DSM based Congestion Management in Pool Electricity Markets with FACTS Devices

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Energy Procedia

Energy Procedia (2011) 14 000–000 Energy00 Procedia (2012) 94 – 100 www.elsevier.com/locate/procedia

International Conference on Advances in Engineering ICAEE-2011)

DSM based Congestion Management in Pool Electricity Markets with FACTS Devices Ashwani Kumara and Charan Sekhar b b

a Associate Prof.Department of Electrical Engineering, NIT Kurukshetra, India-136119 Masters’s student,.Department of Electrical Engineering, NIT Kurukshetra, India-136119

Abstract In a restructured electricity market environment, when the producers and consumers of electric energy desire to produce and consume in amounts that would cause the transmission network to operate at or beyond one or more transfer limits, the system is said to be congested. The congestion in the system cannot be allowed to persist for a long time, as it can cause sudden rise in the electricity price and threaten system security and reliability. Congestion management (CM) is one of the most important and challenging tasks of the Independent System Operator (ISO) in the deregulated environment. FACTS devices can play an important role for demand side management and thereby controlling transmission line congestion. In this paper, demand side based CM approach to manage transmission line congestion has been presented for pool based electricity market model. The results have also been obtained for IEEE 24 bus test system.

© 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the organizing committee 2011 PublishedConference by Elsevieron Ltd. Selection and/or peer-review under responsibility of [name organizer] of©2nd International Advances in Energy Engineering (ICAEE). Keywords: Demand side management; congestion management; FACTS devices;pool electricity market;bid function.

Nomenclature Ng,Nb, Nl

Set of generators, number of buses and lines

Pgi, Qgi

Real and reactive power generation at bus-i with economic load dispatch

Pgni, Pdni

Real power generation and new demand at bus-i with demand side management

* Corresponding author. Tel.: +91-9416366091; fax: +911744238050. E-mail address: [email protected].

1876-6102 © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the organizing committee of 2nd International Conference on Advances in Energy Engineering (ICAEE). doi:10.1016/j.egypro.2011.12.901

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Ashwani Kumar and Charan Sekhar\ / Energy Procedia 14 (2012) 94 – 100 Author name / Energy Procedia 00 (2011) 000–000

Pdi, Qdi

Real and reactive power demand at bus-i with Pd as base case demand

Vi ,δi

Voltage magnitude and angle at bus-i

Yij = Gij + Bij

i-jth element of Y-bus matrix

Pgimin , Pgimax

Minimum and maximum real power generation limit

Q gimin , Q gimax

Minimum and maximum reactive power generation limit

Vi min ,Vi max

Upper and lower voltage magnitude limit

δ imin , δ imax

Upper and lower voltage angle limit

ΔPdup , ΔPddown

up and down demand at bus-i for congestion management

1. Introduction

The transmission congestion can cause market inefficiency as well may threaten the overall security of the system [1]. The congestion management (CM) is one of the important tasks of the ISO and utilizes market based approaches in competitive markets for alleviating congestion [1]. The market based approaches can be categorized based on locational marginal prices, price area zones, financial transmission rights, and generation rescheduling [2-14]. The basic concepts of transmission management, dispatch model, and role of the ISO for congestion management are proposed in [7]. Re-dispatching based schemes and application of FACTS to manage transmission congestion minimizing the congestion cost is presented by many authors [8-17]. Fang and David [9] proposed a transmission dispatch methodology as an extension of spot pricing theory in a pool and bilateral as well as multilateral transactions model. Authors proposed FACTS based curtailment strategy, redispatch based on based on Lagrangian Relaxation are proposed [10-14] for congestion management. Bompard et al. [13] developed a unified framework for mathematical representation of the market dispatch and re-dispatch problems, which is based on Congestion Management (CM) schemes and the associated pricing mechanisms. A unified framework has been used to develop and compare the various CM approaches so as to assess their efficiency and effectiveness of the market signals provided to the market participants. A comprehensive literature survey of congestion management methods and their categorization based on the methods used are presented in [14]. A re-dispatch based congestion management approach based on real and reactive power congestion distribution factors based zones and generator’s rescheduling was proposed in [15-16]. With the environmental constraints posing restrictions for installations of new transmission lines, FACTS technology all over the world is playing a key role for fostering the transmission network to be utilized to its full potential. Many authors developed the methodology to incorporate FACTS devices to manage the transmission congestion [9,17]. Demand control is emerging in the competitive markets as an important factor during congestion to play a vital role in efficient management of transmission network and alleviating congestion during such hours. In the present work, demand side management based congestion management approach has been proposed. The impact of Static Var Compensators (SVC) and Thyristor based Series Compensator (TCSC) has also been considered for congestion management. An optimal power flow problem using non-linear

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programming approach has been solved using Conopt solver of GAMS [18]. The results have been obtained for IEEE 24 bus Reliability Test System [19]. 2. Models of FACTS Devices

2.1. Static VAR Compensator(SVC) The Static Var Compensator (SVC) is a shunt compensation device comprising of fixed capacitor and variable inductor controlled through firing angle. Operation of SVC is controlled by the adjusting by the selection of the firing angle of GTOs (Gate turn off transistors) or thyristors to change the reactance of inductor for voltage control. During operation SVC behaves like shunt variable susceptance. For the steady state operation, constant susceptance model is considered [18]. In the active control range of SVC characteristic, current/susceptance and reactive power is varied to regulate voltage according to a slope (droop) characteristic. The slope is typically 1-5%. Connecting SVC on any bus i, reactive power is provided by SVC and can be modelled in an OPF problem as: min max QSVC = −Vi 2 ∗ BSVC , − B SVC ≤ BSVC ≤ B SVC

(1)

2.2. Model of TCSC TCSC behaves as variable capacitive reactance. It reduces the line reactance during the operation which improves the power transfer capability [18]. For providing the series compensation, the TCSC can be considered as a static series capacitive reactance –jXc. The Xij and Rij are the reactance and the resistance of the line i-j, respectively. The effective impedance of the line is given as: Xij,eff = Xij-Xc, X ij ,eff = (1 − k ) X ij , X cmin ≤ X c ≤ X cmax (2) k is the degree of compensation and lies between 0 and 1. The power flow equations with TCSC can be added in the optimal power flow model and are given as: Pijc = Vi2Gij, eff + ViV j (Gij, eff cos δ ij + Bij, eff sin δ ij )

(3)

Qijc = Vi2 ( Bij , eff + Bc ) + ViV j (Gij, eff sin δ ij − Bij, eff cos δ ij )

(4)

c = V 2G P ji j ij, eff + ViV j (Gij, eff cos δ ji + Bij , eff sin δ ji )

(5)

Q cji = −V j2 ( Bij, eff + Bc ) + ViV j (Gij, eff sin δ ji − Bij, eff cos δ ji )

(6)

3. DSM based Congestion Management Model

In a demand side management based congestion management model, the distribution companies (DISCOMs) submit their bids to the ISO for demand management. The ISO based on qualifying bids selects DISCOMs for demand control. The objective function minimizing the congestion cost has been solved using GAMS CONOPT solver with MATLAB and GAMS interfacing [16]. The base case generation schedule with the given demand has been obtained solving economic load dispatch problem. Knowing the schedule, the new schedule for generation and demand is obtained after congestion management. Objective function: Minimize congestion cost CC, the sum of the linear bid functions of the demand control as: CC =

nd

nd

∑ ( ) ∑ ΔC (P ) d =1

ΔC Pdup +

d =1

down d

(7)

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( )

ΔC Pdup = k 2 *

(

)

ΔC Pddown = k 2 *

ΔP

up d

ΔP

* bsmva +

down d

R

* bsmva +

up

(8)

d

R

(9)

down d

bsmva is the base MVA and Rdup and Rddown are the constants of bid function in $/hr. k1 and k2 are demand cost coefficients of a demand bid function submitted to the ISO. (a)Inequality constraints The power injection equations at each bus can be written as: Nb

[

]

Pi = ∑ Vi V j Gij cos(δ i − δ j ) + Bij sin (δ i − δ j ) ∀ i = 1,2, … N b j =1 Nb

[

]

Qi = ∑ ViV j Gij sin (δ i − δ j ) − Bij cos(δ i − δ j ) ∀ i = 1,2,… N b j =1

Nd

∑ ΔP

up d

d =1

−

Pdni = Pd +

Nd

∑ ΔP

down d

=0

−

(11) (12)

d =1

ΔPdup

(10)

ΔPddown

(13) (14)

Pi = Pgni − Pdni Qi = Qgi − Qdi

,

(b)Inequality constraints (i) Up/down demand limits for demand management, generation limit, voltage and angle limits are: up down up ΔPddown min ≤ ΔPd ≤ ΔPmax ΔPd min ≤ ΔPd ≤ ΔPmax

min Pgn

≤ Pgn ≤

max Pgn

, Q min g

≤ Qg ≤

Q max g

Vi min ≤ Vi ≤ Vi max δ imin ≤ δ i ≤ δ imax

(15) (16) (17)

(ii) Power flow limits for congestion management

(

Pij2 + Qij2 ≤ S ijmax

)

2

(18)

4. Results and Discussions

In this section, results have been obtained for three different cases of line congestion categorized as: SL: For single line (SL) congestion, power flow rating of 23rd line connected between buses 14 and 16 has been taken as 2.60 p.u. compared to its given rating of 5.00p.u. 2L: For two line (2L) congestion case, the rating of 18th line connected between buses 11 and 13 has been taken as 2.25 p.u. compared to its given rating of 5.00p.u. along with previous congested line. 3L: For three line (3L) congestion case, rating of 11th line connected between buses 7 and 8 has been taken as 1.50 p.u. compared to its given rating of 1.75p.u. along with previous two congested lines. The results have been obtained for IEEE 24 RTS [20]. The demand management is obtained for single line, two lines, and three line congestion cases are are shown for 3L case in Fig. 2. It is observed that for the single line congested case the demand at buses 1

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and 14 increases and load at bus 18 reduces its demand. For two line congestion case, demand at buses 1 and 14 goes up, demand at bus 13and 18 goes down. For the three line congestion case, the demand at buses 1, 14 and load at buses 6, 7 and 13 goes up and down respectively. The congestion cost is shown in Fig. 4. The demand management with SVC is shown for 3L congestion case in Fig. 2. The demand at bus 14th goes up demand and at buses 7 and 18 reduces demand. For this case demand at buses 1 and 14 increases and at buses 13 and 18 decreases its demand. Similarly, for 3L congestion case SVC is connected to bus at one of congested line. The demand at buses 1 and 14 increases and at bus 7, 13 and 18 reduces. The congestion cost is reduced compared to the case without SVC for all the cases of line congestion. This is due to the fast that with reactive power support obtained from SVC, the voltage profilers improves and thereby reduces the demand to be managed for mitigating congestion and thus reducing the congestion cost. The up and down demand, Pd and new Pd after removing the congestion for SL, 2L and 3L has been shown in Fig 3 for 3L case. The demand at bus 14 increases demand and at bus 7 reduces demand. For 2L congestion case, demand at bus 14 increases and at bus 7 reduces. For 3L congestion, case demand at buses 2 and 14 increases and at bus 7 reduces. The congestion cost reduces for all line congestion cases compared to the case without any FACTS devices. The congestion cost reduces due to the change in the flow patterns with TCSC reducing line reactance and easing the flow in the congested lines. This in turn reduces the demand to be managed and thereby reducing the congestion cost as shown in Fig. 4. 3.5

3.5 Pd Pdn dpdu dpdd

3

2.5

R e a l p o w e r (p .u .)

2.5

R e a l p o w e r (p .u .)

Pd Pdn dpdu dpdd

3

2

2

1.5

1.5

1

1

0.5

0.5

0

0

0

5

10

15

20

0

5

10

15

20

25

Load bus

25

Load bus

Fig 3. Demand management with TCSC on line-10 for 3L congestion

Fig 1. Demand management for 3L congestion

Cost ($.hr) Cost ($.hr) SVC Cost ($.hr) TCSC

3.5 Pd Pdn dpdu dpdd

1600 C o n g e s t io n c o s t ( $ / h r )

3

R e a l p o w e r (p .u .)

2.5

2

1.5

1

0.5

1400 1200 1000 800 600 400 200 0 SL

0

0

5

10

15

20

25

Load bus

Fig 2. Demand management with SVC at bus-11 for 3L congestion

2L

3L

Cases

Fig . 4. Congestion cost without and with FACTS devices

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5. Conclusions

The paper presented demand side management based transmission line congestion management for a pool market model. The congestion cost has been determined for different cases of line congestion without and with FACTS devices. From the above results we concluded that: (i) The congestion cost goes high with more number of lines getting congested in the network. (ii) With SVC and TCSC, the congestion cost is found lower than without ant FACTS device. (iii) FACTS devices control demand management effectively to manage congestion. It is observed lower with FACTS compared to the case without FACTS. Demand side management can play an important role for congestion control in the network. References [1]

Shahidepour Mohammed and Alomoush Muwaffaq. Restructured Electrical Power Systems Operation, Trading, and

Volatility., Marcel Dekker, Inc. New York;2001. [2]

H. Singh H, Hao S, and Papalexopoulos A. Transmission congestion management in competitive markets. IEEE Trans.

on Power Systems, 1998; (13), 2, 672-680. [3]

Bompard E, Carpenato E, Chicco G, and G. Gross G. The Role of load demand elasticity in congestion management and

pricing. in Proc. IEEE PES, Summer Meeting, 2000 (4); 2229-2234. [4]

S. Hao S and Shirmohammadi D. Congestion management with Ex Ante pricing for decentralized markets. IEEE Trans.

on Power Systems,2002 (17); 4, 1030-1036. [5]

Gan D. and Bourcier DV. Locational market power screening and congestion management: Experience and suggestions.

IEEE Trans. on Power Systems,2002; (17); 1;180-185. [6]

Alomoush MI and Shahidehpour SM. Fixed Transmission Rights for Zonal Congestion Management. IEE Proc. on

Generation, Transmission, and Distribution, 1999; (146); 5; 471-476. [7]

Alomoush MI and Shahidehpour SM,. Generalized model for fixed transmission rights auction. Electric Power System

Research, 2000; (54); 207-220. [8]

Shirmohammadi D, Wollenberg B, Vojdani A, Sandrin P, M. Pereira M, Rahimi F, Schneider T, and Stott B.

Transmission dispatch and congestion management in the emerging energy market structures. IEEE Trans. on Power Systems, 1998; (13); 4; 1466-1476. [9]

Fang RS and David AK. Transmission Congestion Management in an Electricity Market. IEEE Trans. on Power Systems,

1999; (14);2; 877-883. [10]

Srivastava SC and Kumar P. Optimal power dispatch in deregulated market considering congestion management. in Proc.

Int. Conf. on Electric Utility Deregulation and Restructuring and Power Technologies, DRPT, 2000; 53-59. [11]

Christie RD, Wollenberg BF and Wangstien I. Transmission Management in the Deregulated Environment. in Proc. of

the IEEE, 2000 (88); 2; 170-195. [12]

Wang X. Song YH, and Lu Q. Lagrangian decomposition approach to active power congestion management across

interconnected regions. IEE Proc. on Generation, Transmission, and Distribution, 2001; (148); 5; 497-503. [13]

Bompard E, Correia P, Gross G, and Amelin M. Congestion-Management Schemes: A comparative Analysis under a

unified framework. IEEE Trans. on Power Systems,2003; (18), 1; 346-352. [14]

Ashwani Kumar, S.C. Srivastava, and S.N. Singh, “Congestion management in competitive electricity markets-A

bibliographical survey,” Electric Power System Research, Vol. 76, No. 4, 2005, pp. 153-164. [15]

Kumar A, Srivastava SC, and Singh SN. A zonal congestion management approach using real and reactive power

rescheduling. IEEE Trans. on Power Systems, 2004; (18), No. 1; 554-562. [16]

Kumar A, Srivastava SC, Singh SN. A zonal congestion management approach using AC transmission congestion

distribution factors. Electric Power Syst. Res. 2004; (72); 11; 2004, 85–93.

99

100 [17]

Ashwani Kumar and/ Energy CharanProcedia Sekhar\ / 00 Energy Procedia 14 (2012) 94 – 100 Author name (2011) 000–000 Singh SN, and David AK. Congestion management by optimising FACTS device location. in proc. International

Conference on Electric Utility Deregulation and Restructuring and Power Technologies, 2000, City University, London, 4-7 April. [18]

Brooke A, Kendrick D, Meeraus A, Raman R, and Rasentha RE. A User’s Guide, GAMS Software, GAMS Development

corporation, 1998.. [19]

IEEE Reliability Test System. A report prepared by the Reliability Test System Task Force of the applications of

probability methods subcommittee. IEEE Trans. on Power Apparatus and Systems, 1979; (PAS-98); 2047-2054. [20]

Gyugyi L and HingoraniNG. Understanding FACTS: Concept and Technology of Flexible AC Transmission System..

Piscataway, NJ: IEEE Press 2001.

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