Open AccessConditional value at risk‐based stochastic unit commitment considering the uncertainty of wind power generation
Author(s) -
Zhang Yao,
Wang Jianxue,
Ding Tao,
Wang Xifan
Publication year - 2018
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.2017.0509
Subject(s) - cvar , expected shortfall , mathematical optimization , power system simulation , wind power , stochastic programming , benders' decomposition , linear programming , electric power system , computer science , risk management , power (physics) , mathematics , economics , engineering , physics , quantum mechanics , electrical engineering , management
This study presents a risk‐averse stochastic unit commitment (SUC) model which considers the loss‐of‐load risk caused by wind power uncertainty. The expected cost of loss‐of‐load is usually considered in the conventional scenario‐based SUC model. However, even if the expected risk of loss‐of‐load induced by all wind scenarios is low, the risk induced by some extreme scenarios can be very high. Thus, there is a strong will to better control the risk in these cases with high costs but low probabilities. In this study, the management of loss‐of‐load risk in worst scenarios is addressed by the conditional value‐at‐risk (CVaR). The proposed SUC model is built in a mixed‐integer linear programming formulation and finally solved by a modified Benders decomposition algorithm with two enhancement strategies (Jensen's inequality and multiple cuts generated from all subproblems). Case studies demonstrate that the loss‐of‐load cost in extreme scenarios decreases after the inclusion of CVaR in the proposed SUC model. The proposed model can also provide multiple unit commitment schedules with different levels of loss‐of‐load risk. Using enhancement strategies in Benders decomposition drastically reduces the total number of iterations, verifying the effectiveness of the modified Benders decomposition algorithm.