BF Based Integral Controller for AGC of Multiarea Thermal System under Deregulated Environment Kanika Wadhwa
J.Raja
Research Scholar, EE Department DCRUST, Murthal Sonipat, Haryana, India
[email protected]
Assistant Director, NPTI Ministry of Power, Sector-33 Faridabad, Haryana, India
[email protected]
Abstract— In this paper, three equal areas are considered. This paper deals with the results of the dynamic performance of frequency control as well as tie line power, for automatic generation control (AGC) in thermal based three-area power system under deregulated environment, based on Bacterial Foraging (BF) optimization technique. In three-area, area 1 consist of two DISCOs and two GENCOs, area 2 consist of one GENCO and two DISCOs and area 3 consist of two DISCOs and three GENCOs. In this each area include steam turbine. Different kinds of ancillary services are present in power system. Amongst these services Load Frequency Control (LFC) is present under the deregulated power system. In this paper, the concept of Disco participation matrix (DPM) is used to simulate the bilateral contracts in three area AGC model. Integral gain is optimized by the Bacterial foraging (BF) optimization technique. Keywords- Automatic Generation Control (AGC); Deregulated Environment; Bacterial Foraging; Optimal Integral gains.
I.
INTRODUCTION
The power system operation can be defined as an interconnected grid system that aims at improving economy of operation and system security. The main objective of the power system is to maintain continuous supply of power with an acceptable quality, to all the consumers in the system. O.I.Elgerd initially started the concept for the design of AGC regulator of interconnected power systems. The North American Power Systems Interconnection Committee had suggested that, in an interconnected system, each control area should set its frequency bias equal to the area frequency response characteristics (AFRC). Elgerd and Fosha [1-2] seriously questioned the basis of this practice and with the help of the methods based on optimal control theory they showed that a wider stability margins and better response could be obtained by lower bias settings. They also showed that a state variable model based on optimal control techniques could greatly improve the stability margins and dynamic response of the megawatt-frequency (P-F) control problem. Concordia and Kirchmayer [3]-[5] have analyzed the AGC problem of two equal area thermal, hydro and hydro-thermal systems. R. K. Green [6] presented a new formulation of the principles of AGC. He suggested the concept of transformed AGC, which could eliminate the need for bias settings, by directly controlling the nominal frequency set-point of each unit. Several areas are present in an interconnected power
978-1-4673-0766-6/12/$31.00 ©2012 IEEE
S.K.Gupta Chairman of EE Department DCRUST, Murthal Sonipat, Haryana, India
[email protected]
system. In each area, an automatic generation controller (AGC) monitors the tie-line power and system frequency, computes the net change in the generation required (generally referred to as area control error-ACE) and changes the set position of the generators within the area so as to keep the time average of the ACE at a low value. In the power system AGC is one of the ancillary services which plays an important role. It maintains the scheduled system frequency and tie line power during normal operating condition and also during small perturbation. Literature survey depicted that very minute attention has been provided upon the study of AGC of multiarea systems. Optimization of supplementary controller gains has been the main area of attraction. In most of the recent reported strategies, attempts have been made to adapt well tested classical AGC schemes to the changing environment i.e. under deregulation [7-8]. Under deregulated power system, the engineering concepts of operation and planning were to be changed though essential ideas remain the same. In recent scenario, all around the world, the electric power industry is migrating towards a deregulated environment. In deregulated environment consumer will have chance to choose amongst competent providers of electrical energy [9]. In new era, a DISCO can be deal directly with a GENCO for power. ISO is responsible for supervising all these transaction. Donde and Pai [10] had a thorough study on optimization and simulation in an AGC system under deregulated environment. K.M.Passino [11] proposed the BF technique. This technique is based on e.coli bacteria’s foraging behavior. This bacteria is found in human intestine. They perform different functions in the following sequence (i) chemotaxis, (ii) swarming, (iii) reproduction and (iv) elimination and dispersal. Under first step i.e. chemotaxis swim function is performed and later on tumbling is performed. Initially the e.coli bacteria determine the food quantity and later on tumbles to follow any arbitrary direction and swim for a predetermined space. One step of chemotaxis is the combination of tumble and swim. Swarming means to work in a group. When one e.coli. bacterium search nutrients, it invites e.coli as well informing about nutrients. In reproduction stage most unhealthy bacteria will vanish and the healthiest ones will break into two bacteria. The no. of population is same in whole process. Furthermore, in elimination and dispersal step,
all the bacteria in a space are ruined or a new part is formed in the dispersal step by a group. II.
RESTRUCTURED SYSTEM OF THREE AREA AGC SYSTEM
Traditionally, the electrical power system were largely in the hand of vertically integrated utilities (VIUs) which has its own generation, transmission and distribution system that supply power to the consumer at regulated rates. For effective competition, there should be large no. of participants and therefore generation and distribution separated horizontally (cutting vertically). The monopolistic behavior of electricity industry will become competitive. In monopoly system electricity was assumed as service where end users are known as customer but in competitive electricity market, electricity is now a commodity which used by consumers. In restructured environment Disco can individually contract with GENCO for power purchase. The ISO will control the power dispatch and the transmission system. The ISO will procure ancillary service in which AGC is one of them.
In this paper value of DPM is
ª 0.3 « 0.2 « « 0 DPM= « « 0.2 « 0.2 « ¬« 0.1
0.25 0 0.4 0.1 0.6 º 0.15 0 0.2 0.1 0 »» 0.15 0 0.2 0.2 0 » » 0.15 1 0 0.2 0.4 » 0.15 0 0.2 0.2 0 » » 0.15 0 0 0.2 0 ¼»
Genco1scheduled = ( 0.3 + 0.25 + 0 + 0.4 + 0.1 + 0.6 )* 0.01 = 0.0165
Genco 2 scheduled = ( 0.2 + 0.15 + 0 + 0.2 + 0.1 + 0 )* 0.01 = 0.065
Genco3scheduled = ( 0 + 0.15 + 0 + 0.2 + 0.2 + 0 )* 0.01 = 0.0055
Genco 4 scheduled = ( 0.2 + 0.15 + 1 + 0 + 0.2 + 0.4 )* 0.01 = 0.0195
Genco5scheduled = ( 0.2 + 0.15 + 0 + 0.2 + 0.2 + 0 )* 0.01 = 0.0075
Genco6 scheduled = ( 0.1 + 0.15 + 0 + 0 + 0.2 + 0 )* 0.01 = 0.0045 Three-area T-T-T system is developed using DPM under restructured environment. In the three-area each area has thermal system. In each area there are no. of GENCOs so ACE signal must be distributed in accordance to respective contribution. The coefficient which represent their sharing, are called as “ACE participation factor (apf)”. The sum of apf of Fig. 1. Configuration of power system under deregulated environment
In Restructured environment, Disco has right to acquire power from any GENCOs at competitive rates. They may or may not be in their own area. DPM is introduced in the restructured environment. The no. of GENCOs equals to the no. of rows and no. of DISCOs equals to the no. of column in DPM. DPM gives the participation of a DISCO in agreement with a GENCO. A fraction of total load power consumed by DISCO toward GENCO is symbolized by any value inside this matrix. In this, three-area system is considered in which each region has 2 DISCOs and area 1 has two GENCOs, area 2 has one GENCO and area 3 has three GENCOs as depicted in Fig. 1. The equivalent DPM will be: ª Cf11 Cf12 Cf13 Cf14 Cf15 Cf16 º «Cf » « 21 Cf 22 Cf 23 Cf 24 Cf 25 Cf 26 » « Cf Cf 32 Cf 33 Cf 34 Cf 35 Cf 36 » DPM= « 31 » «Cf 41 Cf 42 Cf 43 Cf 44 Cf 45 Cf 46 » « Cf 51 Cf 52 Cf 53 Cf 54 Cf 55 Cf 56 » « » ¬« Cf 61 Cf 62 Cf 63 Cf 64 Cf 65 Cf 66 ¼» Here Cf is the contract participation factor. In DPM diagonal element shows the local demand and off diagonal element shows the demand of one region’s Discos value to the another region’s GENCO value. The sum of all the entries in a column in this matrix is unity.
each area equals to unity and
n
¦ apf =1 where n is the number j
j=1
of GENCOs in each area. Under thermal based three-area system, flow of power on any tie-line can be calculated as: ¨Pi-j scheduled = [Demands of DISCOs in area j from GENCOs in area i] - [Demand of DISCOs in area i from GENCOs in area j] (1) and
ΔP1scheduled (k ) = ΔP1− 2 scheduled (k ) + a 31 ΔP3−1scheduled (k )
(2)
ΔP2scheduled (k ) = ΔP2 −3scheduled ( k ) + a12 ΔP1− 2 scheduled ( k ) ΔP3scheduled (k ) = ΔP3 −1scheduled ( k ) + a 23 ΔP2 −3 scheduled (k )
(3) (4)
ΔPtie1−2(scheduled) = (Cf13 + Cf14 + Cf 23 + Cf 24 ) − (Cf 31 + Cf 32 ) ΔPtie2−3(scheduled) = (Cf 35 + Cf 36 ) − (Cf 43 + Cf 44 + Cf 53 + Cf 54 + Cf 63 + Cf 64 ) ΔPtie3−1(scheduled) = (Cf41 + Cf42 + Cf51 + Cf52 + Cf61 + Cf62 ) − (Cf15 + Cf16 + Cf25 + Cf26 )
where a12= a23= a31 = -1, because the rated power of each area is equal and k is sampling key. The in accuracy for the power shown in (2)-(4) is given as follows: ΔPierror = ΔPiactual + ΔPischeduled
(5)
In steady state ACE signal is generated by error signal as described below: (6) ACEi = Bi Δ fierror + Δ Pierror ; where i = 1, 2,3
Using DPM, three-area T-T-T system as depicted in Fig. 2. State space equation of power system enclosing three areas is given below: •
(7) X(t) = AX(t) + BU(t) In equation (7) state vector is X and demands of DISCOs is U. III.
BACTERIAL FORAGING
Bacterial Foraging is an optimization technique. This technique is based on the foraging behavior of e.coli. bacteria. This bacteria is found in the human intestine. This technique
is subdivided into 4 steps chemotaxis, reproduction, elimination and dispersal.
swarming,
A.
Chemotaxis This process is the combination of tumbling and swimming behavior of flagella. When flagella rotate in clockwise direction then swimming action will be performed and when move in counter clockwise direction, tumbling will be done. For tumbling, ĭ(j) is generated as unit length in random direction. After tumble the direction of movement will be defined by ĭ(j).
Fig. 2. Three Area Thermal Thermal Thermal System Under Deregulated Environment
θi ( j + i, k, l) = θi ( j, k, l) + C(i)φ( j)
(8) Where: C(i) is the step size taken in the random direction specified by the tumble and și(j,k,l) represents the ith bacterium at jth chemotactic, kth reproductive and lth elimination and dispersal step.
region. In this process either the bacteria vanishes or they disperse away. Fig. 3 Subsystem 1 and Fig. 4 Subsystem 2 include the DPM value.
B.
Swarming In this step, the bacterium searches nutrients and invite others and they follows optimal path to grab food.
C.
Reproduction According to health status all bacteria are sorted in reverse order. Lower half population is killed and remaining upper half doubles itself at the same location. Thus bacteria population remains same.
D.
Elimination and Dispersal Slow or abrupt changes in the surrounding, where population of bacteria resides may occur due to different
Fig. 3. Sub System-1
In case of BF technique we assign each bacterium with a set of variables to be optimized and are assigned with random values ( Δ ) within the universe of discourse defined through upper and lower limit between which the optimum value is likely to fall. Each bacterium is allowed to take all possible values within the range and the objective function which is ISE defined by following is minimized. ISE = Δ f1 2 + Δ f 22 + Δ f 32 + Δ Ptie2 12 + Δ Ptie2 23 + Δ Ptie2 31 (9)
Fig. 4. Sub System-2
IV.
RESULT DISCUSSION
The proposed BF based approach for solving the LFC was applied to three area Thermal-Thermal-Thermal system with different cases. The program was written in MATLAB 2010 and executed on a PC with Intel(R) core(TM) i3 CPU with 2.13 GHz processor. The Simulations results are presented in Fig. 5. For the optimum AGC controller gain value, the corresponding results of optimum generators participations in generations, tie line exchanges are obtained by using formulae and MATLAB/Simulink Comparative presentation of Three Area ThermalThermal-Thermal AGC in restructured system with Open loop, with conventional controller, With BF controller is shown in Fig. 5. During the simulation study, error signals i.e. frequency and tie-line power is required for the controller is transferred to BF algorithm. In three area Thermal-Thermal-Thermal system, 1% disturbance is given to area 1. Using BF technology, local and global solutions can found simultaneously for the controller gain. The controller value which was calculated by the BF technique one derived from Trial and Error method in various perspectives, namely robustness and stability, performance. V.
proposed Bacterial Foraging based integral controller gives better performance than conventional controller. Nomenclature
Δ
s f ω K p1,2,3
The deviation Laplace domain derivative term Frequency Angular speed Generator Gain Constant
Tp1,2,3
Generator Time Constant
Pt Tt Pg
Turbine Output Power Governor Output Power
Tg
Governor Time Constant
Tij
Tie Line Coefficient
K i1,2,3
Integral Controller Gains
aij
Operator
Bi Pref
Bias Factor
Pl R Tw Di T apfi
Electric Load Variations
The Output of ACE Regulation Parameter Water Starting Time
ΔPDi / Δf i Time Constant of Hydro Governor ACE Participation Factors
DPM
DISCO Participation Matrix
cpf i
Contract Participation Factors
ACE
Area Control Error
Pi − jactual
Tie Line Real Power
Pi − jscheduled
Tie Line Scheduled Power Flow
Pi − jerror
Tie Line Power Error
BF
Bacterial Foraging
H-T-H
Hydro-Thermal-Hydro
T-T-T
Thermal-Thermal-Thermal Appendix-I
CONCLUSION
The paper encapsulates that, under deregulated environment AGC includes bilateral contracts. Controller gains are optimized by BF technique. DPM facilitates bilateral contracts simulation. Calculation time is saved by using BF algorithm. Thermal-Thermal-Thermal three areas system are considered in this paper and each area consists steam turbine. The simulation results show that the
Turbine Time Constant
K p1,2,3 = 120; T p1,2,3 = 20; a12 = −1; a23 = −1; a31 = −1; Tw = 1s; R1,2,3,4,5,6 = 2.4 Hz / p.u.Mw; f = 60 Hz T12 = 0.086 s; T23 = 0.086 s; T31 = 0.086 s; Tt1,2 = 0.3s; Tg 1,2 = 0.08 s;
(a) Frequency Comparision
(b)Tieline Power Comparision
Fig. 5. Shows Comparative performance of Three Area Thermal-Thermal-Thermal AGC under Deregulated Environment with Open loop, with conventional controller and With BF controller
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Biography: Kanika Wadhwa received B.E. in electrical engineering from BPRCE, MDU, Rohtak in 2010 . She is presently pursuing M.Tech from DCRUST, Murthal, Haryana,India. Her field of interest is Power System Dynamics and Control, Deregulation and FACTS. Email:
[email protected]
J.Raja was born in 1980 and received his B.E.degree and M.E. degree in the year 2001 & 2003 respectively, He completed his Ph.D degree in Pondicherry University, Puducherry, India. He has published technical papers in International & National Journals and Conferences. He is currently working as Assistant Director, National Power Training Institute, Ministry of Power, Faridabad, Haryana. India. His areas of interest are power system Controls and Stability, operational planning and control. S. K. Gupta received his B. E. and M. E. degree from University of Allahabad in 1990 and 1994 respectively. Apart from Deenbandhu Chhotu Ram University of Sc. & Technology, Murthal. He served as Lecturer at SLIET Longowal Punjab and Reader in YMCA Institute of Engineering Faridabad from 1993 to 1996 and 2004 to 2005 respectively. Presently he is working as a Professor and CHAIRMAN of Electrical Engineering Department Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat (Haryana). His field of interest is Power System Dynamics and Control, Deregulation and FACTS. Email:
[email protected] 
