High-impact low-probability (HILP) events; Probabilistic-possibilistic self-scheduling; Risk-aversion model; Uncertainty; Conditional Value-at-Risk; Different risk factors; Low probability; Possibilistic approach; Probability density function (pdf); Risk aversion; Self-scheduling; Energy Engineering and Power Technology; Electrical and Electronic Engineering
Abstract :
[en] In recent years, examining the ruinous consequence of extreme weather events on the power system is one of the most challenging issues that researchers have confronted to. Considerable extreme conditions are generally the missing part of a realistic self-scheduling problem. Considering high-impact low-probability (HILP) events into the model have at least two benefits: first, generation companies (GenCos) can elude from financial disadvantages of upcoming HILP events and then the ISO can better clear energy and reserve markets with a preventive-oriented process to enhance power system resilience. This paper provides a pre-extreme condition self-scheduling for a price-taker generation company with renewable generation units which participates in the day-ahead energy and spinning reserve markets. Uncertainties associated with electricity prices and wind power production are characterized by multiple stochastic scenarios. The stochastic behavior of wind power is presented by using the Beta probability density function (PDF). In order to model the uncertainty of forced outages of generating units due to HILP events and the probability of being called for reserve deployment, a possibilistic approach is proposed. By comparing the generation scheduling under different risk factors and according to the financial disadvantages of HILP events, the conditional value-at-risk (CVaR) risk-averse strategy is considered into the model.
Disciplines :
Electrical & electronics engineering Energy
Author, co-author :
Khaloie, Hooman ; Université de Mons - UMONS > Faculté Polytechnique > Service de Génie Electrique ; Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Abdollahi, Amir; Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Rashidinejad, Masoud; Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Siano, Pierluigi; Department of Management & Innovation Systems, University of Salerno, Fisciano, Italy
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