Open AccessRobust expansion co‐planning of electricity and natural gas infrastructures for multi energy‐hub systems with high penetration of renewable energy sources
Author(s) -
Karamdel Shahab,
Moghaddam Mohsen Parsa
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2018.6005
Subject(s) - renewable energy , mathematical optimization , natural gas , robust optimization , electricity , integer programming , linear programming , electricity generation , wind power , electric power system , computer science , engineering , power (physics) , mathematics , electrical engineering , waste management , physics , quantum mechanics
High penetration of renewable energy sources will cause crucial challenges for future energy systems. This study presents a three‐level model for adaptive robust expansion co‐planning of electricity and natural gas infrastructures in multi‐energy‐hub networks, which is robust against uncertainties of maximum production of wind generation and gas‐fired power plants as well as estimated load levels. The proposed min–max–min model is formulated as a mixed integer linear programming problem. The first level minimises the investment cost of electricity and natural gas infrastructures, the worst possible case is determined through the second level, and the third level minimises the overall operation cost under that condition. To solve this model, the final minimisation problem is replaced by its Karush–Kuhn–Tucker conditions and a two‐level problem is determined. Finally, by using the column and constraint generation algorithm the original problem is decomposed to master and sub‐problems and the optimal solution is derived iteratively. The proposed robust expansion co‐planning model is tested on modified Garver's 6‐hub, modified IEEE RTS 24‐hub, and modified IEEE 118‐hub test systems and numerical results show its effectiveness to cope with uncertainties with regard to control conservativeness of the plan.