A data-driven mixed integer programming approach for joint chance-constrained optimal power flow under uncertainty

This paper introduces a novel mixed integer programming (MIP) reformulation for the joint chance-constrained optimal power flow problem under uncertain load and renewable energy generation. Unlike traditional models, our approach incorporates a comprehensive evaluation of system-wide risk without de...

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Vydáno v:	International journal of machine learning and cybernetics Ročník 16; číslo 2; s. 1111 - 1127
Hlavní autoři:	Qin, James Ciyu, Jiang, Rujun, Mo, Huadong, Dong, Daoyi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025 Springer Nature B.V
Témata:	Algorithms Alternative energy sources Artificial Intelligence Complex Systems Computational Intelligence Constraints Control Costs Efficiency Engineering Integer programming Integrated approach Linear programming Mechatronics Mixed integer Original Article Pattern Recognition Power flow Probability Renewable resources Robotics Robustness (mathematics) Systems Biology Systems stability Optimal power flow Mixed integer programming Chance-constrained optimisation
ISSN:	1868-8071, 1868-808X
On-line přístup:	Získat plný text
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