Page 260 - Decision Making Applications in Modern Power Systems

Distributed generation in deregulated Chapter | 9 225

and fluctuations of load profiles along the day. Accordingly, the HC calcu-
lations should not be a deterministic problem with no randomness involved.
However, it should be solved as a probabilistic problem, where uncertainty
levels are considered.
To manage these various uncertainties, decision-making techniques have
been examined in the presence of uncertainties in many works. Soroudi et al.
[5] presented a comprehensive overview of the various uncertainty handling
approaches in energy systems. The authors categorized the uncertainty of
electrical parameters into two sections as follows:
1. uncertainty in technical parameters, such as load changes, DG output
fluctuations, and generation outages and
2. uncertainty in economic parameters, such as the variations in inflation
rates, unemployment rate, and gross domestic product.
Afterward, a comprehensive review of the various decision-making
techniques was presented and mainly categorized into six categories as
follows:
1. Probabilistic approach: where the input parameters of the problem under
study are random data with an identified probability distribution function.
The commonly known probabilistic approaches or uncertainty handling
are Monte Carlo simulation (MCS), point estimate method, and scenario-
based approach.
2. Possibilistic approach: depends on the fuzzy sets where the input para-
meters are represented using a membership function.
3. Information gap decision theory: where the input parameters are grouped
into two groups to represent the known parameters and the parameters
that are essential to be known.
4. Hybrid possibilistic probabilistic: where the input parameters are a mix
of both possibilistic and probabilistic approaches.
5. Robust optimization: in this approach, the achieved decisions are taken
based on solving an optimization problem considering the worst-case sce-
nario of a given uncertain data set.
6. Interval analysis: where the input parameters are assumed to be taken
from a known interval.
All the above-listed decision-making techniques were proposed to assist
the decision maker in evaluating the consequences of the different aspects of
his problem in the presence of uncertain input parameters.
The multicriteria decision-making (MCDM) techniques in the energy
planning sector were overviewed by Pohekar et al. [6]; it was concluded that
the analytical hierarchy process is the most common and efficient technique
for handling multicriteria problems. Wimmler et al. [7] overviewed the vari-
ous MCDM techniques applied to the selection of the optimal renewable
energy resources and storage technologies in the islands.