Cost, emission and reserve pondered pre-dispatch of thermal power generating units coordinated with real coded grey wolf optimisation

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 gener...

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Bibliographic Details
Published in:IET generation, transmission & distribution Vol. 10; no. 4; pp. 972 - 985
Main Authors: Rameshkumar, Jayaraman, Ganesan, Sivarajan, Abirami, Manoharan, Subramanian, Srikrishna
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 10.03.2016
Subjects:
Algorithms
Emission
Evolutionary
grey systems
Operating costs
optimisation
Optimization
population‐based bio‐inspired algorithm
power generation dispatch
RCGWO
real coded grey wolf optimisation
reliability maximisation
reserve pondered predispatch
Reserves
Searching
thermal generation scheduling problem
thermal power generating units
thermal power stations
Thermoelectricity
unit commitment optimisation problem
thermal power stations
population-based bio-inspired algorithm
power generation dispatch
optimisation
reliability maximisation
grey systems
thermal power generating units
unit commitment optimisation problem
reserve pondered predispatch
real coded grey wolf optimisation
thermal generation scheduling problem
RCGWO
ISSN:1751-8687, 1751-8695
Online Access:Get full text
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Summary: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.
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ISSN:1751-8687
1751-8695
DOI:10.1049/iet-gtd.2015.0726