Valuing the Dynamic Power Flow Control of FACTS ...

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Valuing the Dynamic Power Flow Control of FACTS devices under Uncertainties Gerardo Blanco, Member, IEEE, Ulf Häger Student Member, IEEE, Fernando Olsina, Christian Rehtanz, Senior Member, IEEE

Abstract—Restructuring of the power industry that have arisen from the unbundling of the electrical industry have led to complex and still unsolved problems related to transmission system expansion owing to the singular characteristics of their investments. These difficulties are currently issues of considerable interest for researchers and policy-makers since the lack of adaptation of the transmission infrastructure may damage operations and free competition in the emerging electrical sector. In this context, some degree of dynamic control within the transmission investments is deemed to be necessary in order to face the increasing uncertainties of the new market scenarios through contingent claims, which allow the planner to adapt the investment under scenarios where the uncertain variables unfold unfavorably. Under this conjuncture, this paper presents an approach for valuing the dynamic power flow control of FACTS devices under uncertain variables of liberalized power markets as well as the evaluation of the flexibility of power transmission investments through a Real Option Valuation approach based on the Least Square Monte Carlo method. In order to illustrate the proposed valuation approach, a study case is presented, where it shows that the flexibility of the dynamic controllers under uncertainties could plenty justify the higher cost of these devices. Index Terms-- Dynamic programming, flexibility, least square Monte Carlo, risk analysis, stochastic simulation, series compensation, uncertainty.

I. INTRODUCTION

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N the last decades, a transition towards the unbundling of many sectors of the economy has been registered worldwide. The energy markets have been included within this restructuring movement, particularly the gas and the electricity sectors. The pioneering reforms of Chile (1982), followed by United Kingdom (1990) and Argentina (1992) triggered a global wave. Several countries have restructured their electricity industries, changing from being dominated by vertically integrated regulated monopolies to deregulated industries by unbundling the different segments of the industry, i.e. the production segment (generation) from the G. Blanco is with the Facultad Politécnica at Universidad Nacional de Asunción (UNA), Universitary Campus, San Lorenzo 2111, Paraguay (e-mail: [email protected]). F. Olsina is with Argentinean Research Council (CONICET) at the Institute of Electrical Energy (IEE) at National University of San Juan (UNSJ), Av. Lib. Gral. San Martín 1109 (O), J5400ARL, Argentina. (e-mail: [email protected]). Ulf Häger and Christian Rehtanz are with the Institute for Power Systems and Power Economics, Technische Universität Dortmund, 44221, Dortmund, Germany (email: [email protected], [email protected]).

service segments (transmission & distribution). The transformation of power sector has created new challenges to the electric transmission business, mainly related to the network expansion. The electricity transmission system is the cornerstone on which supply-demand coordination depends. The existent transmission grids were designed for transporting, according to predefined reliability levels, significant energy bulks from the generation location to the distribution points for the final consumption. Since the restructuring, the power transmission sector has evolved into a neutral enabler of the competitive generation sector. Therefore, nowadays, the transmission network simultaneously plays a role of substitute and complement of the power generation. Under this competitive environment, the transmission grid must evolve to be able to fulfill its new role in line with future demand requirements, the evolution of the generation subject to private investment, as well as the changing regulation. Currently, the networks are often not adapted to the new power flow patterns of the emerging power markets, and consequently, the transmission lines usually have important congestion levels. This has a harmful impact on the power systems development, since it significantly reduces the competition in electricity markets as well as the levels of supply reliability. In this context, areas with sustained economic growth require frequent and significant investments in capacity expansion of transport networks. The transmission expansion problem is characterized by the nature of the investments involved. Scale economy, irreversibility, low adaptability and intensive capital are some of the features of the expansion network investments. In addition, transmission assets are long lived. Therefore, the value of an investment is amortized over a long period of time and depends on many other developments. Consequently, the transmission investments are significantly exposed to the long term uncertainties. These uncertainties involved in the transmission expansion planning are better coped with flexible investments. Planners need flexibility for seizing opportunities or avoiding losses upon the occurrence of unfavorable scenarios. This flexibility may include various actions at different stages of the investment horizon, such as the options to defer, expand, or even abandon the project. In this context, flexibility has a substantial value, and must be taken into consideration within the decision-making process. Obviously, the inevitable uncertainties associated with Transmission Investments (TIs) are better managed with

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flexibility rather than the fixed scenario expectations implicitly assumed discount cash flow (DCF) approach. Strategic flexibility is a risk management technique that is increasingly gaining research attention, as it allows properly managing major uncertainties, which are unresolved at the time of making investment decisions. However, expressing the value of flexibility in economic terms is not a trivial task and requires sophisticated valuing tools [1]. The Real Option Valuation (ROV) technique provides a well-founded framework –based on the financial option theory- to assess strategic investments under uncertainty [1]. The real-options approach gives a new vision into the effect of uncertainty on an investment assessment problem, vision that runs counter to traditional thinking. When management is asymmetrically positioned to capitalize on upside opportunities but, at the same time, can cut losses on the downside, more uncertainty can actually be beneficial when valuing option-like investments. Gains can be made in highly uncertain or volatile markets by staging the investment because of the exceptional upside potential and limited downside losses, since management can default on planned investments or simply not proceed to the next stage [2]. Power investments include intrinsic flexibility by multiple strategic options, such as: the option to expand in a further stage, postpone and/or abandon the investment later [3]. Usually, grid reinforcements are primarily focused on investments in new Transmission Lines (TL). This kind of TIs has a huge level of irreversibility, which leads to a high risk exposure to long-term uncertainties. An alternative of dealing with these shortcomings is the installation of fixed-series compensation (FSC), in order to increase the use-capacity of existing transmission of power systems allowing to operate the network near the safety margins set up according to the transient stability, and consequently, the power flow through the existing transmission lines can be raised to their thermal limits [3]. Hence, it would be possible to defer the investment needed to build new lines [4]. In addition, expanding the grid in this conventional manner might not be the best way to deal with some constraints, especially those that arise due to the lack of control over power flows [3]. From this point of view, an option for coping with the lack of power flow control is the installation of Flexible AC Transmission Systems (FACTS), in particular Thyristor-Controlled Series Capacitor (TCSC), instead of building TLs. TCSCs are capacitive reactance compensators, which consists of series capacitor bank shunted by a thyristorcontrolled reactor in order to provide smooth variation of series capacitive reactance. One of the special features of TCSC over conventional series compensation scheme is the vernier control. This vernier control enables the TCSC controller to vary the effective impedance of the transmission line over a much wider range. This continuous, dynamic and flexible compensation according to the unfolding of the uncertain power market evolution could be significantly valuable. The advantage of the fixed-series compensation over the TCSC is that the cost of this alternative is much lower.

In addition, fixed-series compensators and FACTS investments exhibit features that considerably improve their flexibility, e.g. modularity and higher reversibility. Thus, the inclusion of these alternatives in the TI portfolios adds new strategic options to the grid expansion plan that significantly improves its flexibility. For instance, a fact often ignored is the ability of relocating or selling out the device at a substantial value should be considered. This flexibility is relevant in order to make optimal TI decisions and should consequently be fairly valued. Any attempt at valuing flexibility almost naturally leads to the notion of Real Options (RO) [1]. RO models often present larger complexity than the financial ones. Certainly, real projects exhibit a intricate set of interacting options, complicating their evaluation. In this context, Longstaff et al. [5] proposed a method for solving interacting financial options, based on Monte Carlo simulations. Recently, Gamba [6] reported an extension of this approach for valuing capital investment problems with embedded options considering the interaction and interdependence among them. This paper extends the approach proposed in [7] to the transmission expansion problem with many embedded real options and fixed as well as dynamic compensation as investment alternatives. Hence, this work considers FSC and TCSC as TI alternatives, appropriately valuing the flexibility of relocating and abandoning them as well as the deferral option on all the expansion projects This article aims at shedding light on the value of FACTS device dynamic control under uncertainties in order to improve the flexibility of the transmission expansion plans by allowing a dynamic adaptation of the transmission grid to the changing scenarios. In a study case, a static expansion alternative (FSC) is compared to a flexible investment alternative (transmission TCSC) in order to explore the investment signals that each approach provides. II. POWER FLOW CONTROL BY SERIES COMPENSATION The aim of this section is to illustrate the advantage of increased control flexibility provided by the use of a TCSC instead of FSC at a simplified power flow example. The test calculations were carried out with the network model presented in Fig. 1. In this model a generation cluster at node A feeds loads at the nodes B, C and D. The transmission lines limiting the transmission capability of the grid are the lines LAB, LAC and LAD. In order to increase the capacity of this transmission corridor, line LAD is equipped with TCSC series compensation. The compensation of the TCSC can be adjusted between -20% and +70% of the impedance of line LAD. The equivalent impedance as a function of the firing angle α of the TCSC is calculated according to [8]-[9]:

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(

)

λ=

−XC

(2)

XL

σ = 2 (π − α )

(3)

XC is the series capacitor and XL is the shunt reactance of the TCSC. We assume that the firing angle of the TCSC can be controlled in steps of 1°. Table I presents the firing angles which have been chosen for our load flow calculations and the corresponding relative compensation.

1. Equal distribution (B: 1/3, C: 1/3, D: 1/3) 2. Major load at node B (B: 40%, C: 30%, D: 30%) 3. Major load at node D (B: 30%, C: 30%, D: 40%) 2400 Equal distribution 2300

PL [MW]

⎡ ⎤ 2 σ + sin σ ⎢1 − λ ⎥ … ⋅ ⎢ ⎥ 2 π ⎢ ⎥ λ −1 ⎢ ⎥ ⎛ ⎞ σ ⎥ (1) XTCSC (α) = XC ⎢ 4λ2 cos2 ⎜⎜ ⎟⎟ ⎢ ⎜⎝ 2 ⎟⎟⎠ ⎛ ⎞⎟⎥⎥ λσ σ ⎢ ⋅ ⎜⎜⎜λ tan − tan ⎟⎟⎥ ⎢+ ⎜ 2 2 2 ⎟⎠⎥ ⎢ ⎝ π λ −1 ⎢⎣ ⎥⎦

2200 Major load at node B

2100 2000

Major load at node D

1900 -20%

0%

20%

40%

XTCSC [%]

60%

80%

Fig. 2. Transmittable active power for different load distributions.

It is obvious that the optimal compensation level differs for different load distributions. The TCSC provides flexibility to increase the maximum transmittable active power. If a FSC is applied instead of a TCSC, then the compensation level is not adjustable any more. A FSC would correspond to a vertical line in the figure. This kind of power flow control is only optimal for a single load distribution. For other load distributions the maximum transmittable power is reduced compared to the application of a TCSC. In real transmission systems worldwide there occur many uncertainties and frequently happen changing load flow scenarios which cause similar situations as described above. III. FLEXIBLE TRANSMISSION SYSTEM INVESTMENTS Fig. 1. Load flow network model with TCSC. TABLE I RELATIVE COMPENSATION AT FIRING ANGLE

α (º)

XTCSC (α) [%]

90

1,28%

132

4,80%

138

10,50%

140

16,43%

141

22,43%

142

34,48%

143

70,78%

148

-20,49%

150

-14,58%

154

-10,04%

180

-6,72%

The results of our test calculations are presented in Fig. 2. This figure shows the maximum total load which can be transmitted through the grid as a function of the compensation level of the TCSC. The maximum loading of the grid is reached when one of the transmission lines LAB, LAC or LAD reaches a loading of 100%. We present the results for three different load distributions:

As was mentioned in [7], the investments in the transmission typically exhibit intrinsic characteristics that affect their performance and should be taken into account during their evaluation. Some of these features are [10]: • Economies of scale, i.e. lower unit cost as long as the size of the expansion increases. • A substantial fraction of the required capital must be paid out prior to the commissioning of the new transmission line, while the depreciation takes many years, even decades. • Investment projects in the transmission system are vulnerable to unforeseen scenarios along the investment horizon. • In general, opportunities to invest in the transmission system are not the now-or-never type, i.e. have the investment can be deferred. Thus, an assessment methodology for transmission investments must be able to incorporate these features in a quantitative manner, which can be integrated into three fundamental characteristics: irreversibility, long-run uncertainties and flexibility. Furthermore, it has been demonstrated that the classic method of NPV can be misleading for assessing irreversible investments with flexibility options [2]. In this context, the theory of real options has been presented as a modern evaluation technique for valuing flexible projects under

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uncertainty, which applies methods derived from the theories of financial theory for the valuation of securities. This section addresses the problem of valuation of flexible transmission investment portfolios on the basis of the social welfare of the electricity market. It proposes a methodology based on real options approach for valuing the flexibility of strategic investments in the transport network and finding the optimal timing of the execution of the investment alternatives [7]. In this sense, as it was mentioned before, FSC and TCSC devices seem to be a suitable alternative for increasing the flexibility of the TI portfolios (TIP). In this context, valuing the gained flexibility by the dynamic power flow control of the FACTS devices is a key issue that still remains uninvestigated. In addition, as it was before pointed out by the authors in [7], the main flexibility options provided by the FACTS, besides the dynamic control, are: the abandon option and the relocation option. A. Abandon option According to the typical cost structure of the FACTS investments, the power electronic components represent about 50% of the total cost [11]. Accordingly, the scrap value of the FACTS devices should be considerable. Recently, in a previous article from the authors [12], it was highlighted the importance of the strategic option to resell the devices in the future if complementary investments have been executed (i.e. TLs) or the evolution of the power market uncertainties unfolds unfavorably. B. Relocation option As remarked in [13], new compensation devices designs allow installation so that they can easily be relocated: e.g. power electronics and auxiliary components are installed in a movable container, whereas high voltage equipment is installed fixed on-site. This new characteristic opens the option to relocate the device according to the development of system uncertainties. This paper proposes a methodology to quantify in economic terms the value of this option. IV. VALUATION OF FLEXIBLE TIP INCLUDING FSC & FACTS The power market model presented here can be characterized as a fundamental model (bottom-up model), since the long-run development of the power market is determined by the explicit modeling of the fundamental variables having direct impact on long-term evolution of supply and demand. As an initial assumption, the developed market model does not account for neither capacity payments nor ancillary services. Within this model, the research work develops a suitable approach for assessment of Transmission Investment Portfolios (TIPs) considering FACTS devices performance under uncertain long-run scenarios. The evolution of fundamental uncertain variables is modelled through appropriate stochastic processes. Some of these are power demand growth and power generation cost evolution. The reduction of the system costs incurred for serving the load demand over the optimization horizon is used as the measure to evaluate the economic performance of the proposed network upgrades. Under this framework, the value of a TIP is defined by the increase (or decrease) of the social welfare

resulting from executing the investments considered in the portfolio. Taking into account an inelastic demand, the incremental social welfare should be quantified through the generation cost savings between the base scenario (BS, without investment) and the investment scenario (IS, with the investment executed). The proposed methodology essentially consists of two main layers. These layers are the following: A. Stochastic simulation of the power market operation In this module, the stochastic behaviour of system components, demand growth and generation cost evolution will be simulated through proper stochastic processes. In this paper, the stochastic models are the same which have been presented in [7]. The uncertain evolution of the load demand on each geographical area is modeled as a stochastic growth rate. The zonal demand growth rate is replicated through a multivariate Brownian motion process. For the sake of simplicity, two demand periods (base and peak) are regarded, and the period durations are assumed constant throughout investment horizon. On the other hand, the uncertainty on the generation cost in thermal units is replicated by the stochastic dynamics of fuel price fluctuations which is modeled by means of a mean reversion stochastic process. Afterwards, in order to determine the operation cost for each hour of the investment horizon under the BS and the IS, an optimal power flow (OPF) model is applied. The cost difference between both scenarios defines the underlying asset which is assessed in the following layer. The DC-OPF is calculated utilizing the MATLAB-based power system simulation package Matpower 3.2 [14], modified to introduce FACTS devices in the transmission system (TS). These FACTS are implemented according to the power injection model (PIM), which results by interpreting the power injections of the converters as real and reactive node injections. Through PIM, FACTS devices can be included into power flow formulation without any alteration of the admittance and the Jacobian matrixes. The PIM method is described in detail in [15]. B. Financial assessment of TIP This layer evaluates the incremental social welfare (ISW) on the basis of the incremental cost calculated in the previous module. Firstly, a stochastic Discount Cash Flow (DCF) is performed in order to find the expected value and volatility of the underlying asset. Hence, the cash flows of the ISW originated by the execution of the proposed TIP are discounted by the WACC (Weighted Average Cost of Capital). Afterwards, taking the present value of the ISW as the underlying asset (state variable), the ROV is applied in order to quantify the value of the strategic flexibility embedded in the expansion project alternative, i.e. the postponement, abandon and relocation option. This paper utilizes the LSM approach proposed in [7] for solving the exposed flexible transmission expansion problem. The LSM method is based on Monte Carlo simulation and

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uses least squares linear regression to determine the optimal stopping time in the decision making process [5]. In order to illustrate the proposed appraisal procedure, in this paper two expansion alternatives are analyzed: a FACTS device or a Fixed Series Compensator. These investment opportunities remain open until the maturity time. Hence, the available mutually exclusive investment strategies to expand the system are: To invest in the FACTS devices; and, to invest in the Fixed Series Compensator. Once it has been already executed any alternative, it becomes available -in the next year- the option to invest a new transmission line, having the option of remaining, abandoning or relocating the FSC or FACTS investment as well. The mathematical formulation of the problem is presented by the authors in detail in [7]. The main contribution of this work is to quantifying in economic terms the dynamic power flow control of FACTS devices, by comparing the ISW value of the FACTS investment alternative with the value of a FSC investment project. In order to shed some light on the implications of this value, these ISW values are also calculated for a deterministic scenario as well as a scenario without any real option (abandon or relocation) available. V. VALUING THE DYNAMIC POWER FLOW CONTROL OF FACTS. STUDY CASE: THE ITALY-FRANCE-SWITZERLAND INTERCONNECTED SYSTEM

As was previously reported in [7], a well-known congestion path in Central Europe is the interconnection between the Italian electrical system and its neighboring countries, France and Switzerland. Italy has got to import huge quantities of electrical energy through these interconnections, because of restricted domestic generation capability. In year 2008, Italy was importing low-cost energy through international interconnectors on average 2013.5 GWh monthly from Switzerland and over one thousand GWh from France. This section exposes a case study about investing in a TCSC or FSC in the lines directly connecting France and Italy applying the valuation approach presented in [7]. The network has been set up as a 10-bus network with 5 equivalent generators, 8 aggregate loads and 13 TLs which replicates the real conditions around the aforementioned congested link. This system is illustrated in Fig. 2. The study case parameters are exposed in detail in [7]. In addition, the electrical and economic parameters of the network elements are presented in [13] and [7]. Two investment alternatives are evaluated; a static (FSC) or dynamic compensation (TCSC) connected to the TL between nodes 6 and 7 with the option to relocation between nodes 2 and 10, and a new TL between the nodes 2 and 10. Generator 1 and 4 are aggregated hydro units, generator 2 aggregated nuclear plants, Generator 3 represents aggregated lignite-based thermal units and generator 5 represents gasfired thermal units. Table II provides the generator parameters required to perform the temporal generation cost simulation.

Fig. 2. 10-bus network with three regions.

Two demand periods are regarded, base and peak with constant period length throughout the investment horizon (8 h peak and 16 h base load). Probabilistic parameters for simulating the annual growth rate are provided in Table III. In this work, the capital expenditure of the installation costs function for the TCSC is 50 000 €/MVar and for the FSC costs are define as 20 000 €/MVar according to [16]. Likewise, the scrap value of the FACTS device and its relocation cost are considered equal to the 40% of and 20% the total FACTS and FSC capital cost respectively. Under energy deficit conditions, the price is established at the Value of Lost Load, which has been assumed to be 500 €/MWh. In addition, it is considered as maturity for all investments options three years and 15 years as the investment horizon, with a discount rate equal to 12%/yr. The Monte Carlo stopping criterion is defined with a maximum relative error of 1.5% with a confidence interval of 95%. Hence, 20 000 simulations were necessary for satisfying the convergence criterion. Afterwards, it is analyzed the uncertain scenario, where the demand growth and the generation cost evolution are taken into account as uncertain variable throughout the investment horizon. The expected present value of the ISW is exposed in Table IV. TABLE II GENERATOR COST PARAMETERS [7] F

Generator

a0(0)

a1(0)

a2(0)

pF (0)

pF

G1

0

6.9

0.00067

0.2

0.21

σln p 0.0319

G2

0

24.3

0.00040

3

2.98

0.107

8000

G3

0

29.1

0.00006

5.51

6.64

0.14

3000

G4

0

6.9

0.00026

0.2

0.21

0.0319

800

G5

0

50.0

0.00150 12.46

17.94

0.129

2000

Pmax 1200

A. Results and analysis First of all, the investment strategies are evaluated under a deterministic scenario. This scenario is built from the parameters exposed in Table II and III, but without volatility. As it can be seen in Table IV, the present value of the ISW is identical for both projects, due to the fact that, under these circumstances, both devices operate at the same deterministic

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compensation level. Therefore, regarding that a FSC investment is much cheaper than a FACTS device. TABLE III. DURATION AND GROWTH PARAMETERS OF DEMAND [7] Country

j Rpeak (0)

σpeak

j Rbase (0)

σbase

France

1.83

4.99

1.68

1.86

Italy

1.07

6.84

2.011

1.33

Switzerland

1.02

5.79

1.36

1.49

TABLE IV DETERMINISTIC ISW PRESENT VALUE (M€) Strategy

PV(ISW)

FACTS

372.851

FSC

372.851

VI. CONCLUSIONS

TABLE IV STOCHASTIC ISW PRESENT VALUE (M€) Strategy

E [PV(ISW)]

FACTS

393.49

FSC

391.67

Under uncertainties, the FACTS performance is better than the FSC’s. Nevertheless, the improvement related to the dynamic operation control of the FACTS devices, which is equal to 1.82 M€, is not enough for justifying the extra expenditure of about 10.95 M€ of installing a FACTS device. For that reason, it is still better to invest in a FSC as least-cost decision. This is consistent with the traditional investment decision making in real power system, where nowadays it is still more frequent to expand the network with non-controlled devices due to the high cost of the dynamic controllers (see Table VI). However, these dynamic devices have still an uninvestigated feature, the investment flexibility. In this sense, the last scenario is discussed, where both the FACTS and FSC have strategic flexible options: the option to invest in a TL later, the option to abandon the device, and/or the option to relocate the device. Under this scenario the approach based on Least Square Monte Carlo is applied which was presented in Section II. The results of this scenario can be seen in Table VI. TABLE VI STOCHASTIC ISW PRESENT VALUE INCLUDING STRATEGIC FLEXIBLE OPTIONS (M€) Expected Expected Strategy Net Present Flexibility value Option Value Value 374.99 (2nd) 656.90 (1st) 281.91 (1st) FACTS FSC

st

384.37 (1 )

nd

630.625 (2 )

the expected option value minus the expected NPV. It can be seen –that, although the typical investment valuation approach (NPV) indicates S2 as the least-cost alternative- the optimal investment strategy, regarding the flexibility value, is to invest in FACTS. Therefore, conventional investment appraisal methods may be inappropriate when assessing transmission investments, since the presence of uncertainties dramatically increases the risk involved in large-scale irreversible decisions. Under these conditions, flexibility of an investment project for relocation or abandonment or even to build a new TL later in light of unfolding information has a significant value under a very uncertain environment, and its quantification is relevant in order to make efficient investment decisions.

nd

246.25 (2 )

It is important to remark that the expected option value represents a Net Present Value (NPV), in other words, it includes, within its value, the investment cost. Hence, it is possible to note, that under this context, the FACTS device is the least-cost decision for the grid reinforcement. In addition, it can be noticed that the investment alternative S1, investing in FACTS first, has the higher flexibility value. This fact is mainly due to the flexibility of FACTS by its dynamic control, allowing a better adaptation to likely adverse scenarios in the long-term. This flexibility value is defined by

This paper applied a fundamental model based on stochastic simulations and real options that is able to replicate the stochastic behavior of the unbundled electrical industry and processing this information in order to execute the strategic option optimally for maximizing the system wide social welfare. This model was utilized for assessing the value of the dynamic power flow control of FACTS devices, and it was capable of quantifying the influence of the uncertainties on the performance of the controller device as well as identifying and valuing the flexibility options that each investment alternative could offer for coping with the unfolding of the uncertainty. In this sense, a numerical application of the mentioned methodology was presented and relevant findings for the transmission investment problem were identified. The behaviors of the expected value of the dynamic series compensation as well as the effect of the strategic flexible options have also been analyzed. It was shown that the traditional NPV investment rule can lead to sub-optimal choices and that the assessment of flexibility in dealing with the uncertainties by executing the available real options is a key task. Thus, this paper enlightens us about the importance of the careful contemplation of these factors in order to conduct an optimal strategic option management. Finally, it has been shown, in the analyzed study case, that accounting for uncertainties as well as the flexibility, the improvement related to the dynamic operation control of the FACTS devices is far enough for justifying the extra expenditure of installing a FACTS. VII. REFERENCES [1] [2] [3]

[4]

S. Olafsson, “Making decisions under uncertainty - implications for high technology investments,” BT Technology Journal, vol. 21, 2003, pp. 170-183. T.A. Luehrman, "Strategy as a portfolio of real options," Harvard Business Review, pp. 76-99, 1998. G. Shrestha and P. Fonseka, “Flexible transmission and network reinforcements planning considering congestion alleviation,” Generation, Transmission and Distribution, IEE Proceedings-, vol. 153, 2006, pp. 591-598. B.T. Ooi, G. Joos, F.D. Galiana, D. McGillis, and R. Marceau. FACTS controllers and the deregulated electric utility environment. In Electrical and Computer Engineering. IEEE Canadian Conference on, vol. 2, pp. 597-600, 1998.

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VIII. BIOGRAPHIES Gerardo Blanco (GS’08-M’11) obtained the Electromechanical Engineer degree from the National University of Asunción, Paraguay in 2004. and the Ph.D. degree from Institute of Electrical Energy (IEE), National University of San Juan (UNSJ), Argentina in 2010. He was visiting researcher at the Institute of Power System & Power Economics, TU Dortmund, Germany. Presently, Dr. Blanco is full-time professor of the Polytechnic Faculty, National University of Asunción, Paraguay. His research interests are investment under uncertainty, and risk management. Ulf Häger (GS’08) received his diploma degree in electrical engineering in 2006 at the TU Dortmund University, Germany. Currently he is working as research associate at the Institute for Power Systems and Power Economics at the TU Dortmund University. His fields of interest are wide area power flow control and wide area protection systems as well as the application and development of FACTS devices for power flow control. Furthermore he is involved in national and international network expansion studies. Fernando Olsina obtained the Mechanical Engineering degree in 2000 from the National University of San Juan (UNSJ) and the Ph.D. degree in Electric Power Engineering in 2005 from the Institute of Electrical Energy (IEE), UNSJ, Argentina. He was Visiting Researcher at IAEW, the RWTH Aachen, Germany and at Lehrstuhl für Energiewirtschaft (EWL) at Universität Duisburg-Essen, Germany. Presently, Dr. Olsina is member of the Argentinean Research Council (CONICET). His research interests focuses on power market modeling, reliability evaluation of power systems, stochastic price simulation and investments under uncertainty. Christian Rehtanz (M’96---SM’05) received his diploma degree in electrical engineering in 1994 and his Ph.D. in 1997 at the TU Dortmund University, Germany. From 2000 he was with ABB Corporate Research, Switzerland and from 2003 Head of Technology for the global ABB business area Power Systems. From 2005 he was Director of ABB Corporate Research in China. From 2007 he is professor and head of the Institute for Power Systems and Power Economics at the TU Dortmund University. His research activities include technologies for network enhancement and congestion relief like stability assessment, wide-area monitoring, protection, and coordinated FACTS- and HVDC-control.