Multi-objective Optimal Power Flow
Using Improved Multi-objective
Muhammad Abdullah1, Nadeem Javaid1(B
), Annas Chand2,
Zain Ahmad Khan2, Muhammad Waqas1, and Zeeshan Abbas1
1COMSATS University Islamabad, Islamabad 44000, Pakistan
2COMSATS University Islamabad, Abbottabad 22010, Pakistan
Abstract. This study proposes an improved multi-objective multi-verse
optimization (IMOMVO) algorithm for solving multi-objective optimal
power ﬂow (MOOPF) problem with uncertain renewable energy sources
(RESs). Cross and self-pollination steps of ﬂower pollination algorithm
(FPA) along with crowding distance and non-dominating sorting app-
roach is incorporated with the basic MOMVO algorithm to further
enhance the exploration, exploitation and for well-distributed Pareto-
optimal solution. To conﬁrm the eﬀectiveness of the proposed IMOMVO
algorithm, modiﬁed IEEE 30-bus system with security constraints is uti-
lized by considering the total generation cost and active power loss min-
imization. The simulation results obtained with IMOMVO is compared
with MOMVO, NSGA-II, and MOPSO, which reveals the capability of
the proposed IMOMVO in terms of solution optimality and distribution.
The optimal power ﬂow (OPF) is considered a vital optimization tool for eﬃcient
operation and planning of the power system. The OPF is used to determine the
control variables at a speciﬁc time for the power system operation in order
to optimize (maximize or minimize) diﬀerent objective functions (OFs) while
assuring the technical feasibility of the solution .
Limited reserves of fossil fuels and environmental protection issues have
raised the concern to integrate renewable energy resources (RESs) into the grid.
Wind power plants and solar photovoltaic are amongst the most common RESs.
A lot of research work has been done on classical OPF, which consider only
thermal generators. Classical OPF, which is by itself non-linear and non-convex
and large scale optimization problem because of diﬀerent OFs and constraints,
however, by incorporating uncertain RESs further increase the complexity of the
The OPF was ﬁrstly introduced in 1962 by Carpentier. Since the inception of
OPF, many optimization methods have been introduced to the solution of OPF
Springer Nature Switzerland AG 2019
L. Barolli et al. (Eds.): WAINA 2019, AISC 927, pp. 1071–1083, 2019.
1072 M. Abdullah et al.
problems. These approaches can be divided into classical numerical analysis and
modern metaheuristics algorithms. The classical method includes linear pro-
gramming, quadratic programming, interior point, and dynamic programming
etc. These techniques are inadequate for large-scale problems because they are
comprised of complex, lengthy calculation and they are excessively dependent on
initial guess values. In the past few years, diﬀerent metaheuristic algorithms have
been developed to overwhelm the deﬁciencies of the classical methods [2,3]. Some
of these algorithms are; whale optimization algorithm (WOA), particle swarm
optimization algorithm (PSO), moth-swarm algorithm (MSA), modiﬁed ﬁreﬂy
algorithm (MODFA), non-dominating sorting genetic algorithm (NSGA), diﬀer-
ential evolution (DE), elephant herding optimization (EHO) technique, Teaching
learning-based optimization (TLBO) technique etc. In any case, due to the incon-
sistency of the OPF solution for diﬀerent objectives, no heuristic algorithm can
be proven optimal for solving all these objectives, Hence, there is always a room
for new techniques and algorithms that can eﬀectively solve multiple objectives
The OPF problem is considered as single-objective if only one objective func-
tion is reckoned to be optimized. However, OPF problem needs to optimize
conﬂicting objectives in real world, simultaneously. For solving multi-objective
optimization (MOO) problems, two techniques are used in the literature: a pri-
ori and a posteriori . In the ﬁrst MOO technique, the MOO problem is con-
verted into a single objective (SO) by assigning weight to diﬀerent objectives and
aggregating them. The importance of each objective is dictated by the weights
assigned to it, which is generally set by an expert. Solving the problem with this
approach does not require any modiﬁcation in the algorithm. In posteriori MOO
approach, there is no need to deﬁne the weights by an expert. In this method,
diﬀerent solutions are there for the decision maker as compared to the former
one. There is no need to deﬁne weights for each objective by an expert and the
Pareto frontier is approximated, even in a single run [5,6].
Increasing the participation of consumers in the electrical network operations
and accommodation of RESs are the most important features of smart grids. The
role of OPF in smart grids is very vital in order to decrease the transmission,
distribution losses, emission of fossil fuels, and the energy generation cost. Sub-
sequently, decreasing the electrical power consumption and prices, concurrently,
enabling consumers and electrical utilities to regulate the demand [7–11].
2 Problem Formulation of MOOPF
The main goal of solving OPF problems is to optimize diﬀerent objectives by
adjusting control parameters. In MOOPF problems, two are more objectives are
optimized simultaneously subject to the constraints. The MOOPF mathematical
formulation can be described as below [12,13].
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gk(´x, ´v)=0 k=1,2,...,G (2)
hl(´x, ´v)0l=1,2,...,L, (3)
where, fobj are the OFs to be minimized, gk(´x, ´v)isthekth equality constraint
(EC) which actually represent the load ﬂow equations . The inequality con-
straints (IC) are expressed by h(´x, ´v). ´xand ´vare the vectors of state (dependent)
variables and control (independent) variables respectively .
The state variables are:
(1) Real power at swing bus (PG1).
(2) Reactive power output of generators (QG).
(3) Voltage at P-Q (Load) buses (VL).
(4) Loading of transmission lines (SL).
The vector of static variables can be written as:
The Control variables are:
(1) Voltages at generation buses (VG).
(2) Generation buses active power output (PG).
Besides these, tap settings of transformer, and compensation of shunt VAR (reac-
tive power) compensators can also be considered as control parameters.
Accordingly, vector ´vcan be expressed as:
2.1 Objective Functions
Two OFs are considered in this paper, i.e. minimization of generation cost and
power loss, whose details are provided subsequently.
2.1.1 First Objective: Minimization of Generation Cost
Total generation cost comprises of the operation cost of thermal generators and
RESs along with penalty and reserve cost, which are described below.
Thermal Power Generator Cost Model
For realistic cost model of thermal generators, valve point eﬀect is considered
. In a thermal generating unit steam is regulated by valves to run the turbine
through a separate nozzle group. To achieve maximum eﬃciency, every nozzle
group operate at its full output .For the required output, the valves are
1074 M. Abdullah et al.
operated in sequence, which results in the non-continuous cost curve. The cost
model of thermal generators considering valve loading eﬀect is given by :
where, NTG is the number of thermal generators. ´ai,´
bi, and ´ciare the cost
coeﬃcient of ith generator, producing output power PTG
diand ´eiare the
coeﬃcients of valve point loading eﬀect. The values of thermal units coeﬃcients
are listed in Table 1.
Table 1. Thermal generators coeﬃcient
Cost coeﬃcients ´a ´
GTh10 2 0.00375 18 0.037
GTh201.75 0.0175 16 0.038
GTh803.25 0.00834 12 0.045
Cost Model of Renewable Energy Sources
Generation from RESs does not require any fuel source for its operation. There-
fore, cost function does not exist, except maintenance cost, if the RESs are owned
by independent system operators (ISO). However, in the case when RESs are
operated by private bodies, the ISO pays to them accordingly to the scheduled
power contractually agreed .
The direct cost function of the kth wind farm in terms of scheduled power is
given by :
where PWis the scheduled power and gwis the direct cost coeﬃcient of the wind
power unit. Similarly, the direct cost for the solar PV unit with scheduled power
PPV , and cost coeﬃcient hpv is given by:
CPV (PPV )=hpvPPV .(6)
The third renewable energy resource that is considered in this study is the
combination of wind and run of river small hydro generation plant. The scheduled
power is jointly delivered by wind and hydro generators. The hydro generation
considered in this work is of 5 MW, which is proportional to the river’s ﬂow rate
. The direct cost of the combined wind and hydro plant is described as:
CWH(PWH)=gwhPWH =gwPWH,w +ghPWH,h,(7)
Multi-objective Optimal Power Flow 1075
where, PWH represent the scheduled real power from the combined plant, PWH,w
and PWH,h are the electrical energy contribution from the wind and hydro units
respectively. gwis the direct cost coeﬃcient of wind unit and ghis that of hydro
As wind energy is stochastic in nature, the actual power produced may be less
or more than scheduled power. Therefore, the ISO must have reserve generating
capability to meet the demand. Reserve cost for the wind unit can be calculated
CRW,i (PWsh,i −PWac,i)=krw,i(PWsh,i −PWac,i)
(PWsh,i −pw,i )fw(pw,i)dpw,i.(8)
In the case when the output power of the wind generators is greater then the
scheduled power, the ISOs pay penalty cost if the surplus power is not utilized
by them by reducing the power of thermal generators.
The penalty cost of wind plant can be mathematically described as:
CPW,i (PWac,i −PWsh,i)=kpw,i(PWac,i −PWsh,i)
where, PWsh,i is the scheduled power, PWac,i is the available power from wind
plant, PWr,i is the rated power, and fw(pw,i)is the probability density function
of wind power. Similarly the reserve and penalty cost of solar and combined
wind and hydro can be calculated. The values of direct, reserve, and penalty
cost coeﬃcients for RESs are provided in Table 2.
The output power of wind power units is directly proportional to wind speed.
Wind speed probability is predominantly represented by Weibull, probability
distribution function (PDF) [17,21,22].
The power given by PV arrays depends on solar irradiance. The probability
distribution of solar irradiance Gsis represented by lognormal PDF [21,23].
Gumbel distribution correctly represent the river ﬂow rate [24,25]. Summary of
the number of turbines, the rated power output from RESs, and the values of
PDF parameters are listed in Table 3.
2.1.2 Second Objective: Minimization of Real Power Loss
The active power loss in the branches of the power system is unavoidable because
of inherent resistance of lines. The real (active) power loss can be mathematically
f2(x, u)=Ploss =
j−2ViVjcos(δij )] (10)
Where nL is the number of branches, Gij is the conductance of the branch which
linked together the ith and jth bus, and δij is the voltage angle between them.
Viand Vjare the voltages of bus iand bus j. The magnitude of these voltages
are calculated through newton-Raphson load ﬂow equation.
1076 M. Abdullah et al.
Table 2. RESs direct, reserve, and penalty cost coeﬃcient
Direct cost coeﬃcient
Wind (at bus number: 5, 13) gw1=1.6,
Solar PV (at bus number 11) hs=1.6
Small hydro (at bus number 13) gh=1.5
Reserve cost coeﬃcient
Wind (at bus number 5) krw =3
Solar PV (at bus number 11) krs =3
Combined wind and hydro (at bus number 13) krwh =3
Penalty cost coeﬃcient
Wind (at bus number 5) kpw =1.5
Solar PV (at bus number 11) kps =1.5
Combined wind and hydro (at bus number 13) kpwh =1.5
2.2 System Constraints
MOOPF is a non-linear, non-concave optimization problem, which requires to
satisfy diﬀerent equality constraints (ECs) and inequality constraints (ICs).
These constraints are described as follow.
2.2.1 Equality Constraints
The ECs in MOOPF comprising of real and reactive power load ﬂow equations
, which stipulates that the real and reactive power generation must be equal
to the load demand and power losses in the network. The ECs can be mathe-
matically described as:
Vl[Gij cos(δij +βij sin(δij ))] = 0 ∀iNB, (11)
Vl[Gij cos(δij +βij sin(δij ))] = 0 ∀iNB. (12)
2.2.2 Inequality Constraints
ICs are actually the active, reactive power generation limits, security constraints
of lines, and voltage limits of generation and load buses [17,27].
The constraints of generators are:
Multi-objective Optimal Power Flow 1077
Table 3. PDF parameters for uncertain RESs.
Wind power generator Solar PV unit
Tota l w ind
25 75 k=2, c=9 60 μ=6,
Tota l w ind
15 45 k=2, c=10 5λ= 15,
TG,i PTG,i Pmax
WG,j PWG Pmax
PV,k PPV Pmax
WHG,j PWHG,j Pmax
TG,i QTG,i Qmax
WG QWG Qmax
PV QPV Qmax
WHG QWHG Qmax
G,i VG,i Vmax
where, PTG,i is the active power generation of ith thermal unit, PWG,PPV,and
PWHG is the real power of wind farm, solar PV array, and combined wind and
hydro generators, respectively. Similarly, QTG,i,QWG,QPV,andQWHG are the
reactive power output of the thermal, wind, solar, and combined wind and hydro
generators. VG,i is the voltage of the ith generation bus.
Security constraints includes the voltage limits of P-Q buses and the trans-
mission lines loading capability, which are described as follow:
Lp VLp Vmax
Equation 22 represents the voltage limits for NLB number of load buses and
the line loading constraint is described in Eq. 23 for Nlbranches.
1078 M. Abdullah et al.
Table 4. Best results of two-objective for Case 1 of 30 bus power system at 500
Control variables MOMVO MOPSO NSGAII IMOMVO
2(MW) 23.6459 23.8473 20.0000 57.9310 20.6786 29.3117 24.8195 31.9106
5(MW) 42.5207 63.1317 46.3870 75.0000 44.7182 101.0292 44.7238 94.7263
8(MW) 10.0000 10.1696 10.0000 34.6162 10.1694 48.4191 10.1382 39.9864
11 (MW) 41.4415 54.7171 44.9287 60.0000 43.0685 66.2612 40.5211 68.7966
P(W+H)G13(MW) 36.4134 35.9545 32.5331 48.6378 34.8491 36.4088 32.1291 35.9207
1(p.u) 1.1000 1.1000 1.1000 1.1000 1.1137 1.0808 1.3422 1.2794
2(p.u) 0.9500 1.0921 1.0913 1.1000 1.0747 1.0813 1.2014 1.1614
5(p.u) 1.1000 1.0731 1.0989 1.0995 1.1697 1.0735 1.3208 1.2728
8(p.u) 1.1000 1.1000 1.1000 1.0958 1.1882 1.1506 1.3124 1.2888
11 (p.u) 1.1000 1.0975 1.0822 1.1000 1.1006 1.1565 1.3652 1.3513
V(W+H)G13 (p.u) 1.1000 1.0780 1.0999 1.0982 20.6786 1.0881 1.4777 1.2716
Cost ($/hr) 776.400 818.017 777.777 942.171 776.882 979.451 770.917 954.880
Power los s (MW) 5.526 3.515 5.275 1.760 5.166 1.376 3.572 0.999
3 IMOMVO Algorithm and Its Application to MOOPF
Many real-world optimization problems entail optimizing diﬀerent objectives
simultaneously while meeting the constraints. These objectives are often con-
ﬂicting in nature and the optimal value of all these objectives cannot be achieved
at the same time.
3.1 Basic MOMVO Algorithm
MOMVO optimization algorithm is based on the theory of the existence of multi-
verse, recently proposed by Mirjalili . The inspiration of MOMVO is the inter-
action between universes through black, white, and worm wholes. The objects
from one universe move to another via tunnels (black, white holes), and move
within the universe or even from one universe to another through worm hole
3.2 IMOMVO Algorithm
The PF obtained by the MOMVO algorithm at higher times (iteration) is not
well distributed. Therefore, to further increase the exploration and exploitation
of MOMVO, levy ﬂight and self-pollination steps of ﬂower pollination algorithm
, is used to create a new set of universes. After ﬁnding the inﬂation rates of
both sets of universes, they are merged together. The solutions are sorted and
selected on the bases of domination level, and crowding distance approach is
Multi-objective Optimal Power Flow 1079
used to maintain the diversity of the non-dominated solution. The solutions are
truncated to the size of the archive to reduce computation time. The new solu-
tions are compared to the stored results in the archive and the better solutions
are replaced in the archive.
4 Simulation Results and Discussions
Modiﬁed IEEE 30-bus test system is considered to varify the eﬀectiveness of the
proposed IMOMVO for the solution of MOOPF and its performance is compared
with the results of MOPSO, MOMVO, and NSGA-II optimization algorithm.
The codes of these algorithms are written in the MATLAB environment and
MATPOWER (MATLAB package) is used for power ﬂow calculation. All the
simulations are performed on Intel Core i7, 2.00 GHz processor with an 8 GB
RAM personal computer. For the fair comparison of the proposed IMOMVO
with MOPSO, MOMVO, and NSGAII in terms of obtaining the best PF. The
size of the population (Np = 50) and numbers of iterations are taken the same
for all of these algorithms. The summary of the adapted 30-bus is provided in
Two conﬂicting objectives, total generation cost minimization, and power
loss minimization are solved simultaneously using proposed IMOMVO and its
performance is compared with other optimization algorithms. The PF obtained
from the simulation of these algorithms for solving MOOPF problem of modiﬁed
Fig. 1. Pareto optimum front obtained at 20 iterations.
1080 M. Abdullah et al.
Fig. 2. Pareto optimum front obtained at 100 iterations.
Fig. 3. Pareto optimum front obtained at 500 iterations.
Multi-objective Optimal Power Flow 1081
Table 5. Summary of the modiﬁed IEEE-30 bus system.
Item Quantity Details
Buses 30 6 generators buses and 24 load
Thermal generators Buses (TG
3 At bus number: 1,2, and 8. Bus
number 1 is a slack bus
Wind generator 1At bus number 5
Solar PV array 1At bus number 11
Wind generator+small hydro unit 1 At bus number 13
Control variables 11 The real power of generator buses
except for the slack bus and
voltages of all generator buses
Allowed range of load bus voltage 24 0.95 to 1.05 p.u.
Connected load 283.4 MW, 126.2 MVAr
IEEE 30-bus system is shown in Figs. 1,2,and3for 20, 100, and 500 runs. From
the simulation results, it is obvious that the PF obtained from IMOMVO per-
formed better than MOPSO, MOMVO, and NSGA-II, both at lower and higher
iteration (times) in term of solution optimality and distribution. The values of
the control variables for the best cost, best power loss, and the resultant OFs are
provided in Table 4. The PF obtained by MOMVO is better than MOPSO and
NSGA-II at lower iteration, however, at higher iteration, the PF obtained by
MOMVO is not well distributed. The proposed IMOMVO eliminated the limi-
tation of PF distribution of MOMVO algorithm by incorporating the crowding
distance and non-dominating sorting approaches and displayed the best results
both at lower and higher iteration.
In this study, IMOMVO algorithm has been proposed and applied to solve
MOOPF problem. The proposed algorithm was successfully implemented on
modiﬁed IEEE 30-bus power systems. The simulation results reveal the superi-
ority of the IMOMVO over NSGA-II, MOPSO and MOMVO algorithms. The
IMOMVO eliminated the limitation of PF distribution of MOMVO algorithm
and displayed the better results both at lower and higher iteration. Therefore,
the IMOMVO provides better PF. The PF obtained by multi-objective algo-
rithms helps the decision maker to take a better informed-decision, concerning
the compromise between the conﬂicting objectives.
1082 M. Abdullah et al.
1. Abdi, H., Beigvand, S.D., La Scala, M.: A review of optimal power ﬂow studies
applied to smart grids and microgrids. Renew. Sustain. Energy Rev. 71, 742–766
2. Attia, A.-F., El Sehiemy, R.A., Hasanien, H.M.: Optimal power ﬂow solution in
power systems using a novel Sine-Cosine algorithm. Int. J. Electr. Power Energy
Syst. 99, 331–343 (2018)
3. Bai, W., Eke, I., Lee, K.Y.: An improved artiﬁcial bee colony optimization algo-
rithm based on orthogonal learning for optimal power ﬂow problem. Control Eng.
Pract. 61, 163–172 (2017)
4. Branke, J., Kaußler, T., Schmeck, H.: Guidance in evolutionary multi-objective
optimization. Adv. Eng. Softw. 32(6), 499–507 (2001)
5. Tan, K.C.: Advances in evolutionary multi-objective optimization. In: Soft Com-
puting Applications. Springer, Heidelberg, pp. 7–8 (2013)
6. Mirjalili, S., et al.: Salp swarm algorithm: a bio-inspired optimizer for engineering
design problems. Adv. Eng. Softw. 114, 163–191 (2017)
7. Javaid, N., et al.: Energy eﬃcient integration of renewable energy sources in the
smart grid for demand side management. IEEE Access 6, 77077–77096 (2018)
8. Javaid, N., et al.: An intelligent load management system with renewable energy
integration for smart homes. IEEE Access 5, 13587–13600 (2017)
9. Khan, M., et al.: Game theoretical demand response management and short-term
load forecasting by knowledge based systems on the basis of priority index. Elec-
tronics 7(12), 431 (2018)
10. Awais, M., et al.: Towards eﬀective and eﬃcient energy management of single home
and a smart community exploiting heuristic optimization algorithms with critical
peak and real-time pricing tariﬀs in smart grids. Energies 11(11), 3125 (2018)
11. Ahmad, A., et al.: An optimized home energy management system with integrated
renewable energy and storage resources. Energies 10(4), 549 (2017)
12. Nuaekaew, K., et al.: Optimal reactive power dispatch problem using a two-archive
multi-objective grey wolf optimizer. Expert Syst. Appl. 87, 79–89 (2017)
13. Kumar, A.R., Premalatha, L.: Optimal power ﬂow for a deregulated power system
using adaptive real coded biogeography-based optimization. Int. J. Electr. Power
Energy Syst. 73, 393–399 (2015)
14. Chen, G., et al.: Quasi-oppositional cuckoo search algorithm for multi-objective
optimal power ﬂow. IAENG Int. J. Comput. Sci. 45(2), 255–266 (2018)
15. Ghasemi, M., et al.: Multi-objective optimal power ﬂow considering the cost, emis-
sion, voltage deviation and power losses using multi-objective modiﬁed imperialist
competitive algorithm. Energy 78, 276–289 (2014)
16. Abdelaziz, A.Y., Ali, E.S., Abd Elazim, S.M.: Flower pollination algorithm to
solve combined economic and emission dispatch problems. Eng. Sci. Technol. Int.
J. 19(2), 980–990 (2016)
17. Biswas, P.P., Suganthan, P.N., Amaratunga, G.A.J.: Optimal power ﬂow solutions
incorporating stochastic wind and solar power. Energy Convers. Manag. 148, 1194–
18. Chen, C.-L., Lee, T.-Y., Jan, R.-M.: Optimal wind-thermal coordination dispatch
in isolated power systems with large integration of wind capacity. Energy Convers.
Manag. 47(18–19), 3456–3472 (2006)
19. Wijesinghe, A., Lai, L.L.: Small hydro power plant analysis and development. In:
2011 4th International Conference on Electric Utility Deregulation and Restruc-
turing and Power Technologies (DRPT). IEEE (2011)
Multi-objective Optimal Power Flow 1083
20. Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for diﬀer-
ential evolution. In: 2013 IEEE Congress on Evolutionary Computation (CEC).
21. Reddy, S.S., Bijwe, P.R., Abhyankar, A.R.: Real-time economic dispatch consider-
ing renewable power generation variability and uncertainty over scheduling period.
IEEE Syst. J. 9(4), 1440–1451 (2015)
22. Reddy, S.S.: Optimal scheduling of thermal-wind-solar power system with storage.
Renew. Energy 101, 1357–1368 (2017)
23. Chang, T.P.: Investigation on frequency distribution of global radiation using dif-
ferent probability density functions. Int. J. Appl. Sci. Eng. 8(2), 99–107 (2010)
24. Mujere, N.: Flood frequency analysis using the Gumbel distribution. Int. J. Com-
put. Sci. Eng. 3(7), 2774–2778 (2011)
25. Cabus, P.: River ﬂow prediction through rainfall runoﬀ modelling with a
probability-distributed model (PDM) in Flanders, Belgium. Agric. Water Manag.
95(7), 859–868 (2008)
26. Ghasemi, M., et al.: Solving non-linear, non-smooth and non-convex optimal power
ﬂow problems using chaotic invasive weed optimization algorithms based on chaos.
Energy 73, 340–353 (2014)
27. Mohamed, A.A.A., et al.: Optimal power ﬂow using moth swarm algorithm. Electr.
Power Syst. Res. 142, 190–206 (2017)
28. Mirjalili, S., et al.: Optimization of problems with multiple objectives using the
multi-verse optimization algorithm. Knowl.-Based Syst. 134, 50–71 (2017)
29. Mirjalili, S., Mirjalili, S.M., Hatamlou, A.: Multi-verse optimizer: a nature-inspired
algorithm for global optimization. Neural Comput. Appl. 27(2), 495–513 (2016)
30. Yang, X.-S.: Flower pollination algorithm for global optimization. In: International
Conference on Unconventional Computing and Natural Computation. Springer,