Open AccessFast security and risk constrained probabilistic unit commitment method using triangular approximate distribution model of wind generators
Author(s) -
Yu Peng,
Venkatesh Bala
Publication year - 2014
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2013.0766
Subject(s) - power system simulation , probabilistic logic , wind power , mathematical optimization , computer science , unit (ring theory) , reliability engineering , mathematics , engineering , electrical engineering , electric power system , artificial intelligence , physics , power (physics) , quantum mechanics , mathematics education
Wind energy is intermittent and uncertain. This uncertainty creates additional risk in the day‐ahead 24‐h dispatch schedule. Wind speed can be forecasted for the next 24‐h and hourly power forecasts can be best described using probabilistic models. Security and risk constrained probabilistic unit commitment (SRCPUC) algorithms considering probabilistic forecast models of wind power can be used to optimally schedule conventional and wind generation to minimise the total cost and minimise risk. However, inclusion of non‐linear probabilistic forecast models in a SRCPUC algorithm is computationally very challenging. In this study, the proposed SRCPUC algorithm uses a triangular approximate distribution (TAD) model to probabilistically represent power output of wind generator. The TAD model quantifies hourly potential risk because of expected energy not served (EENS) from uncertain wind power. Reserves are optimally scheduled to counter EENS. Total energy cost, reserve cost and risk from EENS are minimised in the proposed SRCPUC algorithm. The proposed algorithm is implemented on 6‐bus and 118‐bus IEEE systems. The results are compared with classical enumeration technique. Significant benefits in computing time (more than 500 times faster) are seen while the numerical results are observed to be highly accurate.