International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 814
OPTIMIZATION OF UNIT COMMITMENT PROBLEM USING CLASSICAL
SOFT COMPUTING TECHNIQUE (PSO)
Sanjeev Kumar
1
, Harkamal Deep Singh
2
1
M.Tech. Research Scholar, Department of EEE, IKGPTU University, Punjab
2
Assistant professor, Department of EEE, IKGPTU University, Punjab
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Abstract: In electrical power system network of transmission
and distribution, unit commitment is a complicated decision-
making process, which link to the arrangement of generators
over a desire of time periods to satisfy power system load
demand (industrial and agriculture), operational constraints
and system reliability. A classical soft computing (particle
swarm optimization) is a technique used to apply for the
search space of a given problem to discover out the
parameters required to max. or min. a particular objective.
This research paper presents the way out to short term (one
day) unit commitment of thermal electrical power System
using PSO Algorithm.
Keywords: Unit Commitment problem (UCP), Particle Swarm
Optimization (PSO).
I. INTRODUCTION
The unit commitment problem finds out hourly start-up and
shut down schedule as well as power output for the
generating units over an assured time period. The
optimization schedule of units minimizes the total
operational cost while satisfying all system constraints and
load demand of generating units. In a Unit Commitment
Problem, the main aim is to get the minimum total operating
cost by a accurate scheduling of the units ON/OFF status of
the generators subject to the power system and physical
constraints. For a short-term (one day) unit commitment
problem such as daily or hourly arrangement of generators,
the units operator needs to run the model in real-time. The
operator should have instant right to use to information
concerning which generators should be operated when
emergency situations came up or how to-do list around
planned maintenance of generating units. Modern Soft
Computing Techniques Particle Swarm Optimization is
applied to solve the unit commitment problem.
II. PROBLEM FORMULATION
Unit commitment is a multifarious decision making process
because of the many constraints that must not be desecrated
when finding optimal or close to optimal commitment
schedules. Mathematically, the Unit Commitment Problem is
a mixed-integer, non-linear, combinatorial optimization
problem. The optimal solution of above complex UCP in
power system can be obtained by classical soft computing
global search techniques. The objective function of the short
term thermal Unit Commitment Problem is combination of
the fuel cost, start-up cost and shut-down cost of the
generating units and mathematically can be expressed as [1]:
( 1) ( 1)
1 1
[ ( )* * (1 )* * (1 )* ]
H NG
NH i ih ih ih ih ih ih ih ih
h i
Cost FC P U STUC U U SDC U U
(1)
Where,
NH
Cost
is the total operating cost over the scheduled
horizon
( )
i ih
FC P
is the fuel cost function of units
( 1) ih
U
is the ON/OFF status of i
th
unit at
( 1)
th
h
hour.
ih
U
is the ON/OFF status of i
th
unit at h
th
hour.
U is the decision matrix of the
ih
U
variable. for i=1,2,3,........NG.
ih
P
is the generation output of i
th
unit at h
th
hour.
ih
STUC
is the start-up cost of the i
th
generating unit at h
th
hour.
ih
SDC
is the shut-down cost of the i
th
generating unit at the
h
th
hour.
NG is the number of thermal generating units
{0,1}
ih
U
and
( 1)
{0,1}
ih
U
H is the number of hours in the study of time horizon.
(for Short-Term unit Commitment, H is generally taken as 8-
12 Hours or one day. For general unit commitment
scheduling H is taken as 24 hours and for long term unit
commitment, Time horizon H may be taken as one week ,
one month, three month, six month or one year duration.
(a) Fuel Cost, ( )
i ih
FC P
The fuel cost function of the thermal unit
( )
i ih
FC P
is expressed
as a quadratic equation:
2
1
( ) ( ) $/ .
NG
ih i ih i ih i
i
FC P aP bP c hrs
(2)