Open AccessCost, emission and reserve pondered pre‐dispatch of thermal power generating units coordinated with real coded grey wolf optimisation
Author(s) -
Rameshkumar Jayaraman,
Ganesan Sivarajan,
Abirami Manoharan,
Subramanian Srikrishna
Publication year - 2016
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.2015.0726
Subject(s) - economic dispatch , electric power system , mathematical optimization , power system simulation , operating cost , computer science , population , scheduling (production processes) , total cost , coding (social sciences) , thermal power station , power (physics) , reliability engineering , engineering , mathematics , statistics , physics , demography , quantum mechanics , sociology , economics , microeconomics , waste management
The optimisation of unit commitment (UC) problem in the daily operation and planning of the power system may save the electric utilities millions of dollars per year in production costs. Though many works in the literature uses evolutionary techniques to solve the pre‐dispatch of thermal power generating units, search for optimal generation schedules in order to minimise total operating cost is still an interesting research task. In viewpoint of this, a new population‐based bio‐inspired algorithm namely grey wolf optimisation (GWO) has been implemented to solve thermal generation scheduling problem and the core objectives such as minimisations of total operating cost, emission level and maximisation of reliability are optimised subject to various prevailing constraints. Additionally, real coding scheme is adopted in order to handle the constraints effectively. The effectiveness of real coded GWO (RCGWO) has been verified on standard 10, 20, 40, 60, 80 and 100 unit systems. Further, a practical 38‐unit system has been utilised to show the feasibility of the RCGWO. The simulation results show that RCGWO is very competent in solving the UC problem in comparison to the state‐of‐the‐art methods.