Abstract:
The Unit Commitment Problem (UCP) is a complex engineering optimization problem
of electrical power generation domain. Determining the scheduling for economic
consumption of production assets over a specific period of time is the premier objective
of UCP. This paper presents a take on solving UCP with Binary Slime Mould
Algorithm (BSMA) optimizer. SMA is a recently developed nature-inspired stochastic
optimization technique that imitates the selective vegetative growth of slime mould
while foraging. A binarized SMA with constraint handling through heuristic adjustment
is proposed and implemented to unit commitment problem to generate optimal
scheduling for available power resources. Implementing modern heuristic techniques ensures
an efficient solution to this non-linear, non-convex and complex constraint driven
optimization problem for any number of generating units with maximum profit. To test
BSMA as a UCP optimizer, IEEE standard power generating systems ranging from 10
to 100 units along with IEEE 118-bus system are used and the results are then compared
with existing classical, evolutionary and hybridized approaches. The comparison
reveals superiority of BSMA over all the classical and evolutionary approaches and most
of the hybridized methods that are considered in this paper in terms of total cost and
convergence characteristics.
Description:
Supervised by
Dr. Ashik Ahmed,
Supervisor and Professor,
Electrical and Electronic Engineering Department,
Islamic University of Technology (IUT)
Boardbazar, Gazipur-1704.
