electricity pricing;
risk management;
conditional value at risk;
PORTFOLIO OPTIMIZATION;
DERIVATIVES;
ALGORITHM;
EVOLUTION;
MARKETS;
D O I:
10.3390/en13184698
中图分类号:
TE [石油、天然气工业];
TK [能源与动力工程];
学科分类号:
0807 ;
0820 ;
摘要:
In dealing with sharp changes in electricity prices, contract planning is considered as a vital risk management tool for stakeholders in deregulated power markets. In this paper, dynamics of spot prices in Turkish electricity market are analyzed, and predictive performance of several models are compared, i.e., time series models and regime-switching models. Different models for derivative pricing are proposed, and alternative portfolio optimization problems using mean-variance optimization and conditional value at risk (CVaR) are solved. Expected payoff and risk structure for different hedging strategies for a hypothetical electricity company with a given demand are analyzed. Experimental studies show that regime-switching models are able to capture electricity characteristics better than their standard counterparts. In addition, evaluations with various risk management models demonstrate that those models are highly competent in providing an effective risk control practice for electricity markets.
机构:
Cornell Univ, Samuel Curtis Johnson Grad Sch Management, Cornell SC Johnson Coll Business, Sage Hall, Ithaca, NY 14853 USA
Kamkura Corp, 2222 Kalakaua Ave,Suite 1400, Honolulu, HI 96815 USACornell Univ, Samuel Curtis Johnson Grad Sch Management, Cornell SC Johnson Coll Business, Sage Hall, Ithaca, NY 14853 USA