Open AccessModelling the operation strategies of storages and hydro resources in adequacy analysis of power systems in presence of wind farms
Author(s) -
Shariatkhah MohammadHossein,
Haghifam MahmoudReza,
Chicco Gianfranco,
ParsaMoghaddam Mohsen
Publication year - 2016
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2015.0443
Subject(s) - reliability engineering , reliability (semiconductor) , energy storage , markov chain , markov process , electric power system , computer data storage , computer science , markov model , reduction (mathematics) , wind power , time domain , table (database) , power (physics) , engineering , mathematics , electrical engineering , data mining , physics , quantum mechanics , statistics , geometry , machine learning , computer vision , operating system
This study addresses the adequacy of a generating system considering the impact of operation strategies of storages and hydro energy resources. It is assumed that the installed storage capacity of a system can be assigned for two purposes: first, economical operation which adopts storage to shift electric energy and smooth the load curve; and the second, reliability‐based operation, which deploys storage to avoid load curtailments. The economical operation strategy is represented by starting from time domain data and using the load curve modification method to synthesise the overall impact of storage. The reliability‐based operation is modelled with a new method based on Markov chain model that considers the dynamic behaviour of storage during time. Moreover, a novel state reduction method is presented for decreasing the number of Markov states in applications to large systems. The effectiveness of the presented methods is evaluated by running several simulation scenarios with results presented on three test systems. The results demonstrate that evaluating the system reliability in the presence of energy storage by considering the frequency and duration of system states is more effective than using methods based on the Capacity Outage Probability Table (COPT).