Unit Commitment Optimization Using Gradient-Genetic Algorithm and Fuzzy Logic Approaches - Complex System Modelling and Control Through Intelligent Soft Computations - page 679

Unit Constraints

￿

Generation limits:
These constraints require that the unit generation be within the minimal, P min
i

-

,

and maximal, P max
i

, generation levels.

P min
i

P ih P max ð 6 Þ

i

Minimum up-time constraint:
The minimum-up time constraint determines the shortest duration a unit must
stay in the generation mode, MUPi i after its transit to this mode.

-

for X

h 1

U ih ¼ 1 U ih MUT i ð 7 Þ

t ¼ h up i

Minimum down-time constraint:
The minimum-down time constraint speci

-

es the shortest duration a unit must
stay in the shut-down mode, MDTi, i , after it is shut down.

for X

h 1

U ih ¼ 0 U ih MDT i ð 8 Þ

t ¼ h down i

These constraints are imposed to prevent wear-and-tear of the apparatus due to
too frequent transits from one mode to another.

Knowing that:
P rh

System spinning reserve at the hth hour,

P dh Amount of the consumed power at the hth hour,

P Lh Total active losses at the hth hour,

P i min , P i max

Minimum and maximum power produced by a generator,

MUT i Continuously on-time of unit i

find the solution to the dynamic unit commitment
problem by dividing the entire planning horizon into t intervals and solving each
optimization problem separately. This is equivalent to solving t static unit commit-
ment problems. Some heuristics are applied then to combine the unit commitment for
each time interval so that they also satisfy minimum-up and minimum-down
requirements. In this formulation, the problem becomes much easier to solve and its
dimension reduces substantially. We propose to test the hypothesis that the error
caused by these assumptions will not be signi

This chapter attempts to

cantly higher compared to the error
from dynamic programming. Here in order to transform the complex nonlinear