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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Bibliographic Details
Published in:	International journal of machine learning and cybernetics Vol. 16; no. 2; pp. 1111 - 1127
Main Authors:	Qin, James Ciyu, Jiang, Rujun, Mo, Huadong, Dong, Daoyi
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025 Springer Nature B.V
Subjects:	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
Online Access:	Get full text
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