Chance-constrained programming approach to stochastic congestion management considering system uncertainties

Considering system uncertainties in developing power system algorithms such as congestion management (CM) are a vital issue in power system analysis and studies. This study proposes a new model for network CM based on chance-constrained programming (CCP), accounting for the power system uncertaintie...

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Vydáno v:IET generation, transmission & distribution Ročník 9; číslo 12; s. 1421 - 1429
Hlavní autoři: Hojjat, Mehrdad, Javidi, Mohammad Hossein
Médium: Journal Article
Jazyk:angličtina
Vydáno: The Institution of Engineering and Technology 04.09.2015
Témata:
CCP
chance‐constrained models
chance‐constrained programming approach
Congestion
equivalent easy‐to‐solve deterministic problem
IEEE 30‐bus test system
Management
Mathematical analysis
network CM
Networks
Optimization
power system algorithms
power system analysis
power system management
power system uncertainty
Programming
stochastic CM
stochastic congestion management
stochastic optimisation problem
stochastic programming
Stochasticity
transmission constraints
Uncertainty
network CM
IEEE 30-bus test system
power system analysis
stochastic programming
stochastic optimisation problem
power system management
chance-constrained models
power system uncertainty
stochastic congestion management
CCP
equivalent easy-to-solve deterministic problem
power system algorithms
transmission constraints
chance-constrained programming approach
stochastic CM
ISSN:1751-8687, 1751-8695
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Shrnutí:Considering system uncertainties in developing power system algorithms such as congestion management (CM) are a vital issue in power system analysis and studies. This study proposes a new model for network CM based on chance-constrained programming (CCP), accounting for the power system uncertainties. In the proposed approach, transmission constraints are taken into account by stochastic rather than deterministic models. The proposed approach considers network uncertainties with a specific level of probability in the optimisation process. Then, single and joint chance-constrained models are implemented on the stochastic CM. Finally, an analytical approach is used to derive the new model of the stochastic CM. In both models, the stochastic optimisation problem is transformed into an equivalent easy-to-solve deterministic problem. Effectiveness of the proposed approach is evaluated by applying the method to the IEEE 30-bus test system. The results show that the proposed CCP model outperforms the existing models as the analytical solving approach applies fewer approximations and moreover, may have less complexity and computational burden in some special situations.
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ISSN:1751-8687
1751-8695
DOI:10.1049/iet-gtd.2014.0376