neural networks; Day-ahead market (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:124:y:2017:i:c:p:350-363
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