The development of a natural gas transportation logistics management system

Energy Policy 39 (2011) 4774–4784

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Energy Policy journal homepage: www.elsevier.com/locate/enpol

The development of a natural gas transportation logistics management system Sidney Pereira dos Santos a, Jose´ Eugenio Leal b,n, Fabrı´cio Oliveira b a b

´leo Brasileiro S.A.—PETROBRAS, Av. Almirante Barroso, 81, 12 andar, Centro, Rio de Janeiro RJ 20031-004, Brazil Petro Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Department of Industrial Engineering, R. Marques de S. Vicente 225, Gavea. Rio de Janeiro RJ 22451-900, Brazil

a r t i c l e i n f o

a b s t r a c t

Article history: Received 30 June 2010 Accepted 21 June 2011

Efficient management of the natural gas business chain – based on pipeline transmission networks and taking into consideration the interaction among the main players (e.g., shippers, suppliers, transmission companies and local distribution companies) – requires the use of decision-making support systems. These support systems maximise resources and mitigate contingencies due to gas supply shortfalls, operational contingencies from scheduled and non-scheduled equipment outages and market demand shortfalls. This study presents a practical use for technologies, such as a thermohydraulic simulation of gas flow through pipelines, a Monte Carlo simulation for compressor station availability studies, an economic risk evaluation related to potential revenue losses and contractual penalties and linear programming for the maximisation of income and the minimisation of contractual penalties. The proposed system allows the optimum availability level to be defined and maintained by the Transporter (by installing reserve capacity) to mitigate losses related to revenue and contractual penalties. It also economically identifies, quantifies and justifies the installation of stand-by compressor units that can mitigate the Transporter’s exposure to losses caused by capacity shortfalls as a consequence of scheduled and non-scheduled outages. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Natural gas industry Investment analysis Planning framework

1. Introduction The main objective of an energy distribution system is to provide efficient and reliable distribution of energy from the source to the client. The operation of a distribution system must be robust enough to deal with fluctuations in the transportation capacity, which is mainly the result of supply disruptions. The reliability of the gas supply relative to the needs of the consumer is a matter of risk evaluation and mitigation. All energy supply systems operate at a certain intrinsic level of risk, but it is important to assess whether the level and type of risks are acceptable in the operational context. Furthermore, the reliability of supply in the energy and gas sector is generally more important (e.g., for political and economic reasons) relative to other industries due to the lack of alternative options in the short term. Reliability is also related to the natural gas quality demanded by the consumers. According to Kabirian and Hemmati (2007), the risk of poor reliability is significant because an inadequate supply of natural gas to an energy consumer (such as a power plant) may shut down these facilities for relatively long periods.

n

Corresponding author. Tel./fax: 55 21 3527 1289. E-mail address: [email protected] (J. Eugenio Leal).

0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.06.047

An important characteristic of the gas supply is its complete dependence on dedicated pipeline networks. Consequently, there are generally high costs involved in interruptions to the gas supply. Complete interruptions are more likely to occur when the pipelines pass through a transit country. This issue was ¨ considered in recent works (Soderbergh et al., 2010 and Omonbude, 2007) but is not applicable to the Bolivia–Brazil pipeline. However, recent conflicts caused by the nationalisation policy of Bolivia could cause a similar type of interruption. The definition of ‘adequate’ security depends heavily on the consumers’ willingness to pay for higher reliability, which then has a direct impact on the cost of the service. Supply reliability can be improved by constructing sufficient alternatives to potential supply disruptions in the form of spare capacity, storage capacity, alternative supplies and contractual guarantees from suppliers. These options can cushion sudden disruptions in supply and reduce the impact on energy prices (Lise et al., 2008). Optimal operating methodology depends on the scale and complexity of the transmission and distribution system. Normally, the only economically feasible way to achieve an efficient and reliable energy supply is to optimise the system operation (Potocˇnik et al., 2007). Doing so makes it possible to mitigate the negative effects of equipment unavailability (due to scheduled maintenance or unscheduled outages) or failure for both the transmission and distribution systems.

S.P. dos Santos et al. / Energy Policy 39 (2011) 4774–4784

By 2000, the Brazilian energy matrix was mainly composed of 16% hydro energy, 1% nuclear energy, 46% petroleum-derived fuels, 7% coal and 5% natural gas. Sustainability of projects, environmental issues and new discoveries of gas reserves in San Alberto and San Antonio in southern Bolı´via were all important factors in the introduction of imported natural gas to the Brazilian market. In the context of the Brazilian gas industry, the national Gas and Energy Commission of the MME (Cogas/MME) decided in 1992 to increase the usage of natural gas from 2.5% to 12% of the Brazilian Energy Matrix by 2010. This decision became an institutional target of the ministry and was later (in 2000) endorsed by the National Energy Policy Council (CNPE), the body responsible for establishing policies and guidelines related to energy in Brazil (Mathias and Szklo, 2007). The government’s strategic objective – already accounted for in the strategic planning of the state-owned companies Petroleo Brasileiro S.A. (PETROBRAS) and ELETROBRAS of the petroleum and electricity sectors, respectively – led to important, concrete initiatives, such as the construction of the Bolivia–Brazil gas pipeline (Gasbol), whose commercial operation started in June, 1999 (Fernandes et al., 2008). Achieving this goal was a binational effort that involved multinational companies, such as Petrobras in Brazil and YPFB in Bolivia, and multilateral agencies of credit were established. The complete pipeline project required 1.8 billion US$ as capital investment. The pipeline project was conceived as a special purpose company to make use of the many agreements to support the feasibility of the project and to mitigate the risks associated with this kind of project. Take-or-pay, delivery-or-pay and ship-or-pay contracts with a high percentage level of firm commitment (e.g., 80% take-or-pay and 100% ship-or-pay) set the scenario for having a management system to guarantee the maximisation of resources and/or the mitigation of losses to all of the players associated with the gas chain business, even in the face of the eventual failure of any of the players. The Gasbol pipeline is the object of application of the management system that is presented in Section 6 of this work. Interstate gas pipelines may be seen as a way to approximate neighbour states and to establish co-operations that contribute to peace and stability in the region (SOVACOLL, 2009). Nevertheless, such concepts must be thoroughly developed to overcome the mistrust that often appears in response to the commercial and political conflicts that arise during the lifecycle of the project. Concerns about gas supply security are still bigger in the cases in which transnational pipelines pass through several countries. An example is the gas pipeline between Russian and Poland, which traverses Ukraine and Belarus and allows those countries the ability to interrupt the gas supply from Russian to Europe ¨ (Soderbergh et al., 2010). The issue of transit pipelines and the various ways of negotiating them is well presented by Omonbude (2007), who discusses the Russian Ukraine dispute over gas transit pipelines. Pandian (2005) presents a deep analysis of the concerns and barriers related to a multinational pipeline project between Iran, India and Pakistan. It is clear that the availability of energy sources in India is not enough to ensure the friendly environment that would make the project politically feasible. Political issues may influence the decisions of whether to implement transnational pipelines; they can also produce disturbances during the lifecycle of the project. In the case of the Gasbol project, with the exception of a political crisis in 2006 due to the nationalisation policy of the Bolivian government, no conflicts have threatened to disrupt the gas supply. This study presents a framework for defining the optimum availability level that should be maintained by the natural gas Transporter. The Transporter can maintain this availability level

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by installing redundancy equipment to mitigate losses related to revenue and contractual penalties. The framework also identifies, quantifies and economically justifies the installation of stand-by compressor units that can mitigate the Transporter’s exposure to losses due to capacity shortfalls as a consequence of scheduled and non-scheduled outages. This framework was applied in a real case study of the Gasbol gas pipeline, illustrating its practical application. Here, we consider strategic aspects of pipeline network planning. An integrated model for the strategic planning may be found in Kabirian and Hemmati (2007). The study is structured as follows. In Section 2, the framework for the Management System for Natural Gas Transportation Logistic (MSGTL) is presented. In Section 3, the objectives of the MSGTL are detailed. Section 4 describes the set of tools used in the MSGTL. Section 5 presents the MSGTL methodology. Section 6 details the study of transportation availability and Section 7 conducts the economic evaluation. In Section 8, the conclusions are presented.

2. The MSGTL framework Based on the natural gas network’s process map (in which the gas begins with the Producer and passes through the Transporter to be delivered finally by the Loader to the Distributor), one can optimise its operations using the MSGTL as it is presented in this study. A functional diagram of the system is presented in Fig. 1.

Fig. 1. Functional diagram of the MSGTL.

Fig. 2. MSGTL’s architecture.

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The MSGTL (whose architecture, as proposed by Santos (2008), appears in Fig. 2) contemplates the three situations presented below: (A) New pipeline project: uses the results of the Monte Carlo simulations to determine the availability of compression stations and the economic risks analysis. This information allows decisions to be made concerning the contractual firm capacity allocation and the transport taxes to be levied for using the gas pipeline. (B) Operational gas pipeline: uses information about compression unit failures as detected by the Supervision, Controlling and Data Acquiring System (SCADA). Those data are compared with failure frequency and transport capacity tables, which is fed into the Linear Programming (LP). The objective function of the LP is assessing the total revenue losses and contractual penalties that must be minimised. (C) Gas nomination from the Loader to the Distributor: the optimum allocation of the available gas supply takes into consideration contractual constraints, the price of gas and the Distributor’s required volumes for a given operational day. There is also an LP problem with an objective function for revenue maximisation. The components of the MSGTL used in the project phase are depicted in the right part of Fig. 1, whereas the components needed to solve the operational optimisation under the applicable constraints and the gas nomination optimisation problems are depicted in the left side of the same figure. Fig. 2 shows the corporate web business system architecture, which is based on an Oracle data server with the data model for gas pipelines APDM and the applications ArcSDE (georeferenced data manager), ArcIMS (web publisher), gas pipeline viewer, Thermohydraulic Simulator (a linear optimisation program) and the scenario builder (which is the user’s graphical interface). The mapped processes (Figs. 1 and 2) include the natural gas nomination, the transport by gas-lines, the management of the gas Distributor, the operational contingencies, the transport system maintenance scheduling and a simulation of equipment failure. The gas nomination process involves the Distributor’s solicitation of the desired volumes from the Loader. It also involves the pairing of those solicitations with the supply and the available transport capacity, which results in monthly, weekly and daily scheduled volumes for gas delivery.

The events associated with operational contingencies, scheduled maintenance and non-scheduled equipment outages of the gas transport system may reduce the capacity of the whole system and expose the Loader and the Transporter to contractual penalties due to insufficient gas delivery (i.e., lower than the previously nominated volumes). To mitigate the negative effects of equipment unavailability or failures during operation, a Monte Carlo simulation study is performed in tandem with a thermohydraulic simulation. The Monte Carlos simulations, together with the economic evaluation, indicate the possible levels of redundancy based on the capital, operational and maintenance costs versus the exposure to revenue losses due to contractual penalties. The transportation reliability (and thus, consequently, the equipment availability) becomes a key point in order to achieve the contractually firmed gas volume. Those volumes are often subject to ship-or-pay or take-or-pay contractual clauses. The ship-or-pay clause involves the Loader and the Transporter. The Loader promises to use the contracted transportation capacity for a certain period of time. He will pay for those services even if the demand falls below the contracted capacity. The Transporter commits to transporting the contracted volume of gas and will suffer penalties if he does not fulfil these obligations. The take-orpay clause involves the Loader and the Distributor. The Loader promises to offer the agreed upon natural gas volume to the Distributor at certain delivery points. The Distributor, in turn, is compelled to receive the agreed upon volume. A failure to meet these obligations exposes both the Distributor and the Loader to contractual penalties. Fig. 3 illustrates how such contractual relationships work among the involved parties in the natural gas supply chain. The situation illustrated here reveals the necessity of a methodology that can quantify the availability in the system. Additionally, the system must be able to recommend actions to increase the availability level – as well as the profitability – according to the allowable risk for each agent.

3. The objectives of the MSGTL The Management System for Natural Gas Transportation Logistics (MSGTL) was conceived to attend to the following objectives: (1) To maximise the selling of the available gas supply and fulfilling the Distributor demand.

Fig. 3. Natural gas business network.

S.P. dos Santos et al. / Energy Policy 39 (2011) 4774–4784

(2) To mitigate the network agents’ risk of exposure to potential contractual penalties that might be incurred under contingent conditions of reduced capacity due to equipment failures or gas supply shortfalls. In the case of the Transporter, the risk is not transporting the total nominated gas volume. In the case of the Loader, however, the risk is not delivering the nominated gas volume to the Distributor. (3) To quantify the availability level and the firm capacity of gas transportation using a Monte Carlo simulation (which identifies the failure frequencies of the transportation system), a thermohydraulic simulation of the gas as it travels through the pipelines (which provides an economic and technical evaluation of the optimum availability level to be determined for the transportation system) and mathematical linear programming (which manages the gas delivery costs under certain demand and supply scenarios). (4) To incorporate the core processes related to the natural gas business, involving the Producer, the Loader, the Transporter and the Distributor. (5) Under a consistent and coherent profitability perspective, to optimise the available natural gas supply allocation in a manner that better meets the needs of society and the contractual requirements between the agents. (6) To manage contingent situations of supply, transportation and marketing to mitigate negative effects, in accordance with the previously established procedures. For new natural gas pipeline projects, it is necessary to identify the transport capacity that could be compromised (on a firm contractual basis) and the corresponding transportation taxes that will pay the investments at some internal rate of return expected by the investors, considering that such equipments has nearly twenty years of a useful life. Monte Carlo simulations for equipment failures and thermohydraulic simulations are essential to identify, quantify and then mitigate the economic risks for investors who embrace an adequate redundancy level. Such redundancy can be achieved using spare equipment. Revenue losses and the mitigation of contractual penalties will be necessary if the system only manages to deliver some fraction of the accorded total volume. In order to mitigate the risk of these events, it is necessary to use Monte Carlo and thermohydraulic simulations. The real gas supply available for a specific operation day must be allocated to satisfy the contractual demands for that specific day. This fulfilment of the contractual demands must be in accordance with the criteria of constraints that assures that the optimum resources have been allocated in the face of eventual reductions in capacity or gas supply. The eventual contingent reductions in gas delivery to the Distributor must follow consistent rules, and they must be described in reports that support such decisions. The optimisation of the nomination process is achieved by a process of results maximisation over the operation of the available commercial natural gas supply. The primary objective is to optimally attend to the Distributor demand, which is usually greater than the available supply. Contractual prices and volume conditions are taken into consideration as well.

4. The set of tools The thermohydraulic simulation of the natural gas pipeline transportation network requires a model that represents the lines, equipment and accessories that influence the gas flow directly, as well as the equations and system conditions of delivery, supply

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and operations. It also requires specialised software that is capable of simulating thermohydraulic flow in a permanent (static) or transient (dynamic) regime. Simulation technology of this kind has developed enormously over the last few decades. It allows for the simulation of gas-line network behaviour, under both permanent and transient regime. According to Santos (1997), this technology is absolutely necessary for the development of gas-line projects. Without the use of transient simulation during the gas-line project phase, the investor risks creating an oversized system capacity, which can affect its viability. There is also a risk of undersizing the system’s transportation capacity, which can require reinvestment to cover gas failure issues caused by the undercapacity of the system to cope with the demand patterns of the market. In the case of undersizing, the project value will deteriorate, reducing the internal rate of return for the entire project. Using statistical distribution curves as a method to evaluate processes and projects containing many variables and volatile behaviour has become increasingly popular in the last few decades. According to Evans and Olson (1998), the Monte Carlo simulation is essentially a sampling experiment that aims to estimate the distribution of a result variable that depends on other input variables, each one with its own probabilistic distribution curve. The Monte Carlo simulation is often used to assess the impact of strategic changes and the risks behind such decision making. The risk is usually defined as the probability that an undesired result occurs. The Monte Carlo simulation has been used for gas pipeline analysis in other contexts. Monforti and Szikszai (2010) used a Monte Carlo based model in a macro-analysis of the EU gas network. In our case, Monte Carlo is used in a microanalysis of the gas pipeline operation. According to Ragsdale (2006), deciding how to best use the limited resources of an individual or company is a universal problem. In the current competitive business environment, it is critical to guarantee that an enterprise uses its limited resources as efficiently as possible. When applied to the gas distribution problem, linear programming considers two objective functions: first, to maximise the profits from natural gas commercialisation and, second, to minimise losses (via lost income and contractual penalties). Consequently, two models were developed to attempt to maximise profits and minimise losses, respectively. Each model has its own particular set of constraints and an objective function. Both models are subject to gas supply and gas-line transportation capacity and minimum and maximum delivery point capacity constraints. When the demanding point is a thermoelectric facility, there is also a minimum and maximum gas supply constraint required to allow the facility to operate.

4.1. Maximising gas commercialisation To maximise income, the maximisation objective function considers the commercialisation of a certain available gas supply subject to the transportation capacity declared by the Transporter. It also considers the volumes required by the Distributor and/or the large consumers (such as thermoelectric facilities) in addition to the individual commercial price that would be charged once there was a difference between the final prices (commodityþtransportation) for each type of consumer (industrial and thermoelectric facility). Other parameters that must be taken into account include the transfer costs incurred when the gas is used for the internal consumption of refineries, industrial units and thermoelectric plants that belong to PETROBRAS.

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The linear program is formulated as n X

max f ðzÞ ¼

ni pi

r1,n1,q1,p1

r2,n2,q2,p2

i=1

i=2

Delivery points

i¼1

rn,nn,qn,pn i=n

Subject to n X

ni rTS

ð1Þ

ni rCAP

ð2Þ

Cp1,S1

S

Cp2,S2

i¼1 N X

Cpn,Sn

Transportation network

ri,ni Detail of a delivery point

i¼1

ni rri Yi ,

8i ¼ 1, :::, n

ð3Þ

ni Zqmini Y i

8i ¼ 1, :::, n

ð4Þ

pi ,Cpi ,Si Z0,

8i ¼ 1, :::, n

ð5Þ

Si ¼ ni þ Si þ 1 , Si rCpi , Cpi r 30,

8i ¼ 1, :::, n1

8i ¼ 1, :::, n 8i ¼ 1, :::, n

qmaxi ni Z0,

8i ¼ 1, :::, n

8i ¼ 1, :::, n

Yi A f0,1g,

qmin, qmax

ð6Þ ð7Þ ð8Þ ð9Þ ð10Þ

where Parameters

Cpi, Si

Si+1

Fig. 4. Illustration of the pipeline process.

that point and all the supply located downstream that same point (constraint 6); the gas supply at each delivery point cannot exceed the transportation capacity to that delivery point (constraint 7); the transportation capacity to each delivery point is limited to 30 MMm3/day (constraint 8); the nominated gas volume for each delivery point cannot exceed the Maximum gas volume nominated to the Distributor at delivery point i (constraint 9) and the binary variable Yi indicates the existence (¼1) or not (¼0) of flow to delivery point i (constraint 10). 4.2. Minimising losses (via lost income and contractual penalties)

TS CAP Cpi ri qmini qmaxi pi qi

Total gas supply main pipeline transportation capacity delivery point i transportation capacity gas volume required by the Distributor at delivery point i minimum gas volume nominated to the Distributor at delivery point i maximum gas volume nominated to the Distributor at delivery point i gas price at delivery point i delivery point i capacity

Auxiliary variables Si Yi

amount of gas supplied at delivery point i 1, if there is flow for the delivery point i; 0 otherwise

The minimisation objective function mitigates income losses and the need to pay contractual penalties due to the incapacity to cope with the total nominated volume accorded for a specific day as the result of operational contingencies that reduce the gas transportation capacity of the Transporter or due to gas supply reductions generated by the Producer. Operational contingencies that can reduce the transportation capacity include the failure of compression units, the failure of gas delivery points, or an improper closing of a gas-line block valve. The normal closure of block valves (which takes about 0.001% of the total availability of the line) do not significantly impact the transportation capacity and were, therefore, not considered. The same approach was adopted for failures at the gas delivery points. min f ðzÞ ¼

n X

ðni ei Þðpi mi Þ

i¼1

Decision variable ni

nominated volume of gas to the Distributor at delivery point i

For industrial and thermoelectric usage and for PETROBRAS internal usage, the maximisation objective function aims to maximise the sum of the product of the nominated gas volume delivered to the Distributor with the final gas price at each point of delivery. The applicable constraints require the following conditions: the total nominated volume of gas delivered to each Distributor can exceed neither the total gas supply offered by the Producer (constraint 1) nor the transportation capacity declared by the Transporter (constraint 2); the nominated gas volume at each point cannot exceed the Distributor’s required volume (constraint 3) and cannot be less than the minimum operational volume for that specific point (constraint 4) (Fig. 4); the non-negativity of some of the variables are defined in constraint 5; the gas supply before each delivery point must be sufficient to meet the nominated volume for

Subject to n X

ei rTS

ð11Þ

ei rCAP

ð12Þ

i¼1 n X i¼1

ei rni Yi ,

8i ¼ 1, :::, n

ei Zqmini Y i ei ,Si Z 0,

8i ¼ 1, :::, n

8i ¼ 1, :::, n

Si ¼ ei þ Si þ 1 , Si r Cpi ,

8i ¼ 1, :::, n

0 rCpi r30, Yi A f0,1g,

8i ¼ 1, :::, n1

8i ¼ 1, :::, n

8i ¼ 1, :::, n

ð13Þ ð14Þ ð15Þ ð16Þ ð17Þ ð18Þ ð19Þ

S.P. dos Santos et al. / Energy Policy 39 (2011) 4774–4784

e1,n1,q1,p1

e2,n2,q2,p2

i=1

i=2

Delivery points

S

Cp1,S1

Cp2,S2

en,nn,qn,pn i=n Cpn,Sn

4.

Transportation network

n i , ei Detail of a delivery point Cpi, Si

qmin, qmax 5.

Si+1

Fig. 5. Illustration of the pipeline process.

where Decision variable ei

Gas volume to be delivered to the distributor at delivery point i

The objective function consists of the sum of the product of the difference between the nominated volumes and the real volumes delivered to the Distributor on a specific operational day, with the prices and contractual penalties defined by the solver for the partial fulfilment of the Distributor demand. The applicable constraints 11–19 have essentially the same meaning as the constraints 1–8 and constraint 10, except with the decision variable ei (gas volume to be delivered to the Distributor at delivery point i) replacing the decision variable ni (nominated volume of gas to the Distributor at delivery point i) (Fig. 5).

6.

4.3. Geographical information system (GIS) ArcGIS from ESRI, a world leader in the Geographic Information System (GIS) technology, was chosen to develop the system discussed here. The system is based on georeferenced databases and is used by large petroleum, gas and energy companies around the globe. The Arc Pipeline Data Model (APDM) (used for the development of the system described in this paper) is the relational georeferenced data model and also the intellectual property of ESRI. The APDM incorporates activities such as oil transportation and distribution as well as the transportation and distribution of oil derivatives, general liquids and natural gas.

7.

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redundancy level scenarios. The thermohydraulic simulator used here was the PipelineStudio, which can simulate both transient and permanent regimes. The results obtained from this simulation identified the total available transportation capacity and the frequencies of failures for each failure scenario. System availability study: compares the equipment failure frequency results (activity 2) and the gas-line network’s transportation capacity (obtained from activity 3) to identify the investment required for a certain redundancy level in a certain scenario. This information is essential to define the most appropriate redundancy level that suits the total amount available for investment. Economic viability study: gathers all the information received from the previous phases (activities 1–4) in a structured analysis. It uses a Microsoft Excel spread sheet and the risk simulation software @Risk 4.5 to explore economic premises, such as the internal rate of return, the project’s economic life, the exposure to revenue loss, and the contractual penalties due to incomplete gas deliveries. It also uses a Monte Carlo simulation, which incorporates the statistical distribution of the transportation capacities (activity 3) due to contingent equipment failure (activity 2). The economic viability study is the pillar on which the decision will be based in terms of the optimum level of equipment redundancy. Mathematical linear programming: consists of the mathematical system model that can be solved through optimisation techniques. The system of equations is led by an objective function and a certain optimisation direction. The minimisation function defines the volume of gas that will actually be delivered to each delivery demand point on a certain operation day. Those volumes are chosen to minimise the effects of an unexpected reduction in the gas supply or the constraints of transportation capacity. The program defines which points will be fully covered and which will be partially fulfilled or even cut from the operational plan, according to previously determined criteria. The maximisation function allows the optimisation of the available gas supply reallocation from the Loader to the Distributor. It takes into consideration the contractual constraints, gas prices and the required volumes from the Distributor for a specific operation day. The system was modelled using Microsoft Excel spreadsheets, and its optimisation was accomplished using the Solver tool. Scenario builder application: consists of the integration module of all the different technologies that compose the MSGTL system (APDM, ArcGIS, PipelineStudio, linear mathematical programming and the economic evaluation spread sheet). Using this application, one can directly access the GIS and APDM systems to execute the scenarios on the PipelineStudio.

5. Methodology The methodology adopted for the MSGTL conception considered the following activities: 1. The Gasbol gas pipeline network modelling: the network is composed of lines, equipment, accessories, terrain elevation profiles, gas flow equations, ambient and soil temperatures and other relevant characteristics. It constitutes the basis for the execution of the thermohydraulic simulation. 2. Monte Carlo simulation for equipment failure: it consists of the statistical model, which considers the main components of the gas-line network, each with its own failure rates. The system was created using Microsoft Excel sheets and the risk simulation software @Risk 4.5. 3. Thermohydraulic simulation: evaluates the operational behaviour of the pipeline network under different failures and

6. Transportation system availability study The Bolivia–Brazil gas pipeline (Gasbol) consists of 557 km of pipelines from Rio Grande (Bolivia) to the border between Bolivia and Brazil, 1264 km from the border to Campinas (Sa~ o Paulo, Brazil), 1190 km from Campinas to Canoas (Porto Alegre,Brazil) and a 153 km connection segment from Campinas to Guararema (Sa~ o Paulo, Brazil). The length of the complete line is 3164 km. The current gas transportation capacity of the pipeline reaches 30 MMm3/day (millions of cubic metres per day). The Gasbol project was implemented by the transportation companies Gas Transboliviano S.A. (GTB) and Transportadora Brasileira Gasoduto Bolı´via Brasil (TBG). In order to map the availability and the redundancy investments for each company involved in the transportation

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infrastructure, one has to consider that the business configuration of the Gasbol project involves various companies. The simulations were driven by the following parameters: Software: Number of iterations: Sampling type: Random generator seed: Standard recalculation: Distributions considered:

Microsoft Excel, @RISK 4.5 5000 Latin hypercube Fixed and equal to 1 Expected value All

The 5000 iterations for each scenario were converted to histograms for each type of failure frequency. These frequencies were obtained from the Monte Carlo simulation process that had been previously executed. Following the identification of the failures and their particular frequencies, it was possible to start the thermohydraulic simulations. These simulations were carried out by considering each type of failure and its impact on the transportation capacity of the pipeline network. Finally, with the data extracted from the previous steps, the economic evaluation for the adequate redundancy level could be prepared. For Gasbol’s gas-line transportation network reliability assessment, the methodology presented by Santos et al. (2006) was applied to ten Gasbol compression stations (Brazilian side), for which the authors present a comparative statistical evaluation based on the binomial discrete probability distribution associated with the Monte Carlo simulations. Because the binomial evaluation required that every single piece of equipment has the same availability (i.e., probability of failure), it was necessary to consider some simplifications for the system. In Santos et al. (2006), it was assumed that the compressing units were identical and that each station had the same number of units. The Monte Carlos simulation is not subject to such a constraint and can be applied to any configuration profile, including profiles with differing numbers of units and different availabilities associated with then. The authors highlight the applicability of the Monte Carlos simulation method due to its simplicity and greater flexibility. 6.1. Availability of compression units Data from the Electric Power Research Institute, Epri (1999) and the North American Electric Reliability Council—NERC (2005)

3 x 7000 1 x 15000 hp 2 x 19500 hp

#1

#2

compression stations were considered. Both sources are internationally respected and their work is strongly based on statistical data obtained from the compression stations (EPRI) and thermoelectrical generation (NERC) operations. For this study, the availability value of 0.9294 (92.94%) was adopted from NERC’s methodology instead of the 0.971 (97.10%) found in the EPRI study. According to the operations team of the transportation company Transportadora Brasileira Gasoduto Bolı´via Brasil (TBG), NERC’s value was slightly closer to the last eight years of Gasbol operations history than ERPI’s value. Additionally, the NERC value expects more failures between the operational time horizons than the ERPI value, reflecting a more conservative approach. As shown in Figs. 6 and 7, a simplified approach to arranging the compression stations was maintained. In these figures, it is possible to compare how the compression units are arranged in reality to how they are arranged using the simplified approach. Compression units 1 through 4 are on the Bolivian side, and units 5 through 14 are on the Brazilian side. 6.2. Studied configurations The results of the study of stations on the Brazilian side are presented below. Three alternatives to the configuration of the spare compression units were considered: 1. Configuration with no spare compression units 2. Configuration with 5 spare compression units 3. Configuration with 10 spare compression units. Tables 2–5 present the thermohydraulic and Monte Carlo simulation results. The availability level for each configuration is shown in Table 1. The average availability for each case, considering the installed redundancy level (i.e., the number of spare compression units) is shown in Table 2 (0 spare units), Table 3 (5 spare units), Table 4 (10 spare units) and Table 5 (summary of all of the configurations). In Tables 2–4, column 1 indicates the average capacity resulting from a failure in the station denoted in column 2. Column 3 indicates the average number of occurrences of one failure in one unit in days per year. Column 4 indicates the average number of occurrences of two failures in one station in days per year. Column 5 indicates the average capacity corresponding to failures in two adjacent stations, which are indicated

2 x 15000 hp

2 x 15000 hp

#3

#4

#5

4 x 7000 hp

4 x 7000 hp

#6

#7

#8

#9

#10

2 x 15000 hp

#11

#12

#13

#14

#11

#12

#13

#14

Fig. 6. Gasbol’s compression stations, as installed.

2 x 15000 hp

#1

#2

#3

#4

#5

#6

#7

#8

#9

#10

Fig. 7. Simplified array of Gasbol’s compression stations.

S.P. dos Santos et al. / Energy Policy 39 (2011) 4774–4784

in column 6. The number of failures in days in one year for each combination of stations is presented in column 7. Table 5 summarises, for each spare unit configuration and for each failure condition (0, 1, 2, or (1þ1) adjacent), the total number of occurrences in days per year. This table also presents the average capacity and availability of each spare configuration. The average available capacity can be obtained by calculating the sum for each product of the ‘capacity by failure occurrence’ and dividing it by the total number of days in a year. Only

compression unit failures were considered in the equation; it does not take other factors into account. P Capacityu Failure Occurenceu Average Capacity ¼ u A U ð20Þ 365 U represents the set of compression units. The system’s availability can be obtained by dividing the average capacity by the gas-line firm contracted capacity. Systems Availability ¼

Table 1 Availability for each configuration. Configuration

Availability

No spare compression unit (1) With 5 spare compression units (2) With 10 spare compression units (3)

0.9056 0.9573 0.9956

4781

Average Capacity Contracted Capacity

ð21Þ

Fig. 8, which shows a graph representing the capacity curve versus the percentage of occurrence frequency, indicates the risk exposure incurred by failing to fulfil the contracted firm capacity of 30.08 MMm3/day for each configuration of spare compression units. Fig. 8 clearly indicates that the more spare units installed, the lesser the probability that the system will operate below the firm-contracted capacity. The bars in Fig. 8 represent the

Table 2 Monte Carlo and thermohydraulic simulations results—No spare compressor units.

3

1 Failure/station

2 Failures/station

(1þ 1) Adjacent failures

Capacity MMm /day

Station

Failure occurrence

Failure occurrence

Capacity MMm3/day

Stations

Failure occurrence

25.90 26.10 26.70 26.70 27.20 27.50 28.70 29.80 29.30 25.80

#5 #6 #7 #8 #9 #10 #11 #12 #13 #14

36.07 31.57 29.07 24.50 19.21 19.14 17.57 14.21 12.29 11.21

1.79 1.86 1.79 1.71 1.79 1.71 1.71 1.64 1.71 1.86

21.60 21.70 21.90 22.00 23.00 23.70 24.40 24.90 21.90

5&6 6&7 7&8 8&9 9&10 10&11 11&12 12&13 13&14

5.93 5.07 4.93 5.43 5.86 4.29 5.21 4.14 5.29

Table 3 Monte Carlo and thermohydraulic simulation results—5 spare compressor units. 1 Failure/station

2 Failures/station

(1þ 1) Adjacent failures

Capacity MMm3/day

Station

Failure occurrence

Failure occurrence

Capacity MMm3/day

Stations

Failure occurrence

25.90 26.10 26.70 26.70 27.20 27.50 28.70 29.80 29.30 25.80

#5 #6 #7 #8 #9 #10 #11 #12 #13 #14

1.7 1.6 1.5 1.4 1.4 39.3 33.9 30.0 25.3 24.9

0 0 0 0 0 1.8 1.9 1.8 1.8 1.9

21.60 21.70 21.90 22.00 23.00 23.70 24.40 24.90 21.90

5&6 6&7 7&8 8&9 9&10 10&11 11&12 12&13 13&14

0 0 0.1 0 0.3 5.5 5.7 5.0 5.6

Table 4 Monte Carlo and thermohydraulic simulation results—10 spare compressor units. 1 Failure/station

2 Failures/station

(1þ 1) Adjacent failures

Capacity MMm3/day

Station

Failure occurrence

Failure occurrence

Capacity MMm3/day

Stations

Failure occurrence

25.90 26.10 26.70 26.70 27.20 27.50 28.70 29.80 29.30 25.80

#5 #6 #7 #8 #9 #10 #11 #12 #13 #14

1.8 1.9 1.7 1.7 1.8 1.7 1.7 1.6 1.7 1.9

0 0 0 0 0 0 0 0 0 0

21.60 21.70 21.90 22.00 23.00 23.70 24.40 24.90 21.90

5&6 6&7 7&8 8&9 9&10 10&11 11&12 12&13 13&14

0 0 0.1 0 0 0 0 0 0

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Table 5 Monte Carlo and thermodynamic simulation results for each configuration. Without spare stations

With 5 spare stations

With 10 spare stations

Failure (units by station)

Occurrences in one year

Occurrences in one year

Occurrences in one year

0 1 2 (1 þ1) Adjacent stations Average capacity Firm contractual capacity Availability

86.43 214.86 17.57 46.14

172.93 160.79 9.07 22.21

347.43 17.5 0 0.1

27.2403 30.08

28.7957 30.08

0.9055

0.9673

30 30.08 0.9956

Fig. 8. Capacity versus percent frequency versus # of spare compression units.

frequencies of occurrence of each value of capacity resulting from the spare configurations of compressor equipment. Without any spare units, the maximum capacity of 30 MMm3/d is reached at a frequency below 60%, whereas values of 26.87–27.79 MMm3/d are observed with at a frequency below 10% each. With 5 spare units, the maximum capacity is reached in almost 80% of the cases, and values ranging from 27.05–27.79 MMm3/d are also observed. With 10 spare units, the maximum capacity is reached in almost 100% of the cases. As summarised in Table 5, without any spare units, the availability of the compression system is 0.9055; with 5 spare units, the availability rises to 0.9673 and the availability reaches a maximum of 0.9956 with 10 spare units at each compression station.

7. Economic evaluation According to Santos (2003), economic evaluation for the purpose of decision making has been a subject of major attention, both in the academic community and in the corporate world. When it comes to evaluating projects, one of the most powerful approaches has been the use of the Discounted Cash Flow (DCF) method with the adoption of Net Present Value (NPV) associated with the expected Internal Rate of Return (IRR). Several authors, such as Ross et al., (2004) and Copeland et al., (2000), have strongly recommended this approach, and it has been widely disseminated in the corporate environment where it has gained many adepts and defenders. However, the use of such methodology in isolation is not enough to support efficient decision making, even if the methodology is associated with the analysis of ‘what if scenarios’. For example, this approach does not constitute a quantitative risk analysis and assigns the same probabilistic weight for each scenario, including scenarios in

which all the variables assume their maximum/minimum values (Vose, 1996). According to Hertz (1984), a project is always vulnerable to the uncertainties created by the volatile aspects of production (such as the market conditions, human resources and deadlines) that can be associated with high levels of stochasticity. Such uncertainties include the material and service costs, deadlines, the requirement of environmental licences, workforce management and other similar aspects whose occurrence are strongly associated with probabilities and correlations among themselves. These probabilities and, in particular, these correlations must be known to clearly identify the risks involved in the project so that one can properly mitigate, or even eliminate, those risks. Hertz (1984) warns that combined uncertainties within a project can generate global risks of critical proportions to that project. Risk analysis in the context of the gas industry has also been studied by Pelletier and Wortmann (2009), who used the probability of negative NPV for the investment as the measure of risk. The Monte Carlo simulation has been used to probabilistically evaluate the risk involved in projects that use the DCF method. Thus, an investor can measure the risk level associated with his project and then identify actions to mitigate that risk (and/or discount taxes that can absorb it), reducing or even eliminating his risk exposure. As a consequence, if the uncertainties are incorrectly accounted for during the project evaluation, it can generate adverse future results in terms of negative NPV. This situation could turn into an economic disaster for the corporation, compromising the image of the company among both its shareholders and society. The Monte Carlo simulations, together with the thermohydraulic simulations conducted for Gasbol’s pipeline network, had the basic goal of determining the global system’s transportation availability in terms of capacity. The basic purpose was to define the firm capacity available to define the ship-or-pay contracts between the Transporter and the Loader. The firm transportation capacity must be considered when the gas-line transportation tax is calculated, chiefly because the company must pay back the whole of the capital investment and cover the operational costs throughout the gas pipeline’s economic life. Such a procedure had not been adopted previously for the gas-line network project, thus exposing the Transporter to economic risks from contractual penalties due to the failure to fulfil its contractual firm volumes. The Transporter also exposes himself to the risks associated with the need for new investments in spare equipment installation, which can lead to the underestimation of the transportation tax and, thus, reduce the internal rate of return of the project. Because the costs of spare equipment were not originally considered in Gasbol’s capital costs, it was necessary to begin an economic evaluation to define the total number of spare compression units that must be installed. The required number of spare compression units was determined using both Monte Carlo simulations for failure and thermohydraulic simulations. Because the Gasbol project was constituted by the transportation companies Gas Transboliviano S.A. (GTB) and Transportadora Brasileira Gasoduto Bolı´via Brasil (TBG), economic studies evaluating spare equipment installation were conducted separately for the Brazilian and Bolivian sections. The main objective of the economic evaluation was to identify the most adequate redundancy level to manage the revenue losses and the risk of exposure to contractual penalties. The discounted cash flow methodology was adopted to compare the results for three different conditions. The aim here was to identity which condition had the highest present liquid value. The volumes delivered and the penalties avoided were considered as income and the installation costs of the spare compression units

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Table 6 Comparative summary of the spare compression units’ installation. Name Without spare compression unit Transportation capacity (MMm3/d) Loss of capacity (MMm3/d) Loss of revenue (MMUS$) Contractual penalty (MMUS$) PV of losses (MMUS$) NPV (MMUS$) With 5 spare compression units Transportation capacity (MMm3/d) Loss of capacity (MMm3/d) Loss of revenue (MMUS$) Contractual penalty (MMUS$) PV of losses (MMUS$) Capacity recovery (MMm3/d) Avoided revenue (MMUS$) Avoided contractual penalty (MMUS$) PV of revenues (MMUS$) NPV (MMUS$) With 10 spare compression units Transportation capacity (MMm3/d) Loss of capacity (MMm3/d) Loss of revenue (MMUS$) Contractual penalty (MMUS$) PV of losses (MMUS$) Capacity recovery (MMm3/d) Avoided revenue (MMUS$) Avoided contractual penalty (MMUS$) PV of revenues (MMUS$) NPV (MMUS$)

Min.

Average

Max.

x1

p1 (%)

x2

p2 (%)

x2–x1

p2–p1 (%)

21.60  8.4800  483.21  483.21  966.41  966.41

27.24  2.8397  227.71  227.71  455.43  455.43

30.08 –  10.19  10.19  20.38  20.38

21.90  8.1800  338.32  338.32  676.64  676.64

5 5 5 5 5

30.08 –  125.43  125.43  250.86  250.86

95 95 95 95 95 95

8.18 8.1800 212.89 212.89 425.78 425.78

90 90 90 90 90 90

21.9  8.1800  333.58  333.58  667.16  8.1800  199.87  199.87  399.74  464.24

28.80  1.2843  103.00  103.00  205.99 1.5554 124.72 124.72 249.43 184.93

30.08 – – – – 8.4800 401.59 401.59 803.17 738.67

24.9  5.1800  196.95  196.95  393.90  3.9800  14.48  14.48  28.95  93.43

5 5 5 5 5 5 5 5 5 5

30.08 –  28.74  28.74  57.48 7.0800 261.29 261.29 522.59 458.19

95 95 95 95 95 95 95 95 95 95

5.18 5.1800 168.21 168.21 336.42 11.0600 275.77 275.77 551.54 551.62

90 90 90 90 90 90 90 90 90 90

21.9  8.1800  212.48  212.48  596.84  4.2800  47.34  47.34  94.69  223.69

29.95  0.1334  10.70  10.70  21.41 2.7062 217.01 217.01 434.02 305.02

30.08 – – – – 8.4800 483.21 483.21 966.41 837.41

30.08 –  51.35  51.35  95.28 – 109.68 109.68 219.35 90.35

5 5 5 5 5 5 5 5 5 5

30.08 – – – – 8.1800 329.57 329.57 659.13 530.13

95 95 95 95 95 95 95 95 95 95

0 – 51.35 51.35 95.28 8.1800 219.89 219.89 439.78 439.78

90 90 90 90 90 90 90 90 90 90

were considered as investments in each condition. There were no additional costs related to the spare units’ fuel and maintenance consumption because the operational time of these units is limited. The present value of cash flows of contractual penalties and potential loss of revenue utilises a discount rate of 15% per annum (pa), which is a conservative value. A rate normally used for infrastructure projects is approximately 12%. When we consider the project’s financial leverage (e.g., 30% equityþ70% debt) and depending on the funding costs in the financial market, the discount rate on equity of the project sponsors may rise significantly. The use of discount rates for the project of less than 15% makes the results even more favourable to the adoption of the proposed solution.

values associated with the probability of 90% occurrence. The results of the economic evaluation (based on the previous premises) identified alternative number 3 as the most viable (i.e., the installation of ten spare compression units) according to the NPV obtained, which is the highest among the alternatives (i.e., not to install any redundancy or to install five spare units). The NPV for the configuration without spare units is equal to the PV of losses once this alternative implies no extra investment. Alternative 3, as shown in Table 6, is the only alternative that does not present a capacity loss (on average) while showing lower revenue losses and contractual penalties. The combination of these aspects leads alternative 3 to a more profitable NPV. The NPV for alternative 3 is MMUS$ 305.02, as indicated in Table 6.

8. Conclusions 7.1. Economical premises Firm contractual capacity: 30.08 MMm3/d Superior calorific power: 36,480 BTU/m3 Transportation tax: 1.20 US$/MMBTU Revenue loses: 1  non-delivered capacity Contractual penalties: 1  non-delivered capacity Contractual duration: 10 years Discount rate: 15% yearly Spare unit installation cost: US$ 12,900,000.00

7.2. Results from the economic evaluation The results of the economic evaluation are summarised in Table 6. Columns Min, Average and Max indicate the values of the decision variables described in column Name for each configuration of spare compression units. The values in column x1 are the values for each variable associated with the probability of 5% in column p1. The same occurs with column x2 for the probability of 95% in column p2. In column x2 x1, the differences between columns x2 and x1 are the

The implementation and use of the MSGTL are fundamentally important for the management of a natural gas business due to the complexity of networks and the contractual relationship among the agents. Such contractual relationships can be complicated by the many penalty clauses resulting from the failure to fulfil obligations related to supply, transportation and the receiving and delivery of natural gas. Using the methodology presented in this study to assess the transportation system’s optimum reliability level is fundamentally important for the safekeeping of the transportation process’s financial equilibrium under a ship-or-pay contractual structure that penalises the failure to fulfil firm contracted volumes. In the case of Gasbol, an investment in spare compression units was shown to be economically viable and beneficial to the Transporter, as indicated by the NPV for the Brazilian section of the gas pipeline. The adoption of the Monte Carlo method for risk simulation, together with the Discounted Cash Flow (DCF) method, provides a statistical understanding of the exposure to revenue loss, contractual penalties and the Net Present Value (NPV) of the alternatives. Such an understanding allows the decision maker to identify the expected value trust intervals. This

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methodology also provides the decision maker with better insight into the risks associated with the investment alternatives, thus supporting him with a quality statistical basis for decision making. These findings highlight the need to incorporate a statistical approach into the development of the new natural gas-line project. The thermohydraulic simulations under permanent and transient regimes, together with the Monte Carlo simulation, allow us to assess the availability of the gas pipeline network’s components and to measure the effects of failure on the network’s gas transportation capacity. The proposed management system aims at the improvement of energy security and may be applied to other gas pipelines in Brazil, such as the recently opened pipeline Urucu–Manaus. In this region, the supply of gas is a critical issue due to the dependency of the energy matrix of the region on fossil fuels for the generation of electricity (Frota et al., 2010).

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