04-06-2012, 01:20 PM
Solving the Unit Commitment Problem Using Fuzzy Logic
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Abstract
This paper presents an application of the fuzzy
logic to the unit commitment problem in order to find a
generation scheduling such that the total operating cost can be
minimized while satisfying a variety of constraints. The
optimization algorithm employed to solve the unit commitment
problem benefits from the advantages of dynamic
programming and the fuzzy logic approaches in the purpose of
obtaining preferable unit combinations at each load demand.
INTRODUCTION
In all power stations, investment is quite expensive and
the resources needed to operate them are rapidly becoming
sparser. As a result, the focus today is on optimizing the
operating cost of power stations. In the present world,
meeting the power demand as well as optimizing generation
has become a necessity. Unit commitment (UC) in power
system refers to the optimization problem for determining
the on/off states of generating units that minimize the
operating cost subject to variety of constraints for a given
time horizon.
DYNAMIC PROGRAMMING
Dynamic programming is a commonly used technique to
solve the unit commitment problem. It acts as an important
optimization technique with broad application areas where it
decomposes a problem into a series of smaller problems,
solves them, and develops an optimal solution to the
original problem step-by-step. The optimal solution is
developed from the sub problem respectively. In its
fundamental form, the dynamic programming algorithm for
unit commitment problem examines every possible state in
every interval. Some of these states are found to be
infeasible and hence they are rejected instantly. But even,
for an average size utility, a large number of feasible states
will exist and the requirement of execution time will stretch
the capability of even the largest computers.
FUZZY LOGIC IMPLEMENTATION
Fuzzy logic provides not only a meaningful and powerful
representation for measurement of uncertainties but also a
meaningful representation of blurred concept expressed in
normal language. Fuzzy logic is a mathematical theory,
which encompasses the idea of vagueness when defining a
concept or a meaning. For example, there is uncertainty or
fuzziness in expressions like `large` or `small`, since these
expressions are imprecise and relative.
CONCLUSION
The primary objective has been to demonstrate that if the
process of the unit commitment problem can be described
linguistically, then such linguistic descriptions can be
translated to a solution that yields a logical and a feasible
solution to the problem with better results compared to
dynamic programming. This solution to the unit
commitment problem using fuzzy logic is successfully
obtained and the best plan from a set of good feasible
commitment decisions has been accomplished.