A fuzzy SMART based dynamic decision making system: a voltage control case study
Abstract
Most real-life decisions involve Dynamic
Decision Making (DDM) that is characterised by
the need to make multiple and interdependent
decisions in an environment that changes as a
function of the decision maker’s actions,
environmental events, or both. Some examples
can be found in management of transportation
networks and in controlling of power systems. To
assist humans in these difficult decision making
scenarios, computer-based decision making
systems have been developed. Most of them rely
on dynamic programming algorithms.
Unfortunately, heavy computational burden
makes them not suitable for application to large
systems and precludes finding a solution in a
limited time which is an important determinant of
the performance. This paper describes the development of a fuzzy
Simple Multi-Attribute Rating Technique
(SMART) based dynamic decision making system
which incorporates the merits of human decision
making mechanisms and operational research
methods to find an optimal solution taking the
least amount of time in a dynamic environment.
To illustrate the proposed framework, we apply it
to a practical voltage control problem in abnormal
scenarios in power systems. The proposed system
(see Figure 1) includes three components: a
‘voltage monitor’ to monitor abnormal voltage
profiles based on a power flow algorithm, an
‘evaluator’ to evaluate the effectiveness of
candidate control actions based on a SMART
algorithm, and a ‘decision maker’ to search an
optimal voltage control schedule based on a fuzzy
linear programming algorithm. We present the test results on a benchmark 9-bus
test power system under a dynamic scenario caused
by load demand variations. The results show that
the proposed approach can quickly find optimal
decisions for maintaining an acceptable voltage
profile in a dynamically changing power
transmission environment. Furthermore, it can
reduce the number of unnecessary control actions in
comparison to a traditional sensitivity based method.
The proposed framework can be easily applied to
other similar dynamic decision making problems,
such as ordering system in industry.
