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In DPM diagonal element shows the local demand.The demand of one region discos value to anotherregions GENCO value is shown by the off diagonalelement. The actual and scheduled steady state powerflows on the given tie line is:-ΔPtie i -j, schedule = [ Demand from genco of area iby disco of area j – Demand from genco of area j bydisco of area i ]The tie line error is given by:-ΔPtiei - j, error = ΔPtiei -j, actual - ΔPtiei -j, schedule.The tie line error disappear the steady state error.The ACE signal given to the ISO is:-ACEi = Bi Δfi + ΔPtiei -j, errorΔfi is change of frequency of area „i andBi is frequency Bias factor of area „iGenco1(scheduled) = (0.2+0.1)*0.01 = 0.03 puGenco2(scheduled) = (0.2+0.4)*0.01 = 0.06 puGenco3(scheduled) = 0 pu

Genco4(scheduled) = (0.1+0.2)*0.01 = 0.03 puGenco5(scheduled) = (0.1+ 0.2 + 0.3)*0.01 =0.06 puGenco6(scheduled) = (0.2 +0.4 +0.3)*0.01 = 0.09 puGenco7(scheduled) = 0 puGenco8(scheduled) = 0 puGenco9(scheduled) = (0.1 +0.3 +0.4)*0.01 = 0.08 puGenco10(scheduled)=(0.2)*0.01=0.02puGenco11(scheduled)=(0.2+0.3+0.2+0.5+0.5)*0.01 =0.17 pu

Genco12(scheduled)=(0.2 +0.1+ 0.2+0.2+0.5)*0.01 =0.12 puGenco13(scheduled)=(0.2+0.3+0.2+0.2+0.2+0.2+0.1)*0.01=0.14puGenco14(scheduled) = 0 puGenco15(scheduled) = 0 pu

The schedule tie line powers are:-∆P tie1-2,schedule =-(0.2x0.1+0.1x0.1)+(0.1x0.1)= 0.02pu∆P tie1-3,schedule = -(0.1x0.1) = -0.01pu∆P tie1-4,schedule = [(0.2x0.1+0.2x0.1+0.2x0.1)+(0.3x0.1+0.3x0.1)]+(0.4x0.1)=-0.08pu∆P tie2-3, schedule = 0.2x0.1 = 0.02pu∆P tie2-4, schedule =0.3x0.1 + [(0.2x0.1+0.1x0.1+0.2x0.1) -(0.2x0.1+0.2x0.1)] = 0.06pu∆P tie3-4,schedule =-(0.5x0.1+0.2x0.1+0.2x0.1) = 0.09pu

A. Bacterial foraging optimization techniqueIt is recently developed technique, named as Bacterialforaging optimization (BFO) which has been projected

by Passion based on a bacteria. The BF techniquedependent on the deportment of E.coli bacteria whichis found in the human intestine. The bacterial foragingoptimized the controller gains and other parameters.In simulation work the parameter for coding is to beS=20, Nc=8, Ns=3, Nre = 15, Ned=2, Ped=0.80.D(attr.)=0.003, W(attr.) = 0.04, H(repellent)=0.003,W(repellent)= 10 and P=10 considered.

= (Δ )2 + (Δ )2} B. Result And AnalysisThe simulation is carried out on Four-Areainterconnected deregulated system. The PI controller isimplemented with and without bacterial foragingtechnique. In this, tie line power of the system iscompared. The simulation result are shown in fig(3) tofig(23). Using Simulink/MATLAB formulation theoptimum AGC controller gain value, representing thescheduling of generators, tie line power exchange aredone. With the help of BF algorithm value of Ki isobtained, which is applied to AGC in interconnectedfour area system under the deregulated environment.

C. Tie-line power comparison of different areas

Fig.3. Del Ptie1-2 with and without BF controller

8/20/2019 Performance of Automatic Generation Control in an Interconnected Power System under Deregulated Environment

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701International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882

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Fig. 4. DelPtie1-3 with and without BF controller

Fig. 5. DelPtie1-4 with and without BF controller

Fig. 6. DelPtie2-3 with and without BF controller

Fig. 7. DelPtie2-4 with and without BF controller

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Fig. 8. DelPtie3-4 with and without BF controller

Fig. 9. Del Pg1 with and without BF controller

Fig. 10. Del Pg2 with and without BF controller

Fig. 11. Del Pg3 with and without BF controller

8/20/2019 Performance of Automatic Generation Control in an Interconnected Power System under Deregulated Environment

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703International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882

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Fig. 12. Del Pg4 with and without BF controller

Fig. 13. Del Pg5 with and without BF controller

Fig. 14. Del Pg6 with and without BF controller

Fig. 15. Del Pg7 with and without BF controller

8/20/2019 Performance of Automatic Generation Control in an Interconnected Power System under Deregulated Environment

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704International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882

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Fig. 16. Del Pg8 with and without BF controller

Fig. 17. Del Pg9 with and without BF controller

Fig. 18. Del Pg10 with and without BF controller

Fig.19. Del Pg11 with and without BF controller

8/20/2019 Performance of Automatic Generation Control in an Interconnected Power System under Deregulated Environment

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705International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882

Volume 3, Issue 3, June 2014

Fig. 20. Del Pg12 with and without BF controller

Fig. 21. Del Pg13 with and without BF controller

Fig. 22. Del Pg14 with and without BF controller

Fig. 23. Del Pg15 with and without BF controller

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706International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882

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Abbreviations

Δ Deviations Derivative in terms of Laplace

f frequencyω Angular speed

Tg Governor time Constant Tij Coefficient of i-j tie Lineaij Operator

Bi Bias Factor Pref The Output of ACE Pl Electric Load Variations

R Regulation Parameter Apfi ACE Participation Factors DPM DISCO Participation Matrixcpfi Contract Participation Factors

ACE Area Control Error Pi-jactual Real Tie Line Power

Pi-jscheduled Scheduled Tie Line Power FlowPi-j error Tie Line Power Error

BF Bacterial foragingKp1,2,3 Generator Gain Constant Tp1,2,3 Generator Time Constant Pt Turbine output power Tt Turbine time Constant Pg Governor Output power Tg Governor Time Constant III. CONCLUSIONThis Paper encapsulates automatic generation controlof the power system after deregulation includesbilateral contracts. DPM facilitates bilateral contractssimulation. Controller gains are optimized by bothBacterial Foraging and Proportional integral controller.This is study using simulation on a Four area powersystem considering different contracted scenarios. Thedynamic and steady state responses for tie line powerand power generation changes are shown. Thesimulation reveals that the proposed BacterialForaging based integral controller gives betterperformance than Proportional integral controller.APPENDIX-1

Base =1000MVAa12 = a13 = a14 = a23 = a24 = a34 = 1Thermal DataTp = 20sTij = 0.086saij = -1Tt = 0.3sR = 2.4 hz/pu . MwKpi = 120 hz/pu. MwTg = 0.08sHydro DataTp = 20s

Tw = 1s

Tms = 48.7sKd = 5 hz/pu.MwTd = 0.1sF = 60 hzTime constant (Tps)=2H/BF

delPd1= delPd2 = delPd3 =0.01Governor time constant (Tg)Tg1=Tg2 = 0.08 secTp1=Tp2=Tp3=20 secFrequency Bias Factor (B)B1= B2 = B3 = B4 = 0.425Speed Regulation(R)1/R = 0.417

REFERENCES[1] Vaibhav Donde, M.A. Pai and Iran A. Hiskens,“Simulation and Optimization in a AGC System after Deregulation,” IEEE Trans. Power System s, VOL. 16,no. 3, AUGUST 2001.[2] Surya Prakash and S.K. Sinha, “Intelligent PIControl Technique in Four Area Load FrequencyControl of Interconnected Hydro-themal PowerSystem,” in International Conf. on Computing,Electronics and Electrical Technologies[ICCEET],2012.[3] Kanika Wadhwa, J. Raja and S.K. Gupta, “BFBased Integral Controller for AGC of MultiareaThermal System under DeregulatedEnvironment”,IEEE 5th Power India Conference,19 -22th Dec,2012.[4] E.Rakhahani and J.Sadeh, “LOAD FrequencyControl Of Multi-Area Restructured Power System,”IEEE Trans. Power Systems,2008.[5] F.Liu, Y.H.Song, J.Ma, S.Mei and Q.Lu, “OptimalLoad Frequecy Control in Restructured PowerSystems,” IEE Proc. -Gener. Transm. Distrib., Vol.150, No.1, Jan,2003.[6] Kevin M. Passino, “Biomimicry of BacterialForaging for Distributed Optimization and Control,”IEEE Control Syst. Mag., vol. 22, no. 3,pp.52-67,Jun.2002.

[7] B. Parashuramulu and Ashwani Kumar. “LoadFrequency Control of Hybrid Systems in Open AccessEnvironment” IEEE Annual IndiaConference(INDICON),2010.[8] Richard D.Christie and Anjan Bose , “Load

Frequency Control Issues In Power system OperationsAfter Deregulation,”IEEE Transaction Power Systems,1995.[9] Elyas Rakhshani and Javed Sadeh, “Simulation Of Two-Area AGC System in a Competitive EnvironmentUsing Reduced- Order Observer Method,” IEEETransaction Power Systems,2008.