Page 310 - Decision Making Applications in Modern Power Systems

Decision-making-based optimal generation-side Chapter | 11 271

frequency in its acceptable level in each area. Moreover, load-frequency con-
troller should be able to maintain the exchanged active powers through dif-
ferent tie-lines at their scheduled values [1]. To this end the parameters of
load-frequency controllers in each control area should be tuned optimally to
achieve a suitable performance.
In the past, several control methods and techniques have been proposed
for both generation-side reserve scheduling and LFC. In general the main
aim of using secondary reserve based LFC is to regulate frequency devia-
tions caused by small disturbances such as the uncertainties of renewable
sources and load fluctuation [2]. For LFC, proportional integral and propor-
tional integral derivative (PID) are widely adopted in industrial power sys-
tems due to their simplicity [3]. On the other hand, modern techniques are
suggested in the literature for LFC, such as different fuzzy PID controller
structures [4,5], two and three degree-of-freedom (DOF) integral-derivative
controllers [6,7], and fractional-order PID (FOPID) controller [8,9].It is
worth mentioning that fractional calculus based control technique, which is
another type of controller that provides more DOFs, performs better in com-
parison with traditional PID controllers. In some researches [10,11], in order
to eliminate the noise of the differentiation path in PID controller, PID with
derivation filter controllers have been adopted.
Trial-and-error approach can be used to tune load-frequency controllers
in power systems [1,2]. However, it is not an easy task to tune the control-
lers’ parameters using trial-and-error approach. In addition, it might not lead
to the optimal parameters. Hence, due to its importance in improving the
control performance, a number of optimization methods have been used for
the optimal tuning of load-frequency controllers in interconnected power sys-
tems. In general, methods such as evolutionary computing-based controllers’
parameters tuning, model predictive control, and optimal control have been
suggested for LFC in interconnected power systems [12 15]. From a survey
on the literature, it can be seen that evolutionary algorithms have received a
considerable attention from the researchers due to their good performance
and simplicity. In this regard, a number of algorithms, such as particle swarm
optimization [16], genetic algorithm [17], deferential algorithm [18], bacte-
rial foraging optimization [18], firefly algorithm [19], imperialist competitive
algorithm (ICA) [20], hybrid gravitational search and pattern search (hGSA-
PS) algorithm [21], and many other algorithms [22 25], have been adopted
for solving the problem of tuning load-frequency controllers’ parameters.
Recently, wind-driven optimization (WDO) algorithm is used for LFC in
which it is verified that WDO is superior to other algorithms in improving
the LFC performance [26]. Interested readers are referred to the recent litera-
ture survey [27].
The literature survey shows a knowledge gap regarding LFC design for
future power systems considering the high penetration level of renewable
energy resources (RERs) and their uncertainties. Moreover, it has been