FACTS Devices Allocation With Control Coordination ... - IEEE Xplore

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FACTS Devices Allocation With Control Coordination Considering Congestion Relief and Voltage Stability Rony Seto Wibowo, Student Member, IEEE, Naoto Yorino, Member, IEEE, Mehdi Eghbal, Member, IEEE, Yoshifumi Zoka, Member, IEEE, and Yutaka Sasaki, Member, IEEE

Abstract—This paper presents an optimal allocation method for flexible ac transmission system (FACTS) devices for market-based power systems considering congestion relief and voltage stability. The purpose of the FACTS devices installation is to provide benefit for all entities accomplished by both minimizing annual device investment cost and maximizing annual benefit defined as difference between expected security cost (ESC) with and without FACTS devices installation. Different from previous approaches, the proposed method accurately evaluates the annual cost and benefits obtainable by FACTS devices installation by formulating a large-scale optimization problem that contains power flow analyses for a large number of system states representing annual power system operations. In addition, dynamic state transitions caused by specified contingencies are also simulated in the optimization problem to evaluate the effect of FACTS control actions as well as the other coordinated controls. The expected cost consists of operating cost under normal and contingency states along with their related probabilities to occur. Maximizing social welfare is the objective for normal state while minimizing compensations for generations re-scheduling and load shedding as well as maximizing social welfare are the objectives in case of contingency. Although installation cost of FACTS devices is required, they are useful as cost free means, which can reduce effectively the annual costs for generations re-scheduling and load shedding. Index Terms—Congestion relief, control coordination, expected security cost, FACTS devices allocation, voltage stability.

Active power generation. Active and reactive power demand, respectively. Linear or nonlinear equality constraints such as constant load power factor equation, active and reactive power balance equations, for current and stressed loading condition, respectively. Linear or nonlinear inequality constraints such as generation limits, load limits, voltage limits, transmission line thermal limits, for current and stressed loading condition, respectively. State vector of power system consisting of voltage magnitudes and phase angles. Load margin ( condition).

at current loading

Control vector excluding FACTS devices. Control vector of FACTS devices. Generation re-scheduling vector ( at normal state).

NOMENCLATURE

Load shedding vector ( state).

Operating cost under normal state.

at normal

Operating cost under contingency state.

Compensation paid to generator for increasing active power.

Generation cost and consumer benefit, respectively.

Compensation paid to generator for decreasing active power.

Set of generators and demands, respectively.

Compensation paid to demand for decreasing active power.

Manuscript received August 25, 2010; revised December 02, 2010; accepted January 05, 2011. Paper no. TPWRS-00678-2010. R. S. Wibowo is with the Graduate School of Engineering, Hiroshima University, Higashihiroshima 739-8527, Japan, and also with the Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia (e-mail: [email protected]; [email protected]). N. Yorino, Y. Zoka, and Y. Sasaki are with the Graduate School of Engineering, Hiroshima University, Higashihiroshima 739-8527, Japan (e-mail: [email protected]; [email protected]; [email protected]). M. Eghbal is with the Queensland Geothermal Energy Centre of Excellence (QGECE), The University of Queensland, Brisbane, Australia (e-mail: [email protected]). Digital Object Identifier 10.1109/TPWRS.2011.2106806 0885-8950/$26.00 © 2011 IEEE

Active power generation adjustment up. Active power generation adjustment down. Active power demand adjustment down. Generation ramping limit for adjustment up. Generation ramping limit for adjustment down. Period of time required to adjust generator output.

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. 2

IEEE TRANSACTIONS ON POWER SYSTEMS

Symbol indicating under normal state. Symbol indicating under contingency state. Symbol indicating under stressed loading condition. Investment cost of FACTS devices. TCSC capacities in MVar. SVC capacities in MVar. TCSC investment cost per MVar-installed. SVC investment cost per MVar-installed. Set of location candidates for TCSC. Set of location candidates for SVC. Installed capacity and maximum installed capacity of FACTS device candidate at location . Scalar variable used to represent system losses related to the stressed loading condition. I. INTRODUCTION

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N a deregulated electricity market, independent system operator (ISO) has authority to perform power dispatch to maximize social welfare while maintaining the system security [1]. Therefore, ISO is responsible to address transmission overload alleviation in normal and contingency states as well as to prevent voltage collapse by maintaining load margin. To achieve such secure and economic operation, flexible ac transmission system (FACTS) devices [2] are effective when properly installed. Recently, there has been growing interest in allocation of FACTS devices for relieving transmission congestion as well as improving voltage stability. References [3] and [4] have proposed optimal allocation methods for thyristor-controlled series capacitor (TCSC) to eliminate the line overloads against contingencies, where sensitivity index is introduced for ranking the optimal placement. Optimal allocation method for static var compensator (SVC) has been proposed in [5] using reactive power spot price index for contingency. In [6], voltage-sourced converter models such as for unified power flow controllers (UPFC) and static synchronous series compensator (SSSC) have been proposed for effective sensitivity analysis, which are applied to device allocation problem to maximize their control effects. Priority list method for TCSC allocation for congestion management has been proposed in [7] based on the locational marginal prices (LMPs) in the security constrained optimal power flow (OPF). In [8] and [9], metaheuristic techniques such as particle swarm optimization (PSO), genetic algorithm, and simulated annealing have been used to find optimal locations of FACTS devices in order to minimize installation cost and to improve system loadability. A systematic application of decision tree method has been proposed to identify critical transmission lines for optimal allocation of TCSC to maximize loadability in [10]. The concept of SVC allocation using expected annual operating cost has been proposed by the authors in [11], where

local OPFs for contingency states are incorporated in a single large-scale optimization problem; the Bender decomposition technique is utilized for optimal placement of SVC. The Bender decomposition technique requires the convexity to solve the problem but it is not always guaranteed. Reference [12] has proposed an improved solution using the Bender decomposition with a restart framework, where the objective is to maximize loading margin. In [13], the authors have proposed an extended formulation, where metaheuristic technique is used to avoid difficulties to solve non-convexity problem. Almost all of the previously mentioned methods have not taken into account the detailed formulation for contingency analysis in the FACTS allocation problem; instead, linear sensitivity indices for various objectives are used such as sensitivity index, LMP, etc. Those methods, as a result, may not provide fully accurate estimation of the effect of invested devices to the cost reduction and may result in wrong investment decision. Generally, FACTS devices are able to relieve congestions and decrease power losses as well as to reduce load shedding [11], [13] and generation re-scheduling [14], which may significantly contribute to decreasing annual cost of power system operation. Therefore, inclusion of those control actions into formulation is preferable and an exact evaluation of the cost effects by the FACTS installation is highly required, taking into account accurate contingency power flow analyses in a single optimization problem. This paper proposes an approach for power system planning by allocating multiple FACTS devices and evaluating its impact on operation problem in order to minimize annual total cost, implying to minimize devices investment cost and to maximize benefit due to devices installation. By extending the methods in [11], [13], and [15], the proposed method accurately evaluates the annual cost and benefits obtainable by FACTS installation by formulating a large-scale optimization problem that contains power flow analyses for a large number of system states representing annual power system operations. In addition, dynamic state transitions caused by specified contingencies are also simulated in the optimization problem to evaluate the benefit of FACTS control actions. The benefit is defined as the difference between expected security cost (ESC) with and without FACTS devices. To maximize the benefit, FACTS devices are optimally utilized to minimize ESC which is composed of normal and contingency states operating costs along with their probability to occur. During normal state, multiple FACTS devices are optimally controlled to relieve congestion, implying to maximize social welfare. During contingency states, the devices are firstly utilized to secure the system as well as minimize operating cost. Then, if violations still persist, generation re-scheduling and load shedding will be carried out. System security under all states is maintained by removing line overload and ensuring voltage stability which is done by satisfying minimum load margin requirement. Most of these treatments are new for FACTS devices allocation problem for market-based power system. TCSC and SVC are chosen because of their fast control response, low investment cost, and ability to efficiently increase loadability as discussed in [16] and [17]. Additionally, to determine the probability of contingencies per year, frequency and

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duration of each contingency per year are used. Several load levels representing distinctive conditions are used to accommodate yearly load pattern. Since the problem is highly nonlinear and mixed integer problem, an iterative method using Hybrid PSO is employed to avoid difficulty of the computation. PSO is utilized for determining FACTS devices locations and capacities, while OPF-based optimization is used to determine operating cost and optimal control of each state and solved by sequential quadratic programming (SQP). Modified IEEE 14-bus and 30-bus test systems are used to verify the effectiveness of proposed method. II. PROBLEM FORMULATION The problem is constituted of FACTS devices allocation subproblem (upper level) and operation subproblem (lower level) which consists of normal and contingency states. The former is to determine locations and installed capacities of devices while the latter is OPF problem to obtain minimum operating cost of each state using FACTS devices given by the upper level. Then, the operating costs, component of annual total cost, are fed back to upper level. The iterative process is repeated until termination criterion is satisfied.

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Then, the following expression is used to convert the investment cost into annual term: (6) where

is interest rate and

is lifetime of FACTS devices.

C. Operation Subproblem The objective function of operation subproblem is to minimize ESC with FACTS devices composing of operating cost under normal and contingency states, where each state is separately computed by local OPF as the following explanation. It implies minimizing operating cost of each state by utilizing all possible controls including FACTS devices within their installed capacities specified by the upper level subproblem. In order to minimize operating cost, each state is thus formulated as an OPF problem incorporating voltage stability criteria. The formulation of ESC is given as follows: Minimize (7)

(8)

A. Main Objective Function The main objective function of FACTS devices allocation is formulated as follows: Minimize (1) is the annual devices investment cost and where is the benefit in ESC due to devices installation which is defined as the difference between annual ESC with and without FACTS devices. The first term in (1) is determined by number and installed capacities of devices and explained in Section II-B while the second one is depended on locations and installed capacities of devices and described in Section II-C. ESC without FACTS devices corresponds to the present annual cost of power system operation which is assumed already optimized. B. FACTS Devices Model and Investment Cost In this paper, SVC is treated as a generator with no real power while TCSC is modeled as a variable capacitor connected in series with transmission line [3]. According to [17], the investment costs of TCSC and SVC can be formulated as follows: (2) (3)

(4)

and are hourly operating cost of normal state where and contingency for load level , respectively; is product of frequency and duration of contingency in a year for load level , and 8760 is hours in a year. As probability of each state is defined as divided by 8760, summation of all probabilities is equal to 1. Load duration curve based on yearly load pattern is then clustered into several load levels. In this paper, three load levels are used in simulations, i.e., 100%, 80%, and 60% of peak load. The first level represents peak load condition during which congestion is likely to occur not only during contingency but also during normal state. The second level corresponds to average load level in which congestion is likely to occur only during contingency. During the third level, there is slight possibility that congestion occurs during both normal and contingency states. 1) Normal State Subproblem: The objective function under normal state is expressed as minimizing generation cost and maximizing consumer benefit treated as negative cost. This function is usually called as social welfare maximization and can be written as follows: Minimize (9) subject to constraints for current loading condition

Constraint of FACTS devices is given as follows: (5)

(10)



constraints for stressed loading condition

constraints for current loading condition

(15)

(11) constraints for stressed loading condition

constraints to satisfy minimum loading margin

(16) (12) Constraint (10) is a general constraint for conventional OPF while constraints (11) and (12) are included for incorporating voltage stability into OPF formulation, where stressed loading condition represents critical operating point [18]. All of the power generations or demands under stressed loading condition written in (12), except reactive power generations, will not be constrained by generators or loads limit [19]. In this paper, it is assumed that submitted generation and demand bids are the true marginal cost and the true marginal benefit, respectively. Then, ISO clears the energy market based on those bids. Lagrange multiplier associated with real power balance equations will become market clearing price. 2) Contingency State Subproblem: If contingency occurs, corrective actions such as FACTS devices control, generation re-scheduling, and load shedding are utilized to avoid line overload and maintain load margin during contingency states. Generators participating to increase their power output will not only receive profit from selling additional energy but also compensations for providing emergency reserve while generators decreasing their power output will also obtain compensations for lost of opportunity cost [20]. If load shedding should be executed, demands will also be compensated for their interrupted load during contingency. During contingency, the objective functions are maximizing social welfare as well as minimizing compensations due to generation re-scheduling and load shedding. This function is formulated as follows: Minimize

constraints to satisfy minimum loading margin

(17) Constraints in (14) are intended to express coupling between normal state and contingency states and to ensure that compensations are always positive values. In case of contingency, demands have no option to increase their power exceeding the power demand determined in normal state. D. Overall Problem Formulation The overall problem formulation may be stated as follows: Minimize

subject to Equation (5) rewritten as

Equations (10)–(12) rewritten as (18) Equations (14)–(17) rewritten as

(13) subject to constraints for generation re-scheduling III. HYBRID PARTICLE SWARM OPTIMIZATION

(14)

PSO is a metaheuristic optimization method introduced by Eberhart and Kennedy[21]. PSO conducts search using a population of random generated particles, where each particle has its


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Fig. 1. Structure of the particles.

position and velocity represented as and . To adjust the position, velocity of each particle is calculated using current position , best position of particle so far , and global best posi. Velocity of each particle in tion of particle in population the next generation can be calculated as follows: (19) is inertia weight. Parameters and are random where numbers between 0 and 1 while and are positive constants. To determine the impact of previous velocity to the current velocity, weighting function in (20) is used: (20) and are maximum and minimum of inertia where weight, respectively; and iter are maximum number of iteration and current iteration, respectively. Current position or searching point can be modified by the following equation:

Fig. 2. Flowchart of proposed approach. TABLE I SUITABLE LOCATIONS AND CAPACITIES OF FACTS DEVICES IN VARIOUS LOAD MARGINS (IEEE 14-BUS SYSTEM)

(21) Another method used to develop proposed Hybrid PSO approach is SQP. This method is used to solve OPF problem. IV. SOLUTION ALGORITHM A two level hybrid PSO/SQP method is utilized to solve the overall problem formulation. The upper level is considered as a discrete and continuous problem and solved using standard PSO. Discrete problem is in locating FACTS devices while continuous problem is in determining devices installed capacities. The result from the upper level is passed to the lower level and used in the operation subproblem. This subproblem is composed of multiple states, where each state is classified as a continuous problem. The problem of each state is formulated as OPF incorporating FACTS devices and solved by SQP. Modified Matpower version 2 [22] is used to solve the each state problem using software Matlab 6.5. The lower level will provide ESC with FACTS devices, component of fitness function, as feedback to the upper level. The algorithm of the proposed approach can be described as follows. 1) Read power system data and contingency data such as frequency and duration of contingency per year. 2) Generate a set of particles representing installed capacity of SVC and maximum equivalent reactance of TCSC which correspond to installed capacity of TCSC. Locations of devices are also included in the particles. The structure of particles is described in Fig. 1.

TABLE II REQUIRED CAPACITIES OF TCSCS AND SVCS IN VARIOUS STATES ( 0:1)

3) Calculate OPF incorporating FACTS devices derived from particles, for normal and contingency states. Obtain the operating cost and required devices capacities for each state. 4) Calculate ESC using operating costs of all states with their associated probabilities to occur. 5) Calculate devices investment cost using (4). 6) Calculate fitness function (1) using both annual devices investment cost and difference between annual ESC with and without FACTS devices. 7) Determine Pbest and Gbest for the next iteration.



TABLE III OPERATING COST IN VARIOUS STATES FOR LOAD LEVEL 100% AND 80% (

8) Update the population and go to step 3. Repeat until the last iteration.

0:1)

TABLE IV COST RESULTING FROM SIMULATION (

0:1)

V. CASE STUDY We have performed the numerical simulations based on the proposed procedure given by Fig. 2. Program is run on a computer with Intel Core 2 Duo 3.00 GHz and 2 GB of RAM. A. Location Candidate and Contingency Selection To reduce the number of location candidates, a preliminary computation based on sensitivity index approach has been carried out to rank locations for FACTS devices installation, where a single device is installed at a line or bus to evaluate ESC as in (7). This kind of linear sensitivity approach has been widely applied to FACTS allocation problems as in [3] and [4]. Since such a sensitivity approach is effective but is not necessarily reliable, we will apply the method only to the preliminary computation. Thus, this process has been repeated to check all locations to select the best five of lines and buses. In addition, only contingency significantly affecting cost in (7) will be considered. B. IEEE 14-Bus System Case Study In order of priority, the bus candidates for SVC are buses 12, 13, 10, 14, and 11 while the line candidates for TCSC are lines (6–12), (2–4), (3–4), (9–14), and (7–9). The result of simulations can be seen in Table I. This table provides suitable locations and installed capacities of FACTS devices in various minimum load margins. As shown in this table, set of FACTS devices locations recommended by this table does not entirely confirm the rank of FACTS devices locations candidates in the previous paragraph. As is mentioned before, buses 12 and 13 are the first and the second best location and candidates in minimizing ESC. However, for , it is found that installing SVC at both buses 12 and 13 is expensive from the cost analysis. Therefore, instead of locating SVC at bus 13, it is suggested to be installed at bus 10. Moreover, lines (9–14) and (7–9) are preferable for locations of TCSC to lines (2–4) and (3–4). Table II shows required capacities of FACTS devices in various states, implying control coordination among multiple devices when handling normal and contingency states. This table also suggests that all installed capacities of FACTS devices are determined by required capacity under contingency at line (6–13). The highest required capacity of specific location

in Table II corresponds to the installed capacity in Table I with . Table III provides operating cost of each state consisting of social welfare, generation re-scheduling, and load shedding costs, without and with FACTS devices. During load level 100%, social welfare improves under both normal and contingency states. Congestion relief and loss reduction may contribute to the social welfare improvement under normal state. Moreover, it is observed that load shedding can considerably be reduced in almost all of contingency states. Social welfare improvement during load level 80% is less significant than that during load level 100%, but load shedding can be avoided for all contingency states. Table IV describes the annual cost of system operation, without and with FACTS devices. As shown in this table, there will be annual cost saving more than 2 million dollars due to utilization of FACTS devices. Detailed load shedding and generation re-scheduling for minimum load margin 0.1 are given in Figs. 3 and 4. These figures illustrate that load shedding and generation re-scheduling can significantly be reduced by FACTS devices installation. Almost all of load shedding in contingency states can be eliminated. However, small amount of load shedding still exists in case of contingency at line (9–10). Power generation re-scheduling also considerably decrease under majority of contingency cases but slightly increase when contingencies occur at lines (6–11) and (6–13). Fig. 5 describes load at bus 10, where interrupted part under each contingency is indicated by black or shaded bar. As is seen, the minimum of uninterrupted loads for system with FACTS devices is 19.8 MW, which is guaranteed as uninterruptible load for power transaction. Such uninterruptible loads at individual buses are given in Fig. 6. It is understood from this figure that FACTS devices utilization improves loadability and therefore increases amount of uninterruptible loads. As shown in this figure, interruptible load at almost all of buses can be


Fig. 3. Total load shedding under various contingencies (load level 100%, 0:1).

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Fig. 6. Uninterruptible load without and with FACTS devices utilization (load > 0:1). level 100%, TABLE V STATISTICAL STUDY WITH 20 RUNS

Fig. 4. Total generation re-scheduling under various contingencies (load level 0:1). 100%,

Fig. 7. Annual cost saving under various minimum load margins. TABLE VI SUITABLE LOCATIONS AND CAPACITIES OF FACTS DEVICES WITH (IEEE 30-BUS SYSTEM)

Fig. 5. Uninterrupted load at bus 10 under various contingencies (load level 100%, > 0:1).

0:1

combination between TCSC and SVC is the best solution for congestion relief and voltage stability improvement while SVC is more effective than TCSC. C. IEEE 30-Bus System Case Study

eliminated while small amount of interruptible load at bus 10 still exists. To confirm the robustness of the proposed approach in reaching the optimal or near optimal solution, 20 independent runs have been conducted with the same maximum iteration, i.e., 15 iterations. As can be seen in Table V, simulation using 40 particles is preferable with standard deviations less than 0.5%. Fig. 7 shows annual cost saving that can be achieved by FACTS devices installation. As can be seen in this figure,

The proposed approach is also applied to modified IEEE 30-bus system. The bus candidates for SVC, in order of priority, are buses 30, 29, 27, 25, and 26 while the line candidates for TCSC are lines (15–23), (27–30), (27–29), (23–24), and (10–22). The considered line contingencies are at lines (6–8), (12–15), (10–21), (10–22), (15–23), and (27–30). Table VI shows the suitable location and size after running the simulation with 40 particles and 15 iterations. The calculation time is 18.1 h. By installing those devices, the annual cost saving is $5 049 974.



TABLE VII GENERATOR DATA

TABLE VIII DEMAND DATA

Although the validity of the formulation and the feasibility of the problem have been confirmed in this paper, reducing the calculation time is still necessary to deal with larger system. Applying parallel computing to the proposed approach might be one of the solutions for the calculation time improvement. It can be done by distributing the computation of contingency states or distributing the computation of ESC with FACTS devices, where the devices are represented by particles generated by PSO. VI. CONCLUSION In this paper, we have presented multiple FACTS devices allocation for congestion relief and voltage stability in deregulated electricity market environment. Control coordination among FACTS devices, generation re-scheduling, and load shedding under contingency are also considered. By using the proposed approach, detailed cost and benefit evaluation can be provided. The result of FACTS devices allocation shows that installed capacity of FACTS devices is determined by required capacity under contingency state. Therefore, exact power flow analysis for contingency states is necessary in the optimization process, as well as incorporation of devices control coordination. In addition, locating multiple FACTS devices based on linear sensitivity index, although effective and widely used, may not always result in effective allocation. In the proposed method, fast sensitivity approaches are useful as basis of location candidate reduction. APPENDIX Setting of parameters and constants used in simulation are given as follows. and are assumed to be 5 1) Ramping limit is 10 min [20]. MW/min while and are 0.4 times to 2) We assume that both power price in normal state [23]. Meanwhile, is $10 838 per MWh-curtailed load [24]. 3) Interest rate and life time of devices are assumed to be 0.04 and 15 years, respectively.

, and used in PSO are 1, 1, 4) Parameters , , 0.9, and 0.3, respectively. 5) Maximum equivalent reactance of TCSC is assumed 0.7 times to the reactance of corresponding line while maximum installed capacity of SVC is 0.3 pu or 30 MVar. 6) The duration of load levels 100%, 80%, and 60% are assumed to be 12, 6, and 6 h per day, respectively. Data of generators and demands are given in Tables VII and VIII, respectively. REFERENCES [1] M. Shahidehpour, H. Yatim, and Z. Li, Market Operations in Electric Power Systems. New York: Wiley, 2002. [2] Y. H. Song and A. T. Johns, “Flexible ac transmission systems (FACTS),” in IEE Power and Energy Series, U.K., 1999. [3] S. N. Singh and A. K. David, “Optimal location of FACTS devices for congestion management,” Elect. Power Syst. Res., vol. 58, no. 2, pp. 71–79, 2001. [4] Y. Lu and A. Abur, “Static security enhancement via optimal utilization of thyristor-controlled series capacitor,” IEEE Trans. Power Syst., vol. 17, no. 2, pp. 324–329, May 2002. [5] J. G. Singh, S. N. Singh, and S. C. Srivastava, “An approach for optimal placement of static VAr compensators based on reactive power spot price,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 2021–2029, Nov. 2007. [6] X. Fang, J. H. Chow, X. Jiang, B. Fardanesh, E. Uzunovic, and A.-A. Edris, “Sensitivity methods in the dispatch and siting of FACTS controllers,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 713–720, May 2009. [7] N. Acharya and N. Mithulananthan, “Locating series FACTS devices for congestion management in deregulated electricity markets,” Elect. Power Syst. Res., vol. 77, no. 3-4, pp. 352–360, 2007. [8] M. Gitizadeh and M. Kalantar, “A novel approach for optimum allocation of FACTS devices using multi-objective function,” Energy Convers. Manage., vol. 50, no. 3, pp. 682–690, 2009. [9] R. Benabid, M. Boudour, and M. A. Abido, “Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization,” Elect. Power Syst. Res., vol. 79, no. 12, pp. 1668–1677, 2009. [10] E. A. Leonidaki, D. P. Georgiadis, and N. D. Hatziargyriou, “Decision trees for determination of optimal location and rate of series compensation to increase power system loading margin,” IEEE Trans. Power Syst., vol. 21, no. 3, pp. 1303–1310, Aug. 2006. [11] N. Yorino, E. E. El-Araby, H. Sasaki, and S. Harada, “A new formulation for FACTS allocation for security enhancement against voltage collapse,” IEEE Trans. Power Syst., vol. 18, no. 1, pp. 3–10, Feb. 2003. [12] R. Minguez, F. Milano, R. Zarate-Minano, and A. J. Conejo, “Optimal network placement of SVC devices,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 1851–1860, Nov. 2007. [13] M. Eghbal, N. Yorino, E. E. El-Araby, and Y. Zoka, “Multi load level reactive power planning considering slow and fast VAR devices by means of particle swarm optimization,” IET Trans. Gen., Transm., Distrib., vol. 2, no. 5, pp. 743–751, 2008. [14] R. Zárate-Miñano, A. J. Conejo, and F. Milano, “OPF-Based security redispatching including FACTS devices,” IET Trans. Gen., Transm., Distrib., vol. 2, no. 6, pp. 821–833, 2008. [15] R. S. Wibowo, N. Yorino, M. Eghbal, Y. Zoka, and Y. Sasaki, “FACTS devices allocation for congestion management considering voltage stability by means of MOPSO,” in Proc. IEEE Transmission & Distribution Conf. Expo.: Asia and Pacific, Seoul, Korea, 2009, pp. 1–4. [16] S. Gerbex, R. Cherkaoui, and A. J. Germond, “Optimal location of multi-type FACTS devices in power system by means of genetic algorithm,” IEEE Trans. Power Syst., vol. 16, no. 3, pp. 537–544, Aug. 2001. [17] K. Habur and D. O’Leary, FACTS for Cost Effective and Reliable Transmission of Electrical Energy. [Online]. Available: http://www. worldbank.org/html/fpd/em/transmission/facts_siemens.pdf. [18] W. D. Rosehart, C. A. Canizares, and V. H. Quintana, “Multiobjective optimal power flow to evaluate voltage security cost in power networks,” IEEE Trans. Power Syst., vol. 18, no. 2, pp. 578–587, May 2003. [19] A. J. Conejo, F. Milano, and R. Garcia-Bertrand, “Congestion management ensuring voltage stability,” IEEE Trans. Power Syst., vol. 21, no. 1, pp. 357–364, Feb. 2006.


[20] PJM, Manual 11: Scheduling Operations, 2010. [Online]. Available: http://www.pjm.com. [21] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Networks, Australia, 1995, pp. 1942–1948. [22] R. D. Zimmermann and D. Gan, “Matpower a Matlab power system simulation package,” User’s Manual Version 2, Dec. 1997. [23] Monitoring Analytics, LLC, State of the Market: PJM, 2004. [Online]. Available: http://www.monitoringanalytics.com,reports/PJM_State_of_the_Market/2004. [24] J. A. Momoh, Y. Wang, M. Elfayoumy, B. Mittelstadt, S. K. Agarwal, and R. Adapa, “A value-based reliability enhancement scheme for bulk transmission system planning,” IEEE Trans. Power Syst., vol. 13, no. 4, pp. 1541–1547, Nov. 1998.

Rony Seto Wibowo (S’08) received the B.S. degree in electrical engineering from Institut Teknologi Sepuluh Nopember (ITS) Surabaya, Indonesia, and the M.S. degree from Institut Teknologi Bandung, Indonesia. He is currently on study-leave and pursuing the Ph.D. degree at the Department of Artificial Complex Systems Engineering, Hiroshima University, Higashihiroshima, Japan. He joined ITS Surabaya, Indonesia in 2000. His research interest is in power system operation and planning

Naoto Yorino (M’90) received the B.S., M.S., and Ph.D. degrees in electrical engineering from Waseda University, Shinjuku, Japan, in 1981, 1983, and 1987, respectively. He is a Professor in the Graduate School of Engineering, Hiroshima University, Higashihiroshima, Japan. He was with Fuji Electric Co. Ltd., Japan from 1983 to 1984. He was a Visiting Professor at McGill University, Montreal, QC, Canada, from 1991 to 1992. Dr. Yorino is a member of the IEE of Japan, CIGRE, iREP, and ESCJ.

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Mehdi Eghbal (M’10) received the B.S. degree in electrical engineering from Ferdowsi University of Mashad, Mashad, Iran, in 1998, the M.S. degree from Tarbiat Modares University, Tehran, Iran, in 2001, and the Ph.D. degree from Hiroshima University, Higashihiroshima, Japan, in 2009 in the field of artificial complex system engineering. Currently, he is with the Queensland Geothermal Energy Centre of Excellence (QGECE), University of Queensland, Brisbane, Australia. His research interest lies in application of heuristic techniques in power system planning and operation and renewable energies.

Yoshifumi Zoka (M’99) received the B.S., M.S., and Ph.D. degrees from Hiroshima University, Higashihiroshima, Japan, in 1995, 1997, and 2002, respectively. He was a Visiting Scholar at the University of Washington, Seattle, from 2002 to 2003. He is currently an Associate Professor at the Graduate School of Engineering, Hiroshima University. His research interests are power system planning, stability, and control problems.

Yutaka Sasaki (M’08) received the B.S. degree in electrical engineering and the M.S. and Ph.D. degrees in information science from Hokkaido University, Sapporo, Japan, in 2004, 2006, and 2008, respectively. He is currently an Assistant Professor at the Graduate School of Engineering, Hiroshima University, Higashihiroshima, Japan. His research interests include optimal planning and operation of distributed generations