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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:International journal of machine learning and cybernetics Ročník 16; číslo 2; s. 1111 - 1127
Hlavní autori: Qin, James Ciyu, Jiang, Rujun, Mo, Huadong, Dong, Daoyi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025
Springer Nature B.V
Predmet:
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 prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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 decomposing joint chance constraints into individual constraints, thus preventing overly conservative solutions and ensuring robust system security. A significant innovation in our method is the use of historical data to form a sample average approximation that directly informs the MIP model, bypassing the need for distributional assumptions to enhance solution robustness. Additionally, we implement a model improvement strategy to reduce the computational burden, making our method more scalable for large-scale power systems. Our approach is validated against benchmark systems, i.e., IEEE 14-, 57- and 118-bus systems, demonstrating superior performance in terms of cost-efficiency and robustness, with lower computational demand compared to existing methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-024-02325-x