Use of chance-constrained programming to account for stochastic variation in theA-matrix of large-scale linear programs: A forestry application

1991 ◽  
Vol 31 (1) ◽  
pp. 511-526 ◽  
Author(s):  
James B. Pickens ◽  
John G. Hof ◽  
Brian M. Kent
Keyword(s):  
Large Scale ◽  
Chance Constrained Programming ◽  
Linear Programs ◽  
Stochastic Variation ◽  
Constrained Programming

scholarly journals Participation of an EV Aggregator in the Reserve Market through Chance-Constrained Optimization

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4071
Author(s):  
António Sérgio Faria ◽  
Tiago Soares ◽  
Tiago Sousa ◽  
Manuel A. Matos
Keyword(s):  
Large Scale ◽  
Uncertainty Budget ◽  
Fast Response ◽  
Frequency Regulation ◽  
Test Case ◽  
Chance Constrained Programming ◽  
Risk Levels ◽  
Constrained Model ◽  
Constrained Programming ◽  
Reserve Market

The adoption of Electric Vehicles (EVs) will revolutionize the storage capacity in the power system and, therefore, will contribute to mitigate the uncertainty of renewable generation. In addition, EVs have fast response capabilities and are suitable for frequency regulation, which is essential for the proliferation of intermittent renewable sources. To this end, EV aggregators will arise as a market representative party on behalf of EVs. Thus, this player will be responsible for supplying the power needed to charge EVs, as well as offering their flexibility to support the system. The main goal of EV aggregators is to manage the potential participation of EVs in the reserve market, accounting for their charging and travel needs. This work follows this trend by conceiving a chance-constrained model able to optimize EVs participation in the reserve market, taking into account the uncertain behavior of EVs and their charging needs. The proposed model, includes penalties in the event of a failure in the provision of upward or downward reserve. Therefore, stochastic and chance-constrained programming are used to handle the uncertainty of a small fleet of EVs and the risk profile of the EV aggregator. Two different relaxation approaches, i.e., Big-M and McCormick, of the chance-constrained model are tested and validated for different number of scenarios and risk levels, based on an actual test case in Denmark with actual driving patterns. As a final remark, the McCormick relaxation presents better performance when the uncertainty budget increases, which is appropriated for large-scale problems.

Chance constrained programming with some non-normal continuous random variables

OPSEARCH ◽  
2020 ◽  
Vol 57 (4) ◽  
pp. 1281-1298
Author(s):  
D. K. Mohanty ◽  
Avik Pradhan ◽  
M. P. Biswal
Keyword(s):  
Random Variables ◽  
Chance Constrained Programming ◽  
Constrained Programming

A novel model for multi-criteria assessment based on BWM and possibilistic chance-constrained programming

2021 ◽  
pp. 107287
Author(s):  
Maghsoud Amiri ◽  
Mohammad Hashemi-Tabatabaei ◽  
Mohammad Ghahremanloo ◽  
Mehdi Keshavarz-Ghorabaee ◽  
Edmundas Kazimieras Zavadskas ◽  
...  
Keyword(s):  
Chance Constrained Programming ◽  
Constrained Programming ◽  
Novel Model
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