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Improvement in LFC Performance of Dual Area Thermal Hydro System with

Territory Control of TCPS and Redox Flow Battery Units

Conference Paper · February 2023

DOI: 10.1109/ICTEM56862.2023.10083711

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 2023 8th International Conference on Technology and Energy Management (ICTEM) 8-9 February, 2023,
Mazandaran University of Science and Technology, Babol, Iran

Improvement in LFC Performance of Dual Area

Thermal Hydro System with Territory Control of
TCPS and Redox Flow Battery Units
Narisetti Ashok Kumar Malligunta Kiran Kumar B. Srikanth Goud
EEE Department, EEE Department, EEE Department,
Koneru Lakshmaiah Education Foundation, Koneru Lakshmaiah Education Foundation Anurag University,
Guntur, India-522 302. Guntur, India-522 302 Hyderabad, India-500 088.
ashok.narisetti053@gmail.com mkkumar@kluniversity.in srikantheee@anurag.edu.in

CH.Naga Sai Kalyan Hossein Shahinzadeh Ahmad Hafezimagham

EEE Department, Iran Grid Secure Operation Research Institute Department of Electrical Engineering
Vasireddy Venkatadri Institute of Technology, Amirkabir University of Technology Amirkabir University of Technology
Guntur, India-522 508. Tehran, Iran Tehran, Iran
kalyanchallapalli@vvit.net h.s.shahinzadeh@ieee.org a-hafezimagham@aut.ac.ir

Abstract—In this paper, a new load frequency control (LFC) PI and PID are implemented rigorously by the researchers due
technique is suggested for the dual area thermal hydro (DATH) to design simplicity. Further, extended versions of PID like
system. Tilt-integer-derivative plus filter (TIDN) optimized with adaptive (A) PID (APID), PID plus accelerator (PIDA),
water cycle technique (WCT) is designed and tested on the modified (M) PID (MPID) [5], cascade PI-PD and PID plus
DATH system for a perturbation of 10% step load (10%SLP) on additional derivative (PIDD) are also extensively reported.
area-1. The superiority of TIDN is demonstrated with other Moreover, the effective operation of these controllers requires
controllers of fuzzy PID and PIDD. DATH system is taken with soft computing methodlogies for locating the regulator design
communication time delays (CTDs) to analyze system dynamic indicators. Techniques such as butterfly optimization
behaviour close to practical nature. To substantiate the
technique (BOT) [6], genetic fuzzy technique (GFT), particle
fluctuations in DATH dynamic behaviour a coordinated control
mechanism of redox flow batteries (RFBs) and Thyristor control
swarm optimization (PSO), modified group hunting (MGH)
phase shifter (TCPS) is implemented. Investigation showcases [7] search method, harmony search algorithm (HSA),
the improvement in DATH performance with a coordinated improved JAYA (IJAYA), African vulture algorithm (AFA),
control mechanism with regards to the reponses dampening and mine blast algorithm (MBA), firefly algorithm (FA), seagull
time conceding to attain the stable condition. optimization technique (SOT) [8], genetic algorithm (GA),
symbiotic organisms search (SOS) technique, modified group
Keywords—DATH system, Water cycle technique, TIDN search (MGS), fruit bat algorithm (FBA), bull lion technique
controller, 10%SLP, TCPS-RFBs strategy. (BLT), imperialist competitive algorithm (ICA), water cycle
algorithm (WCA) [9], stochastic fractal search (SFS), artificial
I. INTRODUCTION electric field (AEFA) algorithm, pathfinder approach (PFA),
Rapid industrialization in developing nations requires Harris hawks optimizer (HHO), fruit fly algorithm (FFA) [10],
more electric power for increasing load demands. The donkey and smuggler optimization (DSO), black widow
penetration of renewable and distributed generating (DG) optimization (BWO), elephant herd optimizer (EHO) [11],
sources with the interconnected power system (IPS) has been backtracking search technique (BST), levy flight algorithm
increasing to meet the everlasting power demands. The (LFA), and falcon optimization technique (FOT) etc. are
penetration of numerous units made the IPS network more reported. However, PID type regulators are not suitable for the
complex [1-2]. The control and operation of a complex IPS network of IPS with non-linear realistic features.
network require a sophisticated control mechanism. The load For non-linear IPS models, researchers suggest fuzzy logic
on the IPS will vary momentarily and the generation must be control (FLC) aided conventional controllers. Moreover, these
varied according to it as the storage of electric power in bulk FLC-aided regulators also necessitate soft computing
quantity is not feasible [3]. However, the key indicator is the techniques for efficient performance. However considering
real power gap (RPG) in generation-demand which directly the practical aspects of design, FLC is more complex and not
influences the frequency of the IPS network. The fluctuations feasible. Further, fractional order (FO) based approaches are
in system frequency will significantly affect the IPS network widely accepted by the researchers and utilized the soft
stability. Hence, an efficient control technique is required to computing techniques of krill herd technique (KHT), ant lion-
regulate the system frequency by reducing the RPG in demand pattern search (AL-PS) [12], gas Brownian motion (GBM),
generation. The reduction of RPG in the IPS network is multi-objective external (MOET) technique, lion optimization
fulfilled by the mechanism of LFC [4]. algorithm (LOA), hybrid GA-FA, volleyball algorithm
In LFC, by making use of the governing action the energy (VBA), bacterial foraging technique (BFT), big-bang big-
stored in the moving parts and the frequency will be regulated crunch (BBBC), flower pollination technique (FPT), wild gate
up to a certain extent. Large deviations in IPS network algorithm (WGA) [13] etc. are implemented. Literature study
frequency that evolved due to loading uncertainties are to be displayed the fact that the new algorithms have always the
handled by the secondary regulator. Thus, secondary regulator scope in tuning the regulators.
design is the key to the stability of IPS. Several regulators like

978-1-6654-5285-4/23/$31.00 ©2023 IEEE

 1
RFBs
B R ∆PD

1 1 1 + SK r Tr K PS ∆f1
e − Sτ d TIDN + + +
1 + STg 1 + STt 1 + STr 1 + STPS
CDTs Speed
Governor Reheat-steam Turbine
+ TCPS

2Π T12 +
+
S

1 1 + STrw K PS ∆f 2
+
e − Sτ d TIDN + + +
1 + ST1 1 + ST2 1 + STPS
CDTs Mechanical Hydro Turbine
B 1 Hydro Governor
RFBs
R

Fig.1. DATH system with CTDs and TCPS-RFBs strategy.

However several parameters involved in FO for communication network. The CTDs in the communication
optimization make the computational burden on the system. network result in the delay in generating the error signal which
But, TID is also the FO type controller and similar to that of then is utilized for shifting the IPS operating point to minimize
classical type regulators in design aspects with less the generation-load real power gap. The real power gap
computational burden are gaining momentum. Hence, in this reflects directly on control area frequency and thereby the IPS
paper TIDN regulator is chosen as the frequency regulator for stability. Thus, CTDs are to be treated with utmost care and
IPS. Furthermore, the supplementary control technique is are needed to be considered. The CTDs considered in this
adopted in this work to obtain better performance of the work are represented in Equation (1).
system. The literature review disclosed the supplementary
techniques of AC-DC lines [14], DGs penetration and τd
1− s
coordination of energy storage devices (ESDs) with Thyristor e −sτd
= 2 (1)
controlled series compensators. In this paper, the TCPS-RFBs τ
1+ d s
strategy is adopted to obtain better dynamic behaviour of the 2
considered IPS network. 1
KT
The contributions of this paper are S1 n
Tilt gain
a) DATH system with practical features of CTDS is
perceived for dynamic analysis. U (S )
ACE 1 +
b) TIDN using WCT is developed as LFC for IPS. KI +
S +
c) Analysis of DATH is initiated and subjected to
10%SLP on area-1. Integral gain 1
d) The efficacy of TIDN in performance is revealed S
with PIDD and fuzzy PID. -
e) The significance of CTDs on the performance of KD + N
DATH is showcased.
Derivative gain Filter
f) TCPS-RFBs coordinated strategy is implemented
and improvement in LFC performance is noticed. Fig.2. Architecture of TIDN controller

II. POWER SYSTEM UNDER STUDY III. CONTROLLER AND OBJECTIVE FUNCTION
−1
DATH under investigation is shown in Fig.1, which The injection of the transfer function S n with the
consists of two areas. The power generation units of Thermal proportional component in PID forms the TID regulator.
and hydro are placed in area-1 and area-2. The analysis is Though PID is effective in quickly enhancing the system
carried out upon targeting area-1 of DAHT with 10%SLP. stability but suffers from generating uncertain plant inputs.
DATH is developed in the SIMULINK platform of MATLAB Moreover, the noise rejection is very poor which greatly
and the parameter is taken from [15]. The non-linear feature affects the plant performance. The filter in the TIDN regulator
of CTDs is taken with DATH to get the investigative analysis rejects the disturbances and noises and hence aids the better
close to practicality. CTDs have existed with the data optimal performance. The architecture of TIDN [16] is
exchange among various sensors in the IPS via the
depicted in Fig.2. The parameters of TIDN are found using the the best solution is the sea. The initialization of RDs is
WCT algorithm with regards to integral time area error modelled as given in Equation (4-5).
(ITAE) noted in Equation (2).
Start
TSim

J ITAE =  (Δf
0
1 + Δf 2 + ΔPtie12 ) * T dt (2)
Initialize Initial Parameters

IV. COORDINATED TCPS AND RFBS STRATEGY Generate population matrix-

(4&5)
The tie-line power flow can be enhanced with the
incorporation of FACTS devices and the oscillations can be Every rain drop cost is calculated using (2)
effectively damped out. Hence, TCPS is considered in this and streams, rivers and sea are determined
work and is connected in series with the tie-line. The
Flow of rivers and streams to
disturbance of 10%SLP is injected in area-1 for analysis
the sea-(6&7)
purpose; TCPS is integrated close to the area-1. Making use
of the Thyristor control angle, the voltages at the buses will be
controlled and there by the tie-line power flow. The No Is cost value of sea
(or river) < the river
architecture of TCPS as damping controller is shown in Fig.3 (or stream)?
and the necessary mathematical modeling is considered from Yes
[17]. Alter the positions of river
with sea
∆f1 ( S ) Kϕ ∆PTCPS
T12 + No
1 + STPS ∆Ptie12 Is (8) satisfied?
Yes
+ 2Π T12 ∆Ptie0 12 Flow of new raindrops to streams
∆f 2 ( S ) + in different locations-(10)
S
Fig.3. TCPS as damping controller Decrease dmax using (9)

On the other hand, ESDs will work as significant spinning No

reserves during large load uncertainties. Flow batteries are one Is convergence
satisfied?
of the prominent ESDs which work based on the theory of
Yes
electrochemical reactions. The charging and discharging
processes are controlled by the reduction and oxidation Display global best solutions
processes. The RFBs units will inject energy into the grid with
the reduction reaction. With this, an electron will be dislodged End
from the electrolytic solution and performs useful work.
Fig.4. WCT flowchart
Moreover, RFBs possess long life and characteristics of quick
response. The modelling of RFBs is given in Equation (3) [18- RD i = Yi = [y1 , y 2 .........y Nvar ] (4)
19].
 RD 1 
K RFBs − − −
G RFBs = (3)  
1 + sTRFBs RD Population =  RD i  (5)
− − −
V. WATER CYCLE TECHNIQUE  
 RD N POP 
To get a solution for realistic, complex and constrained
The objective index of all the RDs is to be evaluated with
engineering problems, an effective optimization technique is
Equation (3) [21-22] and the position of streams/rivers is
strongly necessitated. In this work, an optimization technique
initiated as
is required to find the parameters of the suggested TIDN
controller for the LFC study. WCT is the recent method that new
Pstream = Pstream + rand() * C * (Priver − Pstream ) (6)
suits complex IPS networks. Earlier, WCT is rigorously new
implemented for several engineering problems. But, for the Priver = Priver + rand() * C * (Psea − Priver ) (7)
study of LFC, the implementation of WCT has to be done Where, rand () takes the value from [0-1] and ‘C’ from [0-
extensively. Depending on the cycle of water theory in nature, 2]. Further, the evaporation and the raining loop begin and
this WCT is proposed by researchers in [20]. The design of terminate with the following procedure illustrated in Equation
WCT is carried out based on the assumptions of the water flow (8-10).
towards the down streams. The rains will fell mostly on the
hilly regions and form the streams which collectively form the Psea − Priver < d max (8)
rivers. The rivers will merge into the sea lastly. Here in this d new
max = d max − (d max/ /max.iteration) (9)
WCT, the droplets of rain (RDs) are the initial particles and new
Pstream = Psea + U X rand(1, N var ) (10)
 -3
WCT mechanism gives the global parameters of the TIDN x 10
5
regulator when the searching process met the sea. WCA is
developed in MATLAB (.m file) with populations and
iterations of 100. The detailed flow of WCT is indicated as a 0
flowchart in Fig.4.
VI. SIMULATION RESULTS -5

Δf2 (Hz)
A. Case-I: Analysis of DATH under different controllers
-10
To assess the DATH system dynamic behaviour area-1 is
loaded with 10%SLP. Regulators like PIDD, fuzzy PID and
-15 PIDD
TIDN are placed one after the other in both the areas of DATH
Fuzzy PID
and the parameters of all the above said regulators are found TIDN
optimally using the WCT. To elevate the efficient control -20
technique, the DATH responses under the governing of TIDN, 0 5 10 15 20 25 30
PIDD and fuzzy PID are compared in Fig.5. It is evident that Time (sec)
the dynamic behaviour of DATH is strongly controlled by the (c)
TIDN compared to PIDD and fuzzy PID. Responses in Fig.5
are interpreted in terms of settling time and are noted in Table Fig.5. Case-I responses a.∆f1 b.∆Ptie12 c.∆f2.
I. It has been noticed that in the DATH under the WCT-based B. Case-II: Exhibiting the CTDs effect on DATH
TIDN regulator, the fluctuations in the responses are very performance
much mitigated and attain a steady position in less time.
From the analysis in the above subsection, it is concluded
Moreover, the TIDN effectively minimized the ITAE and
that the WCT-based TIDN is superior and it is continued as
increased by 70.48%with PIDD and 43.33%with fuzzy PID.
the secondary regulator to the DATH system for further
The parameters of TIDN, PIDD and fuzzy PID are retrieved
investigation.
using WCT and are provided in Table II.
-3
0.01 x 10
2

0.005 0

0 -2

-4
Δf1 (Hz)

-0.005
Δf1 (Hz)

-0.01 -6

-0.015 -8
PIDD
-10 Without CTDs
-0.02 Fuzzy PID
With CTDs
TIDN
-12
-0.025 0 5 10 15 20
0 5 10 15 20 25 30 Time (sec)
Time (sec)
(a)
(a)
-3
-3 x 10
x 10 0.5
4
0
2
ΔPtie12 (p.u. MW)

-0.5
ΔPtie12 (p.u. MW)

0
-1

-2 -1.5

-4 PIDD -2 Without CTDs

Fuzzy PID
With CTDs
TIDN
-2.5
-6 0 5 10 15 20
0 5 10 15 20 25 30 Time (sec)
Time (sec)
(b)
(b)
 TABLE I. RESPONSES SETTLING TIME
Settling time ∆f1 ∆Ptie12 ∆f2 ITAE*10-3
(Seconds)
PIDD 31.65 32.75 27.43 105.757
Fuzzy PID 22.1 27.83 23.63 55.094
TIDN 11.89 17.94 10.61 31.218
With TCPS 9.29 10.99 9.59 -
With TCPS-RFBs 7.62 8.29 7.57 -

TABLE II. CONTROLLER OPTIMAL GAINS

Parameters Area-1 Area-2
TIDN Fuzzy PID PIDD TIDN Fuzzy PID PIDD
KP/KT 0.219 0.577 0.175 0.199 0.366 0.183
KI 0.424 0.068 0.069 0.372 0.287 0.210
KD 0.093 0.177 0.155 0.176 0.188 0.277
KDD - - 0.068 - - 0.087
N 246.182 - - 245.178 - -

-3 -3
x 10 x 10
2 0.5

0
0

ΔPtie12 (p.u. MW)

-0.5
-2
Δf2 (Hz)

-1
-4
-1.5

-6 Without Device
Without CTDs -2 With TCPS
With CTDs With TCPS-RFBs
-8 -2.5
0 5 10 15 20 0 5 10 15
Time (sec) Time (sec)
(c) (b)
-3
Fig.6. Case-II responses a.∆f1 b.∆Ptie12 c.∆f2. x 10
2
For 10%SLP on area-1 and under WCT tuned TIDN
regulator the responses of DATH with and without
0
considering CTDs are analyzed and depicted in Fig.6.
Observing the responses in Fig.6 the DATH dynamic
-2
Δf2 (Hz)

responses with CTDs are more fluctuated. Moreover, the

responses for the case of believing the CTDs attain the stable
position after conceding longer duration. This is due to the -4
delay in shifting the IPS operating point due to the lag in the
generation of the control area error signal. Without Device
-6
-3 With TCPS
x 10
2 With TCPS-RFBs
-8
0 5 10 15
0
Time (sec)
-2 (c)
Fig.7. Case-III responses a.∆f1 b.∆Ptie12 c.∆f2.
Δf1 (Hz)

-4

-6 Thus, in realistic practice, the lag exists with the data

exchange via the communication network and hence the CTDs
-8 are to be believed with IPS while developing the regulator.
Without Device Otherwise, the developed regulator won’t be robust enough
-10 With TCPS and the IPS may even drag to instability.
With TCPS-RFBs
-12 C. Case-III: Analysis of DATH with TCPS and RFBs
0 5 10 15
coordinated strategy
Time (sec)
To diminish the fluctuations in the DATH system, TCPS-
(a) RFBs strategy is implemented in this work. At first, the TCPS
is integrated with the tie-line and the dynamic behaviour of
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