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Pág. 131

ANEXO J – Simulaciones Dinámicas

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Pág. 132

ANEXO K – Metodología de Análisis de Riesgo

1

Strategic Assessment of Supply Options in Power Systems with Significant Resource Uncertainty

Carlos A. Dortolina, Ramón Nadira, Hans Fendt,

Nelson Bacalao, and Jacobo Di Bella

Abstract:1 This paper presents a decision analysis approach for the assessment of options available to power systems with significant supply uncertainty. The approach considers the cost, effectiveness, and benefits of all appropriate, available, and feasible options. These include both, supply-side and demand-side options. The paper also provides details about the implementation of the approach in terms of a computer model developed specifically for each particular case. In addition, we illustrate the use of hedging strategies for the mitigation of the exposure to risk, and present the results of the application of the proposed approach in two developing countries: Malawi and Venezuela.

I. INTRODUCTION

n many developing countries around the world, the growth of electricity demand is outpacing the expansion

of generation resources , thus increasing the likelihood of supply shortages . In the case of large industrial customers, which depend rather heavily on a reliable supply of electricity, this represents a serious threat. To make matters worse, many of these developing countries have a large component of hydroelectric generation, and thus their energy production is often uncertain. In this paper, we present an approach for assessing the options available to power systems with significant supply uncertainty. The approach considers the cost, effectiveness, and benefits of all appropriate, available, and feasible options. These include both, supply-side and demand-side options. The results yielded by the approach can effectively be used to provide input to the development of strategic energy requirement plans in the short, medium, and long run. We also illustrate the use of hedging strategies for the mitigation of the exposure to risk, and present the results of the application of the proposed approach in two developing countries: Malawi and Venezuela.

C. A. Dortolina is with Stone & Webster Consultants Inc., Houston, TX 77077, USA (email: [email protected]) R. Nadira is with Stone & Webster Consultants Inc., Houston, TX 77077, USA (email: [email protected]) H. Fendt is with Empresas Polar, Caracas, Venezuela (email: [email protected]) N. Bacalao is with Stone & Webster Consultants Inc., Houston, TX 77077, USA (email: [email protected]) J. Di Bella is with Empresas Polar, Caracas, Venezuela (email: [email protected] )

II. METHODOLOGY

We applied the methodology known as trade-off risk (or “TOR”) [1, 2, 3], that has been proven to be very effective in the context of planning in the presence of uncertainties. Key to the successful application of TOR is the correct definition of options, uncertainties , and attributes (see Figure 1). Options are alternatives that the planner can control (i.e., actions or decisions that can be taken, such as building inside-the-fence generation). Sets of specific options are called Plans . Uncertainties, on the other hand, are variables over which the planner has no control. Scenarios combine specific options with given materia lizations of the uncertainties.

Options

Uncertainties

AttributesScenariosScenarios

DecisionAnalysisDecisionAnalysis

Figure 1. Planning Methodology. Uncertainties can be modeled either by means of unknown-but-bounded or probabilistic representations. Unknown-but-bounded representations account for the limits on the modeled uncertainties, with no assumptions about their underlying probability distributions. That is, uncertainties are modeled as ranging between known maximum and minimum expected values. However, if the probabilistic distributions are known, as is the case for example of water flows into a reservoir, then they can be modeled using other techniques , such as Monte Carlo simulations. Attributes are measures of the quality of options or plans, i.e., costs or benefits. The evaluation of alternatives in the planning process involves postulating credible and relevant scenarios (i.e., assuming that uncertainties materialize at a given level), and testing how such alternatives would perform in that context. This yields tradeoffs in the attribute space. An example of this is illustrated in Figure 2, that shows the trade-off between two attributes: supply costs [in US$/MWh] and the cost of unavailability [in US$/year]. Each point in this figure corresponds to a particular scenario. The figure also illustrates the process whereby a decision set

I

2

can be arrived at (from the decision analysis shown in Figure 1). A decision set consists of those scenarios that are not completely dominated by others. In Figure 2, the scenarios that belong to the decision set are connected by a line. It is obvious that there are no other scenarios that would be better than those in the decision set with respect to the attributes shown in the figure. Hence, all scenarios that do not belong to decision set may be dropped from future consideration. A hedge is an action (or set of actions) that could be taken to mitigate or reduce the risk associated with a given decision. Hedges seek to add robustness to a plan, usually at the expense of some level of sub-optimality (e.g., hedges generally command a premium). The approach yields a decision set, but ultimately the decision-maker has to make a decision. We assume that decision makers act rationally, in the sense that they compare the costs and benefits of a given option/plan and then decide to engage in that option/plan if it maximizes their perceived return relative to cost and the resulting exposure to risk is acceptable. This is the foundation of many theories in psychology relating to human behavior.

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In our case, we selected a Minimum Regret method as the proxy tool used to arrive at a decision. This method, also known as the MaxiMin (or MiniMax) method, seeks to maximize the benefits derived from a given decision, while minimizing the potential adverse consequences of such a decision. Below we present the results of the application of the proposed methodology in two developing countries: Malawi and Venezuela. The Malawi case involved developing a country-wide integrated resource plan, while the case in Venezuela was for the strategic evaluation of options available to meet the short, medium, and long term energy requirements of Empresas Polar, a large industrial end-user that operates in that country.

III. OPTIONS AND PLANS

The particular conditions in these developing countries (i.e., Malawi and Venezuela) are quite unique and no one-size-fits-all options are truly applicable. This section summarizes the nature and issues affecting electric generation in these countries, followed by a discussion on how these issues affect the supply-side initiatives. In the case of Malawi (see Table 1), all of the currently available generation resources to the public utility are hydro and 100% of these consist of run-of-river plants on the same river (i.e., Shire river). This means that most of the generation depends on a single hydrological system and that the plants do not have significant regulation capacity. Therefore, the available water that is not used to generate power must be wasted (i.e., spilled). This situation is one of the most important features of the generation segment in this country and it creates both challenges and opportunities. In Venezuela, as of the end of 2002, the installed capacity was 19,667 MW, including 12,491 MW of hydro units (see Table 1). The maximum demand reached 12,813 MW with a load factor of over 80% for the same year, meaning that hydro units could not have supplied 100% of energy or demand needs (even before considering transmission and other technical constraints ). These numbers could be misleading if one did not consider that the availability of the thermal generation has been historically poor and hence the system is heavily dependent on the hydro. In evaluating hydroelectric plants, one of the most important input requirements is the historical inflows of the rivers where the plants are located. This information normally takes the form of a relatively long series of historical flows (50 or more years into the past), which can be used for estimating the firm and average energy production from each plant. Firm energy is defined as the production associated with water flows that are likely to be exceeded 95% or more of the time.

Table 1 INSTALLED GENERATION CAPACITY (MW, 2002)

PLANT TYPE MALAWI VENEZUELA

Hydro (Interconnected System) 280 12,491 Hydro (Isolated System) 5 0 Gas Turbines 15 2,623 Diesel 6 27 Steam 0 4,526 Private Generation 52 (*)

TOTAL 358 19,667 (*) There is a total of 2,784 MW of private generation in Venezuela,

included within the values shown in this Table (1,866 MW are steam and 918 MW are gas turbines). In Malawi the value is made of mostly small diesel plants.

Table 2 summarizes the options considered in our study. For Malawi [4], plans were assembled from specific combinations of the following five energy supply options: (i) transmission interconnection with Mozambique (i.e., and international interconnection), (ii) new hydro plants (i.e.,

3

Kapichira II), (iii) rehabilitation of existing hydro plants, (iv) new thermal plants, and (v) pump ed storage. International interconnections increase the size of the electricity market, reduce the need for reserves, add flexibility and increase the size of the maximum unit that can be reliably installed in the system. In the case of Venezuela, twelve options were considered for meeting the energy requirements of Empresas Polar [5]. These included both local/regional as well as system wide solutions (the latter exploiting the obvious economies of scale). Furthermore, the possibility of surplus energy sales to other nearby loads in certain regions, as well as in the future Wholesale Electricity Market [6, 7], were also considered. Options were assembled from specific combinations of the following five energy supply choices (the last four are self-generation options): (i) purchases from the Venezuelan power grid, (ii) internal combustion machines (burning diesel), (iii) combustion turbines (burning dual fuels, natural gas and other), (iv) steam turbines (burning liquid or solid fossil fuels excluding coal), and (v) a combined-cycle power plant. Some of these options required concomitantly strengthening the electrical interconnections from some of the Polar plants to the underlying transmission and/or distribution systems .

Table 2 OPTIONS

OPTION MALAWI VENEZUELA

1 International Interconnection

Connection to Power Utilities (Grid)

2 New Hydro Plants Internal Combustion Units

3 Rehabilitation of Existing Hydro Plants

Combustion Turbine Units

4 New Thermal Plants Steam Turbine Gen-Sets 5 Pumped Storage Combined Cycle Units

Once the options were defined, several plans were formed, as necessary. In our case, eight plans were postulated for Malawi while twelve plans were postulated for Venezuela, as shown in Tables 3 and 4.

Table 3 MALAWI PLANS

(In Terms of the Year of Start of Operations of the Various Options)

PLAN 1 2 3 4 5 6 7 8

Option 1 2006 2006 2006 2006 2008 2008 2008 2008

Option 2 2008 2010 2008 2010 2008 2010 2008 2010

Option 3 2008 2008 2008 2008 2010 2010 2010 2010

Option 4 2010 2010 2012 2012 2010 2010 2012 2012

Option 5 - - 2010 2010 - - 2010 2010

IV. UNCERTAINTIES AND FUTURES

The uncertainties that were modeled included those believed to be the most relevant ones as result of experts-team meetings using Delphi technique (in terms of both the

uncertainty itself and its impact on the outcomes). Table 5 summarizes the uncertainties that were explicitly modeled in the example cases. For Malawi, the uncertainties were: (i) existing load growth, (ii) new mining/smelters load growth, (iii) prices in the South African Power Pool (SAPP), and (iv) the cost of capital. The hydrology of the Shire River was modeled using Monte Carlo techniques, which took into account the monthly variation of flows in the river and the available history of inflows.

Table 4 VENEZUELA PLANS

(In Terms of Total MW of each Option)

PLAN 1 2 3 4 5 6 7 8 9 10 11 12

1 100 75 75 75 50 50 50 25 25 25 25 0

2a 0 10 10 10 10 10 10 10 10 10 10 0

2b 15 5 5 15 15 15 15 20 20 15

2c 25 25

3a 10 15 10 15 15

3b 10 10

3c 30

4a 10 15 15 15 15

4b 10 10

4c 30

O P T I O N

5 100

Note: The difference between options with the same number but different letter (e.g., options 2a, 2b and 2c) is the size of the individual units.

In the case of Venezuela the uncertainties were: (i) the future structure of the electricity and natural gas sectors in the country, (ii) future price and availability of non conventional fuels in Venezuela (particularly Orimulsión), (iii) Empresas Polar’s expected rate of return for investments in self-generation, and (iv) Polar’s expected energy demand. The bounds for the first uncertainties (i.e., the structural ones, that are clearly the most important) were: (1) status quo, and (2) completely open and competitive sectors.

Table 5 UNCERTAINTIES

MALAWI VENEZUELA Load Forecast Electricity Sector Structure New Industrial and Mining Loads Natural Gas Sector Structure

Power Import Prices Price & Availability of Fuels Cost of Capital Expectations Cost of Capital Expectations Load Growth

Load forecasting is a critical element of the power supply planning process. The purpose of any forecast is to estimate future levels of demand to serve. In our case, the main objective was to determine those assumptions about load

4

demand growth that would be utilized in the development of the different scenarios. Optimistic and pessimistic demand scenarios were defined for this purpose. The demand analysis focused on either the power requirements of the country (Malawi) or an industry (Empresas Polar in Venezuela) on a short- mid- and long-term basis. In line with the unknown-but-bounded methodology, we selected two energy and demand forecasts considering available historical data.

V. SCENARIOS

Risk analysis of power supply is typically developed to ensure sufficient resources to maintain an optimum reliability of supply to the load, while at the same time minimizing the cost of supplying (and the cost of not supplying) customers [8]. Additionally, such analysis is aimed at keeping plant redundancy to a minimum. The analysis should (and usually does) include Demand Side Management (DSM) options, on the basis of their economic value in comparison to supply-side options [9]. For the case of Malawi, there were two possible futures for each of the main uncertainties in Table 5 (high and low); their combination amounted to 16 futures. Therefore, the combination of these 16 futures and the 8 proposed plans resulted in a total of 128 scenarios. In the case of Venezuela , the combination of the 32 futures and the 12 proposed plans resulted in a total of 384 scenarios.

VI. ATTRIBUTES

Scenarios are constructed from options and uncertainties, and then characterized in terms of attributes. Attributes included those with the most relevance (in terms of impact). Table 6 summarizes the attributes used in each case.

Table 6 ATTRIBUTES

MALAWI

Present Value of Energy Costs

Present Value of the cost of Energy Not Served (ENS)

VENEZUELA

NPV of the Equivalent Energy Costs

NPV of the Reliability Costs

NPV of the Capital Requirements

VII. INTEGRATED MODEL

Detailed models were developed in each case for a 16-year horizon. The models take into account all the features, assumptions, and data already described in this paper. The models were useful in: 1. assessing the feasibility of the power supply options,

2. calculating the attributes (from the corresponding options and uncertainties),

3. performing the risk analysis from the results obtained,

4. estimating the regret associated with the possible decisions, and

5. designing hedging strategies to lower the risk exposure. The models contained specific routines for the performance of ancillary tasks, such as the calculation of indices measuring the reliability of supply. These indices were calculated by means of Monte Carlo simulations; for the generation units, a Normal probability distribution function was assumed, while for the power grid, a logNormal distribution was selected. Our experience indicates that these are the most appropriate distributions to use. Finally, the various routines in the developed model are automatically linked with each other, both from an input and output perspectives (For the case of the reliability analysis we used a binomial probability for each unit, i.e. probability of being in or out). An important model input is the cost of Energy-Not-Served (ENS). This represents the total costs (i.e., production, market share, and cost of opportunity, among others) incurred when load is failed to be served (due to outages). In the developed model, we parameterized the cost of ENS in terms of outage (i.e., 1, 8 and 24 hours).

VIII. SIMULATION RESULTS, RISK ANALYSIS, AND HEDGING

Figure 3 summarizes the results for all scenarios (128 in total) for the case of Malawi. From this figure we note the following:

• The decision set, consisting of those plans that yield the best results (lower costs), is highlighted with the dotted line.

• There are also four groups of highlighted scenarios, clustered according to the relative area where they are located within the figure. These four groups are discussed below.

• Starting from the top, the first group corresponds to the results of Plans 7 and 8 (see Table 3). The second group (from the top) corresponds to Plans 3 and 4. The two groups on the lower side of the figure correspond to Plans 1, 2, 5 and 6. In all cases, the groups to the right of the dashed line (i.e., the one that divides the figure in two – left and right – sides) correspond to high growth of native load plus high new industrial load.

• The fact that the higher groups include pumped storage indicates that such project may be most likely used to export power to other countries and hence use the interconnection line rather intensively. This fact would explain the values obtained in terms of present value of ENS for these plans, as the logic of the model looks for periods when the interconnection is loaded at more than 20% of the total load in the Malawi system, and associates the sudden loss of this interconnection to the loss of stability and hence a blackout with the corresponding ENS.

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IRP Malawi - 128 Scenarios

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Figure 3. Trade-Offs in the Case of Malawi.

• The fact that the scenarios with the higher present value of the energy costs are related to futures with a high growth in both existing and new industrial – mining and smelters – load is not surprising, since investment levels for such futures per unit of energy are higher (i.e., the cheapest projects come first, while more expensive projects are in place only when the load requires them to be executed). This fact is a strong indication of the necessity of implementing DSM initiatives [10].

Thus, in the case of Malawi, unless there is low load growth, all of the plans in the decision set include an international interconnection as early as possible (2006). Table 7 shows the summary of results from the regret (hedging) analysis. In this table, the values shown represent the maximum “regret” of each plan, (i.e. the maximum difference between the plan’s outcome and the best possible outcome for all futures). Here we observe that the decision set may initially include Plans 1 and 5, since these plans minimize the regret for some of the attributes. Plan 2 is not part of the decision set although it has similar results as Plan 1 and slightly better present value of capital requirements. Since no plan has the minimum regret for every attribute (i.e., there is no “robust” plan), a detailed hedging analysis had to be developed, considering the expected total value of the projects (measured from the perspective of attributes, adding computed net present values), level of regret (calculated as the difference between the values for the best and

worst possible scenarios, i.e., the plan with the lowest maximum regret would be the preferred one), and its volatility (also comparing upside versus downside from the expected value). Plan 1 was chosen as its results present the least volatility, i.e. there are no large excursions in the regret.

Table 7 SUMMARY OF RESULTS FROM THE MINIMUM REGRET

ANALYSIS [MUS$]

PLAN

ATTRIBUTE 1 2 3 4 5 6 7 8

A1 11 19 2 18 12 12 0 1 A2 0 0 10 9 3 3 13 14 A3 25 19 57 52 6 0 43 37

A1 = PV of the Cost of Energy A2 = PV of ENS (Cost of Energy Not Served) A3 = PV of Capital Requirements

In the case of Venezuela, the results of the analysis showed that – as expected - the best options are widely different for the various possible materializations of the future structure of the electricity and natural gas sectors in Venezuela [5]. The maximum risk exposure to Empresas Polar varies widely with the possible materializations of the structural uncertainties (i.e., future structure of the electricity and natural gas sectors in

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Venezuela). In fact, the maximum regret, characterized in terms of technological options, is as follows:

• Power supply from the grid: approximately US$ 1,500 million. This relatively large exposure is fundamentally due to the impending threat of widespread electricity rationing in the country, and the increasingly deteriorated reliability of service from the grid. (Note: The immediate threat of wide spread rationing in Venezuela seems to have been adverted for now, due to a combination of better water inflows to the Guri reservoir and the commissioning of the Caruachi Hydroelectric project. However the structural problems in the Venezuelan sector still remain as the thermal supply is still poor).

• Power supply mainly from diesel units: about US$ 800 million,

• Power supply primarily from diesel, gas, and steam units: around US$ 400 million, and

• Power supply from a single combined-cycle natural gas unit (with backup from the grid): about US$ 200 million.

The above results point to the fact that in order to reduce the risk exposure to acceptable levels, Empresas Polar will likely need to engage in a significant amount of self-generation in the future. Otherwise (and this means the power grid-only option is implemented), Empresas Polar exposes itself to the risk of significant supply shortages in the future. Empresas Polar reduced its risk exposure by implementing the following two actions:

• installing own-generation at some of its plants (to satisfy approximately 15% of its electricity requirements). This action lowered the maximum regret from approximately US$ 1,500 million to an amount between US$ 1,500 and US$ 800 million; and

• implementing an “emergency program”, consisting of, among other measures, the lease of a number of diesel gen-sets for a period of several years. This lowered the maximum regret levels at that time to approximately US$ 850 million.

IX. CONCLUSIONS

The trade-off risk analysis methodology has once again proven to be a very useful tool for the strategic evaluation of options available to meet future energy requirements. The methodology is particularly useful in the: (i) analysis of available options, (ii) formulation of scenarios, (iii) analysis of risks, and (iv) design and evaluation of hedging strategies. We have demonstrated the flexibility and usefulness of the methodology by applying it to two very different real-world cases, one involving the future energy requirements of an entire country (Malawi), and the other (Empresas Polar, Venezuela) for the development of a strategic energy plan in the short, medium, and long run.

X. REFERENCES [1] Schweppe, F.C., H.M. Merrill, and W.J. Burke, “Least -Cost Planning:

Issues and Methods”, Proceedings of the IEEE, Vol. 77, No 6, June 1989. [2] Merrill H.M., and P.D. Fuller, “Playing Leapfrog with a Unicorn: Risk

and Uncertainty in Power System Planning”, Minnesota Power Systems Conference, St. Paul, MN, October 1992.

[3] Pereira, M.V.F., M.F. McCoy, and H.M. Merrill, “Managing Risk in the New Power Business,” IEEE Computer Applications in Power, April 2000, pp. 18-24.

[4] Dortolina, C.A., N. Bacalao, R. Nadira, and P. De Arizón, “Integrated Resource Planning in Developing Countries – A Novel Practical Approach”, Invited P aper, approved for presentation in the Panel Session titled “Integrated Resource Planning and Sustainability in the Deregulated Environment”, in the 2004 IEEE 4PES General Meeting, to be held in Denver, Colorado, 6-10 June, 2004.

[5] Dortolina, C.A., H. Fendt, R. Nadira, N. Bacalao, and J. Di Bella, “Supply Risk Analysis in Electricity Markets from the Perspective of a Large Customer”, paper approved for presentation at the 2004 IEEE PES General Meeting, to be held in Denver, Colorado, 6-10 June, 2004.

[6] Dortolina, C.A., and J.A. Martínez, “Evaluation of Energy Supply Options in Venezuela – Deregulation of the Gas and Electricity Industries”, paper presented at the 2002 CIGRE Conference: Gas and Electricity Networks – Complementarity or Competition?, Brasilia, Brazil, May 2002.

[7] Dortolina, C.A., “Venezuelan Restructured Electricity Market - Analysis of a Dominant Firm’s Market Power” 2001 IEEE PES Summer Power Meeting , Vancouver, BC, Canada.

[8] Harry G. Stoll, “Least -Cost Electric Utility Planning”. John Wiley & Sons, Inc., 1989.

[9] Swisher J., G. de Martino, and R. Redlinger, “Tools and Methods for Integrated Resource Planning – Improving Energy Efficiency and Protecting the Environment”, United Nations Environment Program, November, 1997.

[10] Electric Power Research Institute, “Efficient Electricity Use. Estimates of Maxim um Energy Savings”, EPRI/CU-6746, 1990.

XI. BIOGRAPHIES Carlos A. Dortolina (SMIEEE) received an Electrical Engineering degree from Universidad Simón Bolívar (USB), Caracas, Venezuela, the MSEPE from Rensselaer Polytechnic Institute (RPI), Troy, NY, and the MBA in Economics from Universidad Católica Andrés Bello, Caracas, Venezuela. He is currently a Senior Consultant in the Project Services Practice of Stone & Webster Management Consultants, Inc. Ramón Nadira received an Electrical Engineering degree (Summa Cum Laude) from USB, Caracas, Venezuela, and the M.Sc. and the Ph.D. degrees, both from Case Western Reserve University, Cleveland, Ohio. He is currently a Vice President with the Project Services Practice of Stone & Webster Management Consultants, Inc. Hans Fendt (SMIEEE) received an Electrical Engineering degree (Cum Laude) and a Graduate degree in Technology Management, both from USB, Caracas, Venezuela, He is currently Corporate Utilities Manager of the Beer and Malt Business Unit at Empresas Polar in Venezuela. Nelson J. Bacalao received an Electrical Engineering degree (Cum Laude) from USB, Caracas, Venezuela, the M.Sc. from RPI, Troy, NY, the Ph.D. from University of British Columbia, Canada, and the MBA from the Instituto de Estudios Superiores de Administración (IESA), Caracas, Venezuela. He is an Executive Consultant with the Project Services Practice of Stone & Webster Management Consultants, Inc. Jacobo Di Bella (MIEEE) received an Electrical Engineering degree and a Graduate degree in Technology Management, both from USB, Caracas, Venezuela, He is currently Corporate Senior Engineer of the Power System Department at Empresas Polar in Venezuela.

Integrated Resource Planning in Developing Countries – A Novel Practical Approach

Carlos A. Dortolina, Senior Member, IEEE, Nelson Bacalao, Ramón Nadira, and Paloma De Arizón, Member, IEEE

Abstract: Integrated Resource Planning (“IRP”) is an effective approach to energy utility planning for future energy requirements. The goal of IRP is the identification of resources and/or the mix of resources for meeting near and long term consumer energy needs in an efficient and reliable manner at the lowest reasonable cost. These plans take into account the cost, effectiveness, and benefits of all appropriate, available, and feasible power generation and customer options. This paper presents an IRP developed for an African country, detailing a projection of supply- and demand-side capacity requirements to meet national objectives during the 2004-2020 periods. It also presents the risk assessment methodology employed; describing its implementation in terms of a computer model developed specifically for this particular case, and discusses the main results from the application of this model. Finally, we recommend several hedging strategies designed to mitigate the risk exposure. .

I. INTRODUCTION

he main objective of this research was to develop an original applied methodology to provide the Electricity

Regulator of an African nation with an economic planning tool, which should lead to determining the lowest practical cost at which the Electricity Supply Industry can deliver reliable energy services to its customers. The complex nature of modern electricity planning, which must satisfy multiple economic, social and environmental objectives, requires the application of a planning process that integrates these often-conflicting objectives and considers the widest possible range of traditional and alternative energy resources. Traditional electricity planning has sought to expand supply resources to meet anticipated demand growth with very high reliability, and to minimize the economic cost of this expansion (see Figure 1). These criteria, aided until recently by improving economies of scale in electric generation, led to a nearly-universal strategy of rapid capacity expansion and promotion of demand growth, with little consideration of the necessity or efficiency of energy use. In addition, in many developing countries, dependent on the Donor Community, there is

C. A. Dortolina is with Stone & Webster Management Consultants, Houston, TX 77077, USA (email:[email protected])

N. Bacalao is with Stone & Webster Management Consultants, Houston, TX 77077, USA (email:[email protected])

R. Nadira is with Stone & Webster Management Consultants, Houston, TX 77077, USA (email:[email protected])

P. De Arizón is with Shaw Power Technologies, Houston, TX 77077, USA (email:[email protected])

also a dynamic in play where the expansion the system can be driven more by the availability of projects of interest to Donor Nations than the priorities of supply. This can lead to additions not always coherent with the best long term expansion of the existing system and/or the immediate needs. Further, during the past few years increasing supply costs and environmental constraints have reduced or removed incentives for capacity expansion, and the concept of "least-cost" utility planning has begun to be completely redefined worldwide.

Technical Criteria

Load Forecast

Supply Expansion Plan

Production Costs and

Pricing

Cost of Capital and Rate of

Return

Revenues and Profits

Costs of Supply Alternatives

Technical Criteria

Load Forecast

Supply Expansion Plan

Production Costs and

Pricing

Cost of Capital and Rate of

Return

Revenues and Profits

Costs of Supply Alternatives

Fig. 1. Traditional “Least-Cost” Planning.

Rather than least-cost supply expansion, modern utility planning is evolving toward IRP (see Figure 2). This means integrating a broader range of technological options, including technologies for energy efficiency and load control on the "demand-side," as well as decentralized and non-utility generating sources, into the mix of potential resources. Additionally, it means integrating a broader range of cost components, including environmental and social costs, into the evaluation and selection of potential technical resources. Incentives for the different stakeholders (utilities and customers, mainly) must be considered to obtain final revenues, as a function of performance. In this sense, an important feature of this model is the feedback that must exist of prices and customers’ incentives.

T

The final objective of the IRP is to provide a level “playing field” where all the attributes of different supply options are valued. Hence the expected result of the market - and non-market - changes brought about by IRP is to create a more favorable economic environment for the development and application of efficient end-use technologies and cleaner and less centralized supply technologies, including renewable sources. IRP means that these options will be considered, and the inclusion of environmental costs means that they will appear relatively attractive compared to traditional supply options.

The difficulty with implementing such changes in developing countries is that the value of environmental quality is not generally traded in the market, since it is a common social good, and that the benefits of energy efficiency technologies not always accrue or are perceived by the decision makers, due to various distortions, e.g. in the relative fuel prices, and/or institutional barriers. Thus, planning, regulation and fiscal incentives has been used to correct these problems and to provide signals to move the Electricity Supply Industry (ESI) toward cleaner and more efficient energy technology. Higher electricity prices are often needed to implement the plans and resource allocations resulting from IRP, but price measures are not a sufficient solution in an ESI with imperfect competition and incomplete information.

The results of the model are documented in this paper, as an example of how to develop a strategic energy plan in the short, medium and long run.

Risks (including environmental)

Load Forecasts

Expansion Plan Options

Total Production

Costs (including social costs)

Revenues

Demand Side (energy saving)

Initiatives

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Initiatives

Integrated Model, Risk Analysis and

HedgingCost of Service

Risks (including environmental)

Load Forecasts

Expansion Plan Options

Total Production

Costs (including social costs)

Revenues

Demand Side (energy saving)

Initiatives

Supply Side (energy costs)

Initiatives

Integrated Model, Risk Analysis and

HedgingCost of Service

Fig. 2. Integrated Resource Planning.

II. SOLUTION APPROACH

IRP is the combined development of electricity supplies and energy-efficiency improvements, including Demand-Side Management (“DSM”) options, to provide energy services at

minimum cost, including environmental and social costs. The implementation of IRP generally requires:

1) collection of reliable data on electricity end-use

demand patterns and technical alternatives for improving their energy-efficiency or load profiles,

2) definition and projection of future energy-service demand scenarios,

3) calculation of costs’ impacts of the demand-side alternatives,

4) determination of economic costs and environmental impacts of conventional and alternative electricity supply options, to be compared with DSM,

5) design of an integrated supply and demand-side plan that satisfies the least-cost criteria in terms of economic costs and environmental impacts, and

6) implementation of the least-cost strategy.

Furthermore, the integrated least-cost planning process has the following characteristics:

1) The process recognizes multiple stakeholders, such

as various classes of ratepayers, investors, and other elements of society. Each stakeholder may measure the cost or goodness of a plan differently.

2) Planning options are evaluated on a level playing field, using criteria and methods that do not unfairly bias the selection of alternatives nor unfairly represent the interest of a single stakeholder.

3) Uncertainties are treated explicitly.

IRP explicitly addresses the full range of options for investments to expand the provision of energy services. This sub-section presents some of the principal actions that are commonly considered in an IRP process.

• Integrating both DSM & Loss Reduction Programs with Supply Expansion Plans

The implementation of energy-efficiency measures via DSM is a common change resulting from the use of IRP. IRP is particularly appropriate for developing countries (such as African countries), where there are often severe capital constraints and a large potential for electricity loss (both technical and non-technical) reduction.

• Integrating Non-Utility with Utility Generation

Recent years have seen a drastic shift away from the construction of large central power stations by electric utilities. In the case of developing countries, this has been the result of a trend toward deregulation and/or privatization in the electricity sector. This change is leading to the advent of competition for supplying utility power and a more general deregulation of the power sector. A further goal of IRP may be then to allow the evaluation of such sources on an equal basis with central supply expansion.

• Integrating Risks with Cost Analysis

One of the principal reasons for pursuing energy-efficiency improvements is that energy consumption leads to pervasive externalities, ranging from local pollution and global greenhouse gases to risks associated with uncertainties, that are not necessarily reflected in energy supply costs and planning efforts.

• Integrating the Customer with the Utility Perspective

Since in most of the developing countries the government is mainly responsible for managing the energy sector, and a reliable supply of electricity is considered an essential public service, most governments would need to shift their traditional focus of power-development by adding supply to the efficiency with which all investment resources are used.

III. METHODOLOGY

The approach used was the Unknown but Bounded, which accounts for the limits on the modeled uncertainties, with no assumption about probability distributions. The consideration of alternatives in the process of planning involves setting up input uncertainties describing the states of the world considered relevant for the planning process, and testing how options would perform in that context. This would yield tradeoffs in the attribute space. An example of this is shown in Figure 1, which shows the trade-off between two attributes: Energy Costs [in US$/MWh] and Unavailability Costs [in US$/year]. Each point in this figure shows a scenario. The figure also illustrates the process whereby a decision set can be arrived at. A decision set consists of those scenarios, which are not completely dominated by others. In Figure 3, the scenarios that belong to the decision set are linked together by a line. It is obvious that there are no other scenarios, which would be better than those on the decision set regarding the attributes shown. Hence, all of those outsides the decision set can be dropped from future consideration. Additionally, it is usually assumed that people act rationally when they compare the costs and benefits of any activity and then decide to engage in that activity if that choice maximizes return relative to cost. This is not a particularly “economic theory”, it is the foundation of most theories in psychology relating to human behavior. It is also important to understand that this comparison is a very subjective analysis. That is to say: the costs and/or the benefits of engaging in any economic activity are likely to be different for any two people. The Unknown but Bounded approach yields as decision set, but ultimately the Agent has to make a decision. In our case we selected the method known as the Minimum Regret as the tool to arrive to this decision.

Regret, which is strongly related to the cost of opportunity, is said to be the difference between what you got, and the best you could have gotten, given a specified outcome. The

Minimum Regret, also known as the MaxiMin (or MiniMax) method, seeks to maximize the profit derived from a decision, while minimizing the adverse consequences of it.

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Fig. 3. Strategic Model. Tradeoff Example.

IV. DEMAND-SIDE INITIATIVES AND LOAD FORECAST

As it was stated above, DSM programs involve a systematic effort to manage the timing or amount of electricity demanded by customers. Usually DSM programs are developed and implemented by the corresponding utility. However, in some countries government agencies such as electricity regulators can also take action in these DSM efforts. DSM strategies consider initiatives aiming to change the shape of the load curve or the total area under the load curve (i.e. the total energy consumed), or can be a combination of both goals.

Figure 4 describes the classical DSM strategies. Electrical utilities or regulators can design programs combining two or more of the load shaping strategies, aiming to modify the load profiles of their customers and/or total energy demanded.

Figure 4A represents the objective of reducing the peak of the load curve. It can be achieved by raising tariffs during peak-hours, for example and lowering them off-peak. Reducing the peak does not necessarily decrease overall energy consumption, as shown in Figure 4C. This strategy is particularly useful when the supply consists of run of river plants, which could be spilling water overnight. Load shifting (C) is the DSM equivalent of a pumped storage facility.

Figure 4B and Figure 4E illustrate the objective of increasing electricity sales during certain hours. In the first case efforts are made to direct load growth toward specific periods during the day (although it could also be during the year), and the second case promotes general load growth. In some cases, increased electricity use can result from fuel-switching that reduces direct fuel use, so electric load growth does not necessarily (though usually) increase total primary energy consumption.

Figure 4F represents a situation where a utility has the possibility to create a flexible load curve that can accommodate customers' demand and the utility's operational characteristics. For example, in a hydroelectric system during the dry-season the utility is interested in reducing electricity demand, but during the wet-season it has the opposite situation. Direct load control is one technology used for this purpose.

Strategic Load Growth

(E)

Flexible Load Shape

(F)

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(D)

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(B)

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(C)

Peak Clipping

(A)


(E)


(E)

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(D)


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(B)

Valley Filling

(B)

Load Shifting

(C)

Load Shifting

(C)

Peak Clipping

(A)

Peak Clipping

(A)

Fig. 4. DSM Load Shape Objectives.

Not all the programs researched and investigated met the selection criteria for inclusion in this IRP. In fact, due to the particular conditions of the electricity sector in this selected African country (i.e., mainly run-of-river hydro plants), the DSM option that met the selection criteria and was most attractive is the load shifting alternative (Figure 4C). The benefits from these alternatives were investigated by the modeling of the pump-storage equivalent and a gradual increase in the load factor of the demand.

Load Forecasting is a critical element during the planning process. The purpose of any forecast is to estimate future levels of demand to serve; and this information will be the basis for the IRP. The main objective of this activity is to determine those scenarios of demand that would be utilized in the development of the different scenarios. Optimistic and pessimistic demand scenarios were defined for this purpose.

The demand analysis focused on the domestic retail power requirements of the country on both a short- and long-term basis. Retail is the fundamental driver of all power sector activity. The retail analysis decomposed the total retail sales forecasts into per class analyses to examine the relative importance of each class to electricity demand.

A detailed load forecast was beyond the scope of this research, but as this input is crucial for the IRP, extensive efforts were made to gather and use as much information as possible. Consistent with the unknown but bounded methodology, we selected two energy and demand forecasts considering historical data provided by the utility. These included historical data on number of new accounts, electrification level, energy sales (residential, general, LV high consumption and MV customers), average losses and expected new large industrial loads.

The only way to determine the accuracy of any load forecast is to wait until the forecast year has ended and then compare the actual load to the forecast load. Even though the whole idea of forecasts is accuracy, the only thing certain about any long-range forecast is that it can never be absolutely precise. Forecasting accuracy depends on (i) the quality and quantity of the historical data used, (ii) the validity of the forecast basic assumptions, and (iii) the accuracy of the forecasts of the demand-influencing factors (population, income, and price). Since none of these is ever perfect, long-term load forecasts are typically reviewed annually. Even so, there is simply no assurance that electricity demand will be as forecasted, no matter what method is used or who makes the forecast.

V. SUPPLY-SIDE INITIATIVES.

The particular conditions of the electric generation in this African country are quite unique and no ready-made recipes for IRP are applicable. Therefore, this section summarizes the nature and issues affecting electric generation in this country, followed by a discussion on how these issues affect the supply-side initiatives. 100% of the generation in Table 1 consists of run-on-river plants located on the same river. This means that all the generation depends on a single hydrological system and the plants do not have significant regulation capacity. Therefore the plants must either use the water for generation as it flows or spill it. This situation is one of the most important features of the generation segment in this country and it creates both challenges and opportunities.

Table 1. Total Installed Generation in Country (2002 data).

Type of Plant Capacity [MW] Hydro (Interconnected System) 280 Hydro (Isolated System) 5 Gas Turbines 15 Diesel 6 Private Generation 52

TOTAL 358

The available capacity of the total system is approximately 210 MW, which is very close to, if not lower than the system peak (peak was about 209.9 in 2003). However, these plants could generate an average of over 1,800 GWh in a year (at the observed availability), 60% more than the required energy. These plants are capable of producing almost double the country’s current requirement assuming internationally acceptable values of availability. Even the firm energy (energy available under extremely dry conditions) is approximately 20% higher than the requirements.

In evaluating hydroelectric plants, one of the most important information points is the hydrology of the rivers where the plants are located. This information normally takes the form of a series of historical flows for 50 or more years, that can be used for estimating the average energy and firm energy production (that is the production associated with water flows that are exceeded 95% of the time), in each plant. There cannot be a good IRP, if there are no options to choose from. Considering more options would improve the

chances that the plans originated by sorting them will be robust and ensure the desired outcomes. Next section illustrates the scenario analysis and integrated model developed to design an appropriate IRP for this African country.

VI. SCENARIO ANALYSIS AND INTEGRATED MODEL.

IRP plans are developed to ensure sufficient resources (in service and in reserve) to maintain an optimum reliability of supply to the country, minimizing the cost of supplying and the cost of not supplying customers. Additionally, these plans are aimed at keeping plant redundancy to a minimum. They also usually include DSM options (hence we talk about resources) on the basis of their economic value in comparison to supply-side options. This section presents a summary of the scenarios produced in the IRP model and the results obtained.

A. Options & Plans

Table 2 summarizes the options considered in our study. Once the options are defined, several plans are formed, as necessary. In this case, twelve plans were postulated, as shown in Table 3.

Table 2. OPTIONS CONSIDERED

# Options

1 International Interconnection

2 New Hydro Plants

3 Rehabilitation of Existing Hydro Plants

4 New Thermal Plants

5 Pump Storage

Table 3. PLANS - - Year of Start of Operations

Plan #

Option #1

Option #2

Option #3

Option #4

Option #5

1 2006 2008 2008 2010 - 2 2006 2010 2008 2010 -

3 2006 2008 2008 2012 2010 4 2006 2010 2008 2012 2010 5 2008 2008 2010 2010 -

6 2008 2010 2010 2010 - 7 2008 2008 2010 2012 2010 8 2008 2010 2010 2012 2010

B. Uncertainties & FUTURES

The uncertainties that were modeled included the most relevant (in terms of both the uncertainty itself and its impact) set of variables. Table 4 summarizes the modeled uncertainties.

Table 4. MODELED UNCERTAINTIES

Number Uncertainties

1 Load Forecasts

2 New Industrial and Mining Loads

3 Power Import Prices

4 Cost of capital expectations

There are two possible futures for the main uncertainties above (high and low) and their combination amount to 16 futures as shown in Table 5. Therefore, the combination these 16 futures, and the 8 proposed plans, account for a total of 128 scenarios.

Table 5. MODELED FUTURES

UNCERTAINTIES

# Load Forecasts

New Industrial & Mining Loads

SAPP Prices

Cost of Capital

Expectations A High High High High

B High High High Low

C High High Low High

D High High Low Low

E High Low High High

F High Low High Low

G High Low Low High

H High Low Low Low

I Low High High High

J Low High High Low

K Low High Low High

L Low High Low Low

M Low Low High High

N Low Low High Low

O Low Low Low High

P Low Low Low Low

C. Attributes

The considered attributes included those with the most relevance (in terms of impact). Table 6 summarizes the considered attributes.

Table 6. CONSIDERED ATTRIBUTES

# Attributes

1 Present Value of Energy Costs

2 Present Value of the cost of Energy Not Served (ENS)

A specific model was developed for the 2004-2020 time period. Scenarios are constructed from options and uncertainties, and then characterized in terms of attributes.

VII. RESULTS

Figure 4 summarizes the results for every considered scenario (128 in total). From this figure we note the following:

• The decision set, i.e. those plans and conditions that produce the best results (lower costs), is highlighted with the red line

• There are also highlighted four groups of scenarios, clustered according to the relative area where they are located within the Figure. These four groups are discussed below.

• Starting from the top-right the first group corresponds to the results of high existing load growth and high new industrial load (scenarios A, B, C and D). Plans 3, 4,5,6,7 and 8 are within this area. The fact that plans 1 and 2 are not there indicates that under these high load conditions these two plans dominate the other six (are better in all aspects as they are located closer to the origin of the graph).

• The second group to the left of the first corresponds to the results under intermediate total load conditions, as it considers that there is low growth of existing load but the new industrial (mining) loads materialize (scenarios I, J, K and L). Plans 5, 6, 7 and 8 are within this area. The fact that plans 1 through 4 are not there indicates that under this intermediate load conditions these plans dominate the other four (are better in all aspects).

• The third group below and to the left of the second considers that there is high growth of existing load but the new industrial (mining) load materialize at their minimum (scenarios E, F, G and H). Again only plans 5, 6, 7 and 8 are within this area. The fact that plans 1 through 4 are not there indicates that also under this variation of intermediate total load these plans again dominate the other four (are better in all aspects).

• The final group (lower right corner) corresponds to low load conditions (new large industrial and existing). It includes some of the best results in terms of cost of energy and ENS. It includes plans 1, 2, 5 and 8. The fact that low demand growth produces the best results in terms of cost of service is a clear indication of the advantages of DSM.


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Fig. 4. IRP Results [in US$/MWh].

Table 7 shows the summary of results from the regret analysis. In this table the values shown represent the maximum “regret” of each plan, (i.e. the maximum difference between the plan’s outcome and the best possible outcome for all futures). Here we observe that the decision set may initially include Plans 1 and 5, since these plans minimize the regret for some of the attributes. Plan # 2 is not

part of the decision set although it has similar results as Plan # 1 and slightly better present value of capital requirements. Since no Plan has the minimum regret for every attribute, Plan 1 was chosen as its results present the least volatility, i.e. there are no large excursions in the regret..

Table 7. SUMMARY OF RESULTS FROM THE MINIMUM REGRET ANALYSIS [MUS$]

Plan # 1 2 3 4 5 6 7 8

A 4.2 4.6 12.0 12.2 5.5 7.6 10.1 10.9

B 0.1 0.2 15.0 15.0 138 179.2 309.0 357.7

C 21 15.9 238.9 234.3 5.0 6.6 225.8 221.1

A = PV of Energy Costs

B = PV of ENS (Cost of Energy Not Served) C = PV of Capital Requirements

VIII. CONCLUSIONS

Economic development requires increased access to commercial energy in developing countries, as increasing urbanization and industrialization both create greater demands for energy. Urban populations demand high levels of transportation (of people and goods) and other energy-using services. Building and operating the urban infrastructure and industrial and commercial facilities all require energy, especially electricity. Rising living standards result in greater demands for energy-consuming services, such as private transportation and home appliances. Moreover, rural electrification is a major priority in many developing nations, where a small supply of electricity can significantly improve living conditions. Whereas the base plan is the least cost option, it is important to incorporate strategies for fuel and geographic diversity and multiple investment opportunities in the energy market. These strategies will produce better relative environmental performance and have greater potential for regional development. Based upon the results of our analysis, we conclude as follows: The trade-off risk analysis methodology proved to be a very useful tool for the strategic evaluation of options available to developing countries to meet their energy requirements. It aided in the: (i) formulation of scenarios, (ii) analysis of available fuels and generation technologies, (iii) analysis of risks, and (iv) design of hedging strategies. A simulation model based upon this methodology was developed. The model simulates the situation for the country for the 2004-2020 time frame and combines 16 uncertainties and 8 options to obtain 128 scenarios, and then characterizes these in terms of three (3) attributes: (1) present value (PV) of the cost of energy [US$/MWh], (2) PV of the ENS [US$/MWh], and (iii) PV of capital requirements [MUS$]. The results show that – as expected - the best options for the ESI are different for the various possible materializations of the uncertainties. It appears that the key for the Plans included within the decision set is that unless there is low load growth, all the best plans include an international interconnection as early as possible (2006). International Interconnections increase the size of the electricity market, reduce the need for reserves, add flexibility and increase the size of the maximum unit that can be reliably installed in

the system. This result is a constant in our experience, as it allows the countries to move away from the ‘curse of small size’. Under the lower growth scenarios it would be feasible to delay this interconnection (provided that the existing plants are restored to minimum acceptable levels for availability) in which case the interconnection could be delayed to 2008. However this option resulted to be very volatile and hence risky.

IX. REFERENCES [1] Schweppe, F.C., H.M. Merrill and W.J. Burke “Least-Cost Planning:

Issues and Methods”, paper published at the Proceedings of the IEEE, Vol. 77, No 6, June 1989.

[2] Merrill H.M. and P.D. Fuller “Playing Leapfrog with a Unicorn: Risk and Uncertainty in Power System Planning”, paper published at the Minnesota Power Systems Conference, St. Paul, MN, October 1992.

[3] Harry G. Stoll. “Least-Cost Electric Utility Planning”. John Wiley & Sons, Inc. 1989.

[4] Pereira, M.V.F., M.F. McCoy, and H.M. Merrill, “Managing Risk in the New Power Business,” IEEE Computer Applications in Power, April 2000, pp. 18-24.

[5] Swisher J., G. de Martino, and R. Redlinger, “Tools and Methods for Integrated Resource Planning – Improving Energy Efficiency and Protecting the Environment”. United Nations Environment Program. November 1997.

[6] Electric Power Research Institute, “Efficient Electricity Use. Estimates of Maximum Energy Savings”, EPRI/CU-6746, 1990.

X. BIOGRAPHIES Carlos A. Dortolina (SM) received an Electrical Engineering degree from Universidad Simón Bolívar (USB), Caracas, Venezuela, and the MSEPE from Rensselaer Polytechnic Institute, Troy, NY, and the MBA in Economics from Universidad Católica Andrés Bello, Caracas, Venezuela. He is a Senior Consultant with Stone & Webster Management Consultants. His experience covers a wide range of topics including privatization and regulation, strategic planning, tariff design, risk management and demand forecasting. Nelson Bacalao received an Electrical Engineering degree (Cum Laude) from USB, Caracas, Venezuela, the MSc. from RPI, Troy, NY, the PhD from University of British Columbia, Canada, and the MBA from the Instituto de Estudios Superiores de Administración (IESA), Caracas, Venezuela. He is currently Executive Consultant in the Project Finance Services Practice at Stone & Webster Consultants, Inc. Dr Bacalao provides advice to public utility regulators, multilateral banks and private investors across the world. His areas of expertise include electric power regulation, evaluation of technical and financial performance of Transmission and Distribution companies, as well as ratemaking in the context of restructuring of the electric power sector. Ramón Nadira received an Electrical Engineering degree (Summa Cum Laude) from USB, Caracas, Venezuela, and the M.Sc. and the Ph.D. degrees, both from Case Western Research University, Cleveland, Ohio. He is currently Vice-President in the Project Finance Services Practice at Stone & Webster Consultants. For over 23 years, Dr. Nadira has provided technical consulting services to electric utilities, independent project developers, and the financial community, in domestic as well as international assignments in the electric power sector. He has recently participated and/or directed independent technical consulting services for electricity transmission and distribution (T&D) companies in several countries around the world. Paloma De Arizón (M) received an Electrical Engineering degree (Summa Cum Laude) from USB, Caracas, Venezuela, the MSc. and the PhD from University of British Columbia, Canada, and the MBA in Finance from USB, Caracas, Venezuela. She is currently Senior Consultant in Shaw Power Technologies, Inc. Dr. De Arizón has wide experience as a consulting engineer, having managed a number of technical and economical feasibility studies related to generation and transmission expansion alternatives, insulation coordination, harmonic analysis and compensation for electric utilities and the oil industry.

SUPPLY RISK ANALYSIS IN ELECTRICITY MARKETS FROM THE PERSPECTIVE OF A

LARGE CUSTOMER Carlos A. Dortolina, Senior Member, IEEE, Hans Fendt, Senior Member, IEEE, Ramón Nadira,

Nelson Bacalao and Jacobo Di Bella, Member, IEEE,

Abstract: It is imperative for large customers in countries that have recently restructured (or are currently restructuring) their energy sectors to carefully analyze - from a strategic point of view - the options available for securing their energy requirements. What makes the process challenging is the presence of new and significant uncertainties, such as those related to the forward energy prices in wholesale markets. This paper presents the results of applying a decision analysis technique which takes explicit account of uncertainties to strategically evaluate the supply options available to Empresas Polar (the “Company”), a large industrial customer in Venezuela.

The paper will help electric utilities and energy traders better understand the most important needs of large customers, in order to optimize the design of their marketing strategies to these important loads.

I. INTRODUCTION

iven the uncertainties associated with the future of the energy sector in Venezuela, it is imperative for industrial

customers in this country, to carefully analyze - from a strategic point of view - the available options to secure the supply of their current and future energy needs, economically and reliably. The uncertainties, which are rather significant, are driven in part by the following considerations:.

1. the legal and regulatory frameworks for both the electric power and natural gas sectors in Venezuela are still in an evolving phase;

2. Venezuela relies heavily on hydroelectric generation and due to a sequence of historically dry conditions, there was a perceived risk of widespread electricity rationing in the country.

3. there is a declared deficit of natural gas in Venezuela. Further, the future availability of natural gas remains uncertain despite recent efforts to jump-start the process of

C. A. Dortolina is with Stone & Webster Consultants Inc., Houston, TX 77077, USA (email: [email protected]) H. Fendt is with Empresas Polar, Caracas, Venezuela (email: [email protected]) R. Nadira is with Stone & Webster Consultants Inc., Houston, TX 77077, USA (email: [email protected]) N. Bacalao is with Stone & Webster Consultants Inc., Houston, TX 77077, USA (email: [email protected]) J. Di Bella is with Empresas Polar, Caracas, Venezuela (email: [email protected] )

development of non-associated natural gas fields in Venezuela;

4. Despite the enactment of rather forward looking electricity and natural gas laws, the Venezuelan energy policy is considered by many sectors as unclear and lacking definition, and

5. there is a lack of formal and detailed plans to meet the anticipated growth in electricity demand, with its perverse effect of making future electricity uncertain.

The major objective of the paper is to show the methodology followed while assessing available power supply options, considering the technical, economical, and regulatory/institutional realities of the energy sector in Venezuela. Emphasis is placed on electricity, natural gas and other liquid fuels as the primary energy sources available.

The methodology and results are documented in this paper, which could help develop any industrial company’s strategic energy plan in the short, medium and long run.

II. RISK ASSESSMENT METHODOLOGY

Conceptually, the risk/opportunity for any event can be defined as a function of the uncertainty and its consequence (its impact):

Risk/Opportunity = f(event, uncertainty, impact)

Therefore, Risk is the hazard to which a company is exposed because of uncertainty. Risk has to do with attributes like cost of electricity, capital requirements, and environmental effects, but there is considerably more to risk than how these might vary. Risk is also characteristic of decisions, with two dimensions:

• The likelihood that a decision is regrettable, and

• The amount by which the decision is regrettable.

If the uncertainty or the impact increases so does the risk. In order to properly assess Risk it is paramount to model adequately the Uncertainty. Uncertainty deals with those factors that can have a major influence an Agent and are not under its control or cannot be predicted with certainty. These uncertainties may be modeled probabilistically if the distributions are known, and if the problem being studied is consistent with the law of large numbers. If it is not, or if the probability distributions are not available, uncertainties can be modeled as unknown-but-bounded variables. This second

G

modeling approach contains less information than a probabilistic model, but is often more appropriate. This is further discussed below.

The approach used was Unknown but Bounded, which accounts for the limits on the modeled uncertainties, with no assumption about probability distributions. The consideration of alternatives in the process of planning involves setting up input uncertainties describing the states of the world considered relevant for the planning process, and testing how options would perform in that context. This would yield tradeoffs in the attribute space. An example of this is shown in Figure 1, which shows the trade-off between two attributes: Energy Costs [in US$/MWh] and Unavailability Costs [in hours/year]. Each point in this figure shows a scenario. The figure also illustrates the process whereby a decision set can be arrived at. A decision set consists of those scenarios, which are not completely dominated by others. In Figure 1, the scenarios that belong to the decision set are linked together by a line. It is obvious that there are no other scenarios, which would be better than those on the decision set regarding the attributes shown. Hence, all of those outsides the decision set can be dropped from future consideration.

Additionally, it is usually assumed that people act rationally when they compare the costs and benefits of any activity and then decide to engage in that activity if that choice maximizes return relative to cost. This is not a particularly “economic theory”, it is the foundation of most theories in psychology relating to human behavior. It is also important to understand that this comparison is a very subjective analysis. That is to say: the costs and/or the benefits of engaging in any economic activity are likely to be different for any two people.

The Unknown but Bonded approach yields as decision set, but ultimately the Agent has to make a decision. In our case we selected the method known as the Minimum Regret as the tool to arrive to this decision.

Regret, which is strongly related to the cost of opportunity, is said to be the difference between what you got, and the best you could have gotten, given a specified outcome. The Minimum Regret, also known as the MaxiMin (or MiniMax) method, seeks to maximize the profit derived from a decision, while minimizing the adverse consequences of it.

III. ELECTRICITY AND NATURAL GAS IN VENEZUELA

Both natural gas and electric service in Venezuela have been traditionally structured as monopolies with regulated tariffs, generally calculated on a cost-of-service basis [3]. In addition, tariffs have not generally been designed to account for cost or regional differences, and often they include crossed-subsidies between customer classes.

In all likelihood, the ongoing restructuring and deregulation of these sectors will introduce many changes in Venezuela. In a competitive market, electricity and gas prices should be lower than they would be under total government control (in absence of subsidies), although regulation will be always required to “balance”, introduce stability in these markets, and ensure competition. What is certain is that until sector restructuring is fully implemented, both the natural gas and electricity

industries will be in a transition period. During this time, the Regulatory Entities (Comisión Nacional de Energía Eléctrica-CNEE, and Ente Nacional del Gas-ENAGAS) will have the task of setting in place rules and policies that will ultimately produce more competitive and efficient natural gas and electricity industries.

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Competition will most likely be introduced in both the electric power and natural gas sectors, especially in their generation and commercialization segments. Transmission and Distribution (T&D) will remain natural monopolies. The rights to exercise these activities will be given by concession over exclusive areas. As a result, T&D tariffs will remain regulated.

In Venezuela, both Electrificación del Caroní (Edelca), a large hydroelectric company, and PDVSA Gas may exercise market power since they supply a very large percentage of the electric power and natural gas requirements, respectively [4]. Furthermore, there are other situations in the market (monopsony) that can introduce further distortion. This complicates their treatment from a regulatory point of view, since a strong monopoly structure on the production side and a monopsony structure on the consumption side characterize their market. To complicate matters further, it should be noticed that the expectations and the objectives of the three major agents (the Government, the Companies, and the Consumers) are often in conflict.

Both the natural gas pipeline and electric power transmission infrastructures in Venezuela need to be upgraded to improve both service quality and reliability, while being able to meet additional demand. Furthermore, while the electric power transmission system in Venezuela is fully integrated (with some important bottlenecks), the gas transportation network is separated in two isolated islands. Thus, an opportunity for convergence is being offered to the electric power industry and to a deregulated natural gas industry.

IV. OPTIONS, UNCERTAINTIES, AND ATTRIBUTES

The options, uncertainties, and attributes - and specially the scenarios - were thoroughly analyzed with the objective of narrowing down the number of scenarios to a small enough number that: (1) captured the important issues (i.e., produced

useful results), (2) represented a wide range of possibilities, and (3) aided in the decision making process of the Company.

As a result, a total of twelve options were considered for meeting the energy requirements of this Venezuelan Company (Polar). These included both local/regional as well as system wide solutions (the latter exploiting the obvious economies of scale). Furthermore, the possibility of surplus energy sales to other nearby loads in certain regions, as well as in the future Wholesale Electricity Market, was also considered. Options were assembled from specific combinations of the following five energy supply choices (the last four are self-generation options): (i) purchases from the Venezuelan Power Grid, (ii) internal combustion machines (burning diesel), (iii) combustion turbines (burning dual fuels, natural gas and other), (iv) steam turbines (burning liquid or solid fossil fuels), and (v) a combined-cycle power plant (burning natural gas or other fuels). Some of these options required concomitantly strengthening the electrical interconnections from some of the industrial plants (owned by the company) to the underlying transmission and/or distribution systems. In addition, five main uncertainties were identified. They were modeled by means of “unknown but bounded” – rather than probabilistic - representations (i.e., they were modeled as ranging between assumed maximum and minimum expected values). The uncertainties were: (i) the future structure of the electricity industry in Venezuela, (ii) the future structure of the gas industry in Venezuela, (iii) Company’s expected rate of return for investments in self-generation (country risk), (iv) Company’s expected energy demand, and (v) future price and availability of fuels in Venezuela (particularly Orimulsión). The bounds for the first two uncertainties (i.e., the structural ones, which are clearly the most important) were: (1) status quo, and (2) completely open and competitive sectors. Finally, three attributes were selected for evaluating the scenarios: (i) net present value of the cost of energy to the company, (ii) net present value of energy-not-served, and (iii) net present value of capital requirements. The first two were considered to be the most important. As part of the project, we developed a computer model that implements the TOR methodology specifically for this case. Inputs to this model included specific data about generation technologies (e.g., capital and O&M costs), as well as information about the characteristics of the energy sector in Venezuela (e.g., expected cost of electricity). In addition, the model incorporates significant intelligence about the particular characteristics of the system of this particular company. As it will be shown with more detail later, the results showed that – as expected - the best options are widely different for the various possible materializations of the future structure of the electricity and natural gas sectors in Venezuela.

V. TECHNICAL AND REGULATORY EVALUATION

Since the company owns several industries located along Venezuela, it was agreed to define Equivalent Power Plants as the combination of Generation Stations that would meet a

certain percentage (i.e., 25%, 50%, 75% or 100%) of the company energy requirements in each region of the country. Conceptually, grouping general generation sizes and technologies forms Equivalent Power Plants. In every case, the remaining energy not supplied from generating stations comes from the corresponding utility (power grid). For the evaluation of the Equivalent Power Plant and based in historical data, the served load was assumed to have an equivalent power factor of 0.9 and average load factor of 0.70. The key technical assumptions pertaining to project costs, schedules, performance and risk simulations are divided between technical and economic/financial assumptions and they are discussed in this (technical) and the following (economic/financial) section. Different generation technologies were considered, as follows: (I) High Speed Diesel (1800 rpm), (ii) Medium Speed Diesel (approx. 900 rpm), (iii) Medium Speed Diesel (approx. 720 rpm), (iv) Combustion Turbines, (v) Steam Turbines, and (vi) Combined Cycle. Related available technology data included: size, heat rate and availability rates. Construction schedules for different technologies, as well as several generating power plants already installed in different industrial plants, were also considered. The main fuel alternatives were Natural Gas (as described previously), Coal and Orimulsión. Venezuela has coal reserves in the order of 528 million tons, mostly of the bituminous type, being the third larger producer of coal in Latin America, after Colombia and Brazil. The region of Guasare, near the border with Colombia concentrates the biggest production of coal in Venezuela, largely controlled by a state owned company named Carbozulia, a subsidiary of PDVSA. Orimulsión is a liquid fossil fuel produced through a technology developed by a research and development subsidiary of PDVSA, Intevep. Orimulsión is 70% natural bitumen and 30% liquids, plus some preservatives to stabilize the emulsion. It was designed specifically for use in the generation of electricity and the industrial sector. It has been used with success in generating plants in Canada, the United States, the United Kingdom, Japan, Denmark, China, Italy and Lithuania. Venezuela has important reserves of bitumen (estimated at 42 billion metric tons). The fuel-technology alternatives just presented are considered to meet both local and international environmental standards. As previously mentioned, heat rates for the various technologies were considered in the developed model. This would impact the fuel consumption and, therefore, the variable costs. It was modeled as a discrete variable within each generation technology option. Since several plants of the company have steam requirements, steam production with credits for steam was considered to obtain a net heat rate for the generating stations. According to the information available, steam requirements will not be larger than 50 ton/hr at 900 psi. The fuel credits were estimated by:

Fuel Credit = f(Value of Steam) [US$ / lb] x K [lb/MMBTU] This translates into a lower “Net” Heat Rate given by:

Net Heat Rate = [Gross Heat Rate – Fuel Credit]

Most likely, the key uncertainties in this project are of a structural nature. They are related to the electricity and natural gas sectors, about whether or not these would be restructured in the near future. The energy deficit in Venezuela also has structural origins, i.e., a strong dependence on hydropower due to a lack of investment and/or maintenance of the thermal generation plants. Therefore, different degrees of restructuring were considered in the developed model, fully restructured, partially restructured and status quo. While fully restructured and status quo account for the extreme scenarios, the partially restructured scenarios consider that one of the two sectors (gas or electric) completely restructures, while the other remains status quo. With respect to Polar’s demand growth in the next few years, the criterion was to model an extreme range of possibilities for this uncertainty. The selected range considered was between –3% and 10% annually (i.e., pessimistic and optimistic scenarios).

VI. ECONOMIC AND FINANCIAL EVALUATION

Relevant information was gathered for this project from two primary sources, as follows: (i) own database from previous experiences, and (ii) questionnaire and other requests to the several plants of the company. An extensive initial effort was made to gather available information with respect to generation technologies (i.e., typical direct and indirect Capital Expenditures [CapEx], as well as fixed and variable Operation & Maintenance Expenditures [OpEx]), electricity rates and natural gas prices for every sector forecasted scenario, and the financial markets. An estimation of capital costs was performed for the Equivalent Power Plants, using typical international unit costs to determine investment on equipment. In addition, other direct and indirect investments required by the project were estimated based on investment on equipment, as well as fixed and variable O&M costs. Since the capital cost estimation was performed based on historical unit costs applicable at international level, some uncertainty is introduced. This is mainly related to the availability of local related information in Venezuela. However, the developed model is flexible and any updates in the data can be processed within minutes. Transmission costs are included implicitly within the analysis by adding the corresponding tariff within the total tariff for every considered node (region). According to the Venezuelan Electricity Law (1999) and the proposed mechanism for the Wholesale Electricity Market (WEM), the WEM is going to be characterized by two sub-markets, the Spot and the long-term Markets. With regard to projected availability per region, extensive research was done and the data used correspond to either the worst case scenario (status-quo) or the best scenario (fully-restructured). It should be noted that the partially restructured scenarios were defined as follows:

• Electric Power Sector Partially Restructured: electric power sector fully restructured while natural gas sector is still status quo, and

• Natural Gas Sector Partially Restructured: electric power sector still status quo while natural gas sector is fully restructured.

The expected price of both electricity and gas are modeled as uncertainties. No probabilities were assigned since these would add bias from the analysts. Table 1 illustrates all possible fuels available for the generating technologies, with their respective price, in US$/MMBTU. Table 1 complements the fuel data for the cases modeled with uncertainties, i.e., the natural gas and the Orimulsión.

TABLE 1. Fuel Prices Modeled as Fixed Variables

FUEL PRICE

[US$/MMBTU]

Diesel #2 2.52

HFO #6 1.52

Coal 1.25

Operation and Maintenance Costs were divided into two main components, as follows: � Fixed O&M Costs - US$/kW-year. � Variable O&M Costs - US$/MWh. In terms of financing, new power generation plants with a relative stable cash flow are normally financed with a 75% debt/25% equity structure. In this case, a structure of 70% debt/30% equity was selected, considering that this scenario is more feasible and less optimistic. Based on the previous considerations, the project is assumed to be financed as non-recourse financing and there is political risk insurance included. The financial cost is in the order of 10.5%, which is typical for a country such as Venezuela. Corresponding Betas (β’s) were modeled and included into the financial (WACC-CAPM)1 model developed to account for parameters such as country risk and debt structure. All corresponding taxes were also considered.

VII. MODEL

A detailed model was developed for the evaluation of the Equivalent Power Plants for the 2002-2018 time period. The model takes into account all the features, assumptions and data already described in this paper. Historical values for all the specific parameters of the production plants were used as a starting point in the model.

Figure 2 shows the conceptual design of the model. Scenarios are constructed from options and uncertainties, and then characterized in terms of attributes.

1 WACC = Weighted Average Cost of Capital CAPM = Capital Asset Pricing Model

Fig. 2 Strategic Planning.

Options

Scenarios Attributes

Uncertainties

The model was useful in:

1. assessing the feasibility of the Equivalent Power Plants, 2. computing the attributes (from the corresponding options

and uncertainties,) 3. performing the risk analysis from the results obtained,

estimating the regret associated with the possible decisions, and

4. designing hedging strategies to lower the risk exposure.

The model contains a specific designed sub-model for the computation of the reliability indices. It is linked to the rest of the model that contain required information for the calculation of these indices (e.g., files with the results of Monte-Carlo simulations). The energy supply reliability data was entered into a Monte Carlo simulation program to generate all the information needed for the analysis. Figure 3 shows an example of the Failure Probability Distributions considered for the energy supply sources, which basically illustrates the probable number of events that may happen in any particular year if only that source of energy was connected to the plants. For the generating units, a Normal probability distribution function was assumed, while for the Electric Power Grid, a LogNormal distribution was selected. According to our experience, these functions are the ones that represent the best both generating units and power grids.

Another important feature included in the model is the Cost of Energy-Not-Served. These represents the total costs (production, market share, and cost of opportunity, among others) incurred when a power outage occurs with varying duration (i.e., 1, 8 or 24 hours). Due to the company’s specific nature of this data, no comparison with that from similar industries was considered.

VIII. SIMULATION RESULTS, RISK ANALYSIS, AND HEDGING

The results for the Status-Quo scenario are the following:

• The decision set is comprised of two groups of plans. One group is composed of diesel gen-sets and natural gas combustion turbines (this option assumes availability of natural gas) and features lower energy costs (in US$/MWh), while the other comprises diesel gen-sets and steam turbines and features decreased NPV of outage costs (in kUS$).

• The rest of the options are dominated (i.e., are more expensive in terms of energy or unavailability costs) and are therefore not considered further.

Fig. 3.

Failure Probability Distributions for Energy Supply Options.

Electric Power Grid Generating Unit

Similarly, for the Fully Restructured scenario the results are the following:

• The Combined Cycle - Natural Gas (CCNG) generating station is the most reliable option in this scenario.

• The lowest energy costs (in US$/MWh) are associated with the option where the company does not invest in generation, and instead buys 100% of its energy requirements from the power grid.

• The rest of the options are dominated.

From these results it can be concluded that the best options are as follows:

• Restructured Electric Power and Natural Gas Sectors.

Either to buy 100% from the power grid (lowest cost of generation) or supply it from a CCNG generating station (best reliability). In other words, the “extreme” scenarios comprise the decision set.

• Restructured Electric Power Sector and Non-Restructured Natural Gas Sector. Supply 100% of the required energy from a combined-cycle generating station, burning a fuel other than natural gas (normally light fuel oil # 2).

• Non-Restructured Electric Power Sector and Restructured Natural Gas Sector.

Combine purchases from the power grid with “regional” generation stations (diesel gen-sets and natural gas combustion turbines).

• Non-Restructured Electric Power and Natural Gas Sectors. Combine purchases from the power grid with “regional” generation stations (diesel gen-sets and steam turbines). Natural gas is considered not to be available and therefore, other generating fuels must be used.

Risk exposure is done by calculating the impact of the materialization of futures different than those assumed in the selection of certain options. That is, starting from the expected future (scenario) and its corresponding “best option”, it was calculated the impact of a different future materializing.

The “cost of the projects” is defined as the arithmetic sum of the NPV of the energy costs, the cost of energy-not-served

(unavailability), and the capital requirements for all possible scenarios. The relative position of the alternatives on a “expected” cost basis is as follows (100% being the most expensive):

1. Power Grid Only: 100 %

2. Diesel: 75%

3. Diesel + Steam 60%

4. Diesel + Gas 55%

5. CCNG: 45%

From the perspective of regret analysis (regret calculated as the project cost minus the minimum cost of the best possible scenario), obtained results show that the maximum regret for the various options related to the maximum obtained value (which largely exceeds 1,0 US billion) is as follows:

1. Power Grid Only: 100 % . This relatively large exposure is fundamentally due to the possibility of electricity rationing in the country, and the increasingly deteriorated reliability of service from the grid, as discussed earlier in this report.

2. Diesel: 53%,

3. Diesel + Gas and Diesel + Steam: 27% and

4. CCNG: 13%

It can be noted from the results the above that:

(i) The Diesel + Gas, and Diesel + Steam options have a similar range of costs and the later has smaller regret associated to it;

(ii) The CCNG option is clearly the preferred one (i.e., it is the one with the smallest expected cost and minimum regret). However it is only feasible under a future where the electric power sector is fully restructured, and

(iii) Even though the “Power Grid Only” option was found to potentially yield the lowest cost of supply, the risk of interruptions overweighted this consideration and it ended up having the highest expected cost and highest variability (regret).

Finally it was found that the NPV of the capital requirements has little weight compared to the other two attributes of cost of energy and cost of interruption, being this last one the most important.

As the best option is currently non-feasible (it requires the restructuring of the power sector), the recommended course of action was to “time – hedge” that is to wait and see what comes out of the process under development. The cost of this “time-hedge” is exposure to power grid unavailability, which can be mitigated by leasing emergency generating units.

IX. CONCLUSIONS

The trade-off risk analysis methodology proved to be a valuable tool for the strategic evaluation of options available to the company to meet its energy requirements in Venezuela. It aided in the: (i) formulation of scenarios, (ii) analysis of available fuels and generation technologies, (iii) analysis of risks, and (iv) design of hedging strategies. A simulation model based upon this methodology and specific to the company was developed. The model simulates the situation for the company

for the 2002-2018 time frame and combines 32 uncertainties and 12 options to obtain 384 scenarios, and then characterizes these in terms of three (3) attributes: (1) net present value (NPV) of the cost of energy to the company, (2) NPV of energy-not-served, and (iii) NPV of capital outlay requirements.

The results of the analysis showed that – as expected - the best options for the company are widely different for the various possible materializations of the future structure of the electricity and natural gas sectors in Venezuela.

The maximum risk exposure to the company varies widely with the possible materializations of the structural uncertainties (i.e., future structure of the electricity and natural gas sectors in Venezuela).

The above results point to the fact that in order to reduce the risk exposure to acceptable levels, the company will likely need to engage in a significant amount of self-generation in the future. Otherwise (and this means the Power Grid Only option is implemented), the company exposes itself to the risk of significant monetary losses in the future.

X. REFERENCES [1] Schweppe, F.C., H.M. Merrill and W.J. Burke “Least-Cost Planning: Issues

and Methods”, paper published at the Proceedings of the IEEE, Vol. 77, No 6, June 1989.

[2] Merrill H.M. and P.D. Fuller “Playing Leapfrog with a Unicorn: Risk and Uncertainty in Power System Planning”, paper published at the Minnesota Power Systems Conference, St. Paul, MN, October 1992.

{3] Dortolina, C.A. and J.A. Martínez. “Evaluation of Energy Supply Options in Venezuela – Deregulation of the Gas and Electricity Industries”, paper presented at the 2002 CIGRE Conference: Gas and Electricity Networks – Complementarity or Competition?, Brasilia, Brazil, May 2002.

[4] Dortolina, C.A. “Venezuelan Restructured Electricity Market - Analysis of a Dominant Firm’s Market Power” paper presented at the 2001 IEEE PES Summer Power Meeting, Vancouver, BC, Canada.

XI. BIOGRAPHIES Carlos A. Dortolina (SM) received an Electrical Engineering degree from Universidad Simón Bolívar (USB), Caracas, Venezuela, the MSEPE from Rensselaer Polytechnic Institute (RPI), Troy, NY, and the MBA in Economics from Universidad Católica Andrés Bello, Caracas, Venezuela. He is currently a Senior Consultant in the Project Finance Services Practice at Stone & Webster Consultants, Inc.

Hans Fendt (SM) received an Electrical Engineering degree (Cum Laude) and a Graduate degree in Technology Management, both from USB, Caracas, Venezuela, He is currently Corporate Utilities Manager of the Beer and Malt Business Unit in Empresas Polar.

Ramón Nadira received an Electrical Engineering degree (Summa Cum Laude) from USB, Caracas, Venezuela, and the MSc. and the PhD degrees, both from Case Western Research University, Cleveland, Ohio. He is currently Vice-President in the Project Finance Services Practice at Stone & Webster Consultants, Inc.

Nelson Bacalao received an Electrical Engineering degree (Cum Laude) from USB, Caracas, Venezuela, the MSc. from RPI, Troy, NY, the PhD from University of British Columbia, Canada, and the MBA from the Instituto de Estudios Superiores de Administración (IESA), Caracas, Venezuela. He is currently Executive Consultant in the Project Finance Services Practice at Stone & Webster Consultants, Inc.

Jacobo Di Bella (M) received an Electrical Engineering degree and a Graduate degree in Technology Management, both from USB, Caracas, Venezuela, He is currently Corporate Senior Engineer of the Power System Dept. in Empresas Polar.

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ANEXO L – VPN. TIR y Periodo de Repago

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A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 M1 M2 M3 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12

VPN 2,493 2,743 2,369 2,623 (161) (161) (161) (161) (34) (54) (35) (52) (117) (151) (125) (131) (131) (131) (131) (145) (151) (144) (148) (184) (184) (184) (184)TIR 143% 151% 138% 147% #DIV/0! #DIV/0! #DIV/0! #DIV/0! 8% 5% 8% 6% #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!Periodo de Pago 1 1 1 3 17 18 17 18

0 (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19)1 (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56) (56)2 (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112) (112)3 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)4 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)5 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)6 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)7 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)8 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)9 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)

10 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)11 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)12 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)13 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)14 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)15 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)16 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)17 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)18 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)19 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)20 511 559 486 536 (4) (4) (4) (4) 21 17 21 17 5 (2) 3 2 2 2 2 (1) (2) (1) (1) (8) (8) (8) (8)

Plan: Incrementar lacompensación serie 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27VPN 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0TIR #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM! #NUM!Periodo de Pago

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 06 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 07 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 08 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 011 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 012 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 013 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 014 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 015 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 016 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 017 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 018 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 019 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 020 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Nueva Línea DC +Compensación 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27VPN 2,682 2,950 2,500 2,768 (169) (169) (168) (169) (92) (97) (92) (96) (123) (160) (142) (139) (139) (139) (139) (153) (153) (153) (153) (186) (186) (186) (186)TIR 148% 156% 142% 151% #DIV/0! #DIV/0! #DIV/0! #DIV/0! -1% -2% -1% -2% #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!Periodo de Pago 1 1 1 1

0 (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19) (19)1 (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57) (57)2 (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113) (113)3 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)4 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)5 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)6 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)7 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)8 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)9 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)

10 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)11 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)12 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)13 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)14 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)15 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)16 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)17 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)18 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)19 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)20 547 599 512 564 (5) (5) (5) (5) 10 9 10 9 4 (3) 0 1 1 1 1 (2) (2) (2) (2) (8) (8) (8) (8)

Compensación SerieAdicional con Control TCSC 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27VPN (17) (17) (17) (17) (17) (17) (17) (17) 46 27 45 29 (17) (17) (0) (17) (17) (17) (17) (17) (17) (17) (17) (17) (17) (17) (17)TIR #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 48% 35% 47% 36% #DIV/0! #DIV/0! 12% #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!Periodo de Pago 3 3 3 3 2 3 2 3 7

0 (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2)1 (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6)2 (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12) (12)3 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)4 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)5 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)6 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)7 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)8 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)9 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)

10 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)11 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)12 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)13 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)14 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)15 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)16 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)17 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)18 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)19 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)20 (0) (0) (0) (0) (0) (0) (0) (0) 12 8 12 8 (0) (0) 3 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)

Nuevas Líneas AC500 KV + Phase Shifter 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27VPN 2,619 2,888 2,440 2,708 (192) (192) (191) (192) (251) (251) (251) (251) (243) (233) (306) (323) (323) (323) (323) (278) (284) (277) (280) (361) (361) (361) (360)TIR 107% 113% 102% 109% #NUM! #NUM! #NUM! #NUM! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!Periodo de Pago 1 1 1 1

0 (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32)1 (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95) (95)2 (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189) (189)3 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)4 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)5 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)6 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)7 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)8 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)9 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)

10 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)11 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)12 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)13 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)14 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)15 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)16 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)17 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)18 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)19 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)20 554 606 519 571 9 9 9 9 (3) (2) (3) (2) (1) 1 (13) (16) (17) (16) (16) (8) (9) (7) (8) (24) (24) (24) (24)

Futuro

Pág. 134

ANEXO M – Sustento para el Cálculo del Arrepentimiento

Costo Total [USD/MWh] A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 M1 M2 M3 B1 B2 B3 B4 B5Plan: Incrementar la compensación serie 74.58 82.46 66.23 73.82 - - 0.02 0.01 6.66 5.86 8.03 7.39 1.72 0.13 1.13 0.00 0.00 0.01 0.00 - 3ra Nueva Línea AC 220 kV + Compensación 10.86 11.76 8.72 9.39 14.89 13.78 14.93 13.99 8.81 9.17 10.00 10.47 6.34 33.77 8.13 9.92 10.36 9.94 10.36 14.36 MFI/$ 4.72 4.88 4.40 4.57 15.05 13.93 15.09 14.14 12.55 12.12 14.04 13.69 6.29 34.02 9.64 10.03 10.47 10.05 10.47 13.86 Compensación Serie Adicional con Control TCSC 75.20 83.10 66.86 74.46 1.53 1.40 1.58 1.45 3.58 3.78 4.67 5.02 2.44 3.65 0.88 1.04 1.08 1.04 1.08 1.41 Nuevas Líneas AC 500 KV + Phase Shifter 7.58 7.89 7.19 7.50 25.13 23.26 25.19 23.59 18.73 18.01 20.51 19.74 10.51 56.75 16.26 16.76 17.52 16.80 17.51 24.22 Mínimo 4.72 4.88 4.40 4.57 - - 0.02 0.01 3.58 3.78 4.67 5.02 1.72 0.13 0.88 0.00 0.00 0.01 0.00 -

ArrepentimientoPlan: Incrementar la compensación serie 69.86 77.57 61.83 69.25 - - - - 3.08 2.08 3.35 2.37 - - 0.25 - - - - - 3ra Nueva Línea AC 220 kV + Compensación 6.15 6.88 4.32 4.83 14.89 13.78 14.91 13.99 5.23 5.39 5.32 5.45 4.62 33.65 7.25 9.92 10.36 9.93 10.35 14.36 MFI/$ - - - - 15.05 13.93 15.07 14.14 8.98 8.34 9.37 8.67 4.58 33.89 8.76 10.03 10.47 10.05 10.47 13.86 Compensación Serie Adicional con Control TCSC 70.49 78.21 62.46 69.89 1.53 1.40 1.55 1.45 - - - - 0.73 3.52 - 1.03 1.08 1.04 1.08 1.41 Nuevas Líneas AC 500 KV + Phase Shifter 2.86 3.00 2.79 2.93 25.13 23.26 25.16 23.58 15.15 14.23 15.83 14.72 8.79 56.62 15.38 16.76 17.52 16.79 17.51 24.22

Alta Baja MediaPlan: Incrementar la compensación serie 77.57 77.57 - 0.25 3ra Nueva Línea AC 220 kV + Compensación 33.65 14.91 20.70 33.65 MFI/$ 33.89 15.07 20.94 33.89 Compensación Serie Adicional con Control TCSC 78.21 78.21 2.16 3.52 Nuevas Líneas AC 500 KV + Phase Shifter 56.62 25.16 34.95 56.62

Costo Marginal (Total) [USD/MWh] A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 M1 M2 M3 B1 B2 B3 B4 B5Plan: Incrementar la compensación serie 1,040.81 1,211.53 1,036.52 1,233.09 - - - - 29.19 17.86 41.96 32.62 28.13 - 14.48 - - - - - 3ra Nueva Línea AC 220 kV + Compensación 114.96 119.19 97.32 100.58 14.88 13.80 14.93 13.97 34.13 24.86 41.71 34.42 10.91 33.81 16.31 9.91 10.36 9.94 10.36 14.16 MFI/$ 18.77 19.54 13.79 15.39 15.05 13.95 15.10 14.12 36.22 25.06 47.79 38.62 6.29 34.02 21.89 10.03 10.47 10.05 10.47 13.86 Compensación Serie Adicional con Control TCSC 1,041.44 1,212.17 1,037.15 1,233.73 1.55 1.44 1.55 1.46 29.60 19.40 37.15 29.06 28.86 3.52 9.52 1.03 1.08 1.04 1.08 1.43 Nuevas Líneas AC 500 KV + Phase Shifter 24.73 25.83 18.64 20.76 25.11 23.28 25.23 23.58 41.11 29.88 54.47 45.00 10.51 56.78 29.75 18.44 20.17 18.06 18.83 24.00 Mínimo 18.77 19.54 13.79 15.39 - - - - 29.19 17.86 37.15 29.06 6.29 - 9.52 - - - - -

Arrepentimiento Alta Baja MediaPlan: Incrementar la compensación serie 1,022.04 1,191.99 1,022.74 1,217.69 - - - - - - 4.82 3.55 21.83 - 4.96 - - - - - 3ra Nueva Línea AC 220 kV + Compensación 96.20 99.66 83.53 85.18 14.88 13.80 14.93 13.97 4.95 7.00 4.56 5.36 4.62 33.81 6.79 9.91 10.36 9.94 10.36 14.16 MFI/$ - - - - 15.05 13.95 15.10 14.12 7.03 7.20 10.65 9.56 - 34.02 12.37 10.03 10.47 10.05 10.47 13.86 Compensación Serie Adicional con Control TCSC 1,022.67 1,192.63 1,023.36 1,218.33 1.55 1.44 1.55 1.46 0.41 1.54 - - 22.56 3.52 - 1.03 1.08 1.04 1.08 1.43 Nuevas Líneas AC 500 KV + Phase Shifter 5.96 6.30 4.86 5.37 25.11 23.28 25.23 23.58 11.92 12.02 17.32 15.94 4.21 56.78 20.23 18.44 20.17 18.06 18.83 24.00

Plan: Incrementar la compensación serie 1,217.69 1,217.69 - 21.83 3ra Nueva Línea AC 220 kV + Compensación 99.66 99.66 20.72 33.81 MFI/$ 34.02 15.10 20.95 34.02 Compensación Serie Adicional con Control TCSC 1,218.33 1,218.33 2.16 22.56 Nuevas Líneas AC 500 KV + Phase Shifter 56.78 25.23 34.98 56.78

HDN A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 M1 M2 M3 B1 B2 B3 B4 B5Plan: Incrementar la compensación serie 8,761.25 8,620.90 8,761.25 8,629.03 640.50 465.50 553.69 541.50 8,761.25 8,273.41 8,470.55 7,624.91 4,711.23 - 3,792.53 - - - - - 3ra Nueva Línea AC 220 kV + Compensación 6,659.84 6,243.15 6,659.84 6,307.36 21.39 - 19.74 - 8,547.98 7,351.30 7,755.98 7,028.88 793.37 - 2,086.43 - - - - - MFI/$ 3,701.08 3,955.00 3,701.08 3,955.00 15.11 - 13.95 - 8,761.25 8,079.13 8,333.75 7,506.63 - - 3,435.13 - - - - - Compensación Serie Adicional con Control TCSC 8,761.25 8,620.90 8,761.25 8,629.03 640.50 465.50 553.69 541.50 8,654.61 7,411.65 7,831.11 7,082.94 4,711.23 - 2,202.34 - - - - - Nuevas Líneas AC 500 KV + Phase Shifter 4,443.93 4,748.83 4,443.93 4,748.83 - - - - 8,761.25 8,297.69 8,487.65 7,639.69 - - 3,837.20 205.23 243.48 183.28 256.16 - Mínimo 3,701.08 3,955.00 3,701.08 3,955.00 - - - - 8,547.98 7,351.30 7,755.98 7,028.88 - - 2,086.43 - - - - -

ArrepentimientoPlan: Incrementar la compensación serie 5,060.18 4,665.90 5,060.18 4,674.03 640.50 465.50 553.69 541.50 213.28 922.11 714.57 596.03 4,711.23 - 1,706.10 - - - - - 3ra Nueva Línea AC 220 kV + Compensación 2,958.77 2,288.15 2,958.77 2,352.36 21.39 - 19.74 - - - - - 793.37 - - - - - - - MFI/$ - - - - 15.11 - 13.95 - 213.28 727.83 577.78 477.75 - - 1,348.70 - - - - - Compensación Serie Adicional con Control TCSC 5,060.18 4,665.90 5,060.18 4,674.03 640.50 465.50 553.69 541.50 106.64 60.35 75.14 54.06 4,711.23 - 115.91 - - - - - Nuevas Líneas AC 500 KV + Phase Shifter 742.86 793.83 742.86 793.83 - - - - 213.28 946.39 731.67 610.82 - - 1,750.78 205.23 243.48 183.28 256.16 -

Alta Baja MediaPlan: Incrementar la compensación serie 5,060.18 5,060.18 - 4,711.23 3ra Nueva Línea AC 220 kV + Compensación 2,958.77 2,958.77 201.72 793.37 MFI/$ 1,348.70 727.83 - 1,348.70 Compensación Serie Adicional con Control TCSC 5,060.18 5,060.18 - 4,711.23 Nuevas Líneas AC 500 KV + Phase Shifter 1,750.78 946.39 256.16 1,750.78

MFI A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 M1 M2 M3 B1 B2 B3 B4 B5Plan: Incrementar la compensación serie 6,704.06 6,604.70 6,701.64 6,603.41 80.81 54.24 82.72 42.76 5,695.52 5,496.90 5,698.64 5,390.55 1,148.05 17.35 741.90 (0.70) (2.09) 1.57 3.17 (3.04) 3ra Nueva Línea AC 220 kV + Compensación 3,102.44 3,252.45 3,110.29 3,255.95 2.83 11.65 10.67 (5.21) 4,323.11 4,257.03 4,270.75 4,160.58 114.33 (4.15) 97.32 0.54 (2.26) (1.16) 7.81 (95.91) MFI/$ 1,292.35 1,523.79 1,294.04 1,528.30 0.58 11.50 12.78 (3.62) 5,402.03 5,231.59 5,391.23 5,126.90 2.06 11.97 588.15 1.49 (0.69) (0.61) 4.25 (9.97) Compensación Serie Adicional con Control TCSC 6,704.06 6,604.70 6,701.64 6,603.41 80.81 54.24 82.72 42.76 4,404.00 4,329.76 4,350.65 4,231.78 1,148.05 17.35 102.50 (0.70) (2.09) 1.57 3.17 (3.04) Nuevas Líneas AC 500 KV + Phase Shifter 1,551.75 1,829.64 1,553.78 1,835.05 (4.55) 10.48 16.80 0.21 5,732.21 5,530.06 5,737.07 5,423.51 2.48 15.74 761.12 11.41 11.49 13.32 16.80 (106.52) Mínimo 1,292.35 1,523.79 1,294.04 1,528.30 (4.55) 10.48 10.67 (5.21) 4,323.11 4,257.03 4,270.75 4,160.58 2.06 (4.15) 97.32 (0.70) (2.26) (1.16) 3.17 (106.52)

ArrepentimientoPlan: Incrementar la compensación serie 5,411.70 5,080.91 5,407.59 5,075.11 85.36 43.76 72.04 47.97 1,372.41 1,239.87 1,427.89 1,229.97 1,145.99 21.50 644.58 - 0.17 2.73 - 103.48 3ra Nueva Línea AC 220 kV + Compensación 1,810.08 1,728.66 1,816.24 1,727.65 7.38 1.17 - - - - - - 112.27 - - 1.24 - - 4.64 10.60 MFI/$ - - - - 5.13 1.02 2.11 1.59 1,078.92 974.56 1,120.48 966.31 - 16.12 490.82 2.19 1.57 0.55 1.08 96.55 Compensación Serie Adicional con Control TCSC 5,411.70 5,080.91 5,407.59 5,075.11 85.36 43.76 72.04 47.97 80.88 72.74 79.90 71.20 1,145.99 21.50 5.17 - 0.17 2.73 - 103.48 Nuevas Líneas AC 500 KV + Phase Shifter 259.39 305.85 259.73 306.75 - - 6.12 5.42 1,409.09 1,273.03 1,466.32 1,262.92 0.41 19.89 663.80 12.11 13.75 14.48 13.63 -

Alta Baja MediaPlan: Incrementar la compensación serie 5,411.70 5,411.70 548.99 1,145.99 3ra Nueva Línea AC 220 kV + Compensación 1,816.24 1,816.24 56.26 112.27 MFI/$ 1,120.48 1,120.48 512.22 490.82 Compensación Serie Adicional con Control TCSC 5,411.70 5,411.70 551.90 1,145.99 Nuevas Líneas AC 500 KV + Phase Shifter 1,466.32 1,466.32 14.48 663.80

Costo Marginal (Sur) [USD/MWh] A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 M1 M2 M3 B1 B2 B3 B4 B5Plan: Incrementar la compensación serie 1,080.31 1,254.63 1,060.46 1,256.98 2.51 2.51 1.91 1.08 51.91 39.95 56.89 44.89 31.38 1.26 22.98 (0.06) (0.01) (0.03) (0.01) (0.11)

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3ra Nueva Línea AC 220 kV + Compensación 814.94 831.62 663.05 664.62 26.84 26.61 26.26 26.01 206.61 157.05 213.18 175.20 51.20 27.40 56.72 26.59 26.53 26.54 26.53 12.06 MFI/$ 26.02 29.12 18.09 20.09 15.19 14.03 14.92 13.79 56.41 44.53 60.77 49.55 6.36 34.05 28.45 9.99 10.45 10.02 10.47 13.20 Compensación Serie Adicional con Control TCSC 1,080.94 1,255.27 1,061.08 1,257.62 4.06 3.95 3.46 2.54 42.45 31.24 44.67 36.03 32.11 4.78 10.52 0.97 1.07 1.01 1.07 1.32 Nuevas Líneas AC 500 KV + Phase Shifter 33.57 37.57 23.89 26.52 25.27 23.51 25.08 23.22 64.17 52.31 69.65 57.45 10.59 56.44 38.50 18.76 20.11 18.05 19.15 15.35 Mínimo 26.02 29.12 18.09 20.09 2.51 2.51 1.91 1.08 42.45 31.24 44.67 36.03 6.36 1.26 10.52 (0.06) (0.01) (0.03) (0.01) (0.11)

ArrepentimientoPlan: Incrementar la compensación serie 1,054.29 1,225.51 1,042.37 1,236.88 - - - - 9.46 8.71 12.22 8.86 25.01 - 12.46 - - - - - 3ra Nueva Línea AC 220 kV + Compensación 788.93 802.49 644.97 644.53 24.33 24.11 24.35 24.93 164.15 125.81 168.51 139.17 44.84 26.14 46.20 26.65 26.55 26.57 26.54 12.16 MFI/$ - - - - 12.69 11.52 13.01 12.72 13.96 13.29 16.11 13.52 - 32.79 17.93 10.05 10.47 10.05 10.48 13.31 Compensación Serie Adicional con Control TCSC 1,054.92 1,226.15 1,043.00 1,237.52 1.55 1.44 1.55 1.46 - - - - 25.74 3.52 - 1.03 1.08 1.04 1.08 1.43 Nuevas Líneas AC 500 KV + Phase Shifter 7.55 8.45 5.81 6.42 22.76 21.00 23.17 22.15 21.71 21.06 24.98 21.42 4.23 55.18 27.98 18.82 20.13 18.08 19.16 15.46

Alta Baja MediaPlan: Incrementar la compensación serie 1,236.88 1,236.88 12.02 25.01 3ra Nueva Línea AC 220 kV + Compensación 802.49 802.49 27.95 46.20 MFI/$ 32.79 16.11 24.94 32.79 Compensación Serie Adicional con Control TCSC 1,237.52 1,237.52 13.62 25.74 Nuevas Líneas AC 500 KV + Phase Shifter 55.18 24.98 34.74 55.18

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ArrepentimientoPlan: Incrementar la compensación serie3ra Nueva Línea AC 220 kV + CompensaciónMFI/$Compensación Serie Adicional con Control TCSCNuevas Líneas AC 500 KV + Phase Shifter

Plan: Incrementar la compensación serie3ra Nueva Línea AC 220 kV + CompensaciónMFI/$Compensación Serie Adicional con Control TCSCNuevas Líneas AC 500 KV + Phase Shifter

Costo Marginal (Total) [USD/MWh]Plan: Incrementar la compensación serie3ra Nueva Línea AC 220 kV + CompensaciónMFI/$Compensación Serie Adicional con Control TCSCNuevas Líneas AC 500 KV + Phase ShifterMínimo



HDNPlan: Incrementar la compensación serie3ra Nueva Línea AC 220 kV + CompensaciónMFI/$Compensación Serie Adicional con Control TCSCNuevas Líneas AC 500 KV + Phase ShifterMínimo



B6 B7 B8 B9 B10 B11 B12 Mínimo- 0.01 (0.00) 0.00 0.01 0.01 0.02 (0.00)

17.19 14.38 16.80 17.62 20.02 18.56 20.72 6.34 14.44 13.89 14.41 17.80 20.31 18.73 20.96 4.40 1.44 1.44 1.47 1.84 2.10 1.93 2.18 0.88

29.04 24.32 28.55 29.72 33.96 31.27 34.97 7.19 - 0.01 (0.00) 0.00 0.01 0.01 0.02

Solo A Solo M Solo BMáximo Máximo Máximo Máximo Promedio Mínimo

- - - - - - - 77.57 77.57 0.25 - 10.73 - 17.19 14.38 16.80 17.62 20.01 18.56 20.70 33.65 14.91 33.65 20.70 12.10 4.32 14.44 13.89 14.41 17.80 20.30 18.73 20.94 33.89 15.07 33.89 20.94 11.71 - 1.44 1.43 1.47 1.84 2.09 1.92 2.16 78.21 78.21 3.52 2.16 11.45 -

29.04 24.32 28.56 29.72 33.95 31.26 34.95 56.62 25.16 56.62 34.95 20.52 2.79

B6 B7 B8 B9 B10 B11 B12 Mínimo- - - - - - - -

16.32 14.26 16.26 17.62 20.02 18.57 20.72 9.91 14.41 13.88 14.38 17.80 20.29 18.73 20.95 6.29

- 1.43 - 1.84 2.09 1.93 2.16 - 28.06 24.18 27.95 29.72 33.94 31.26 34.98 10.51

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Máximo Máximo Máximo Máximo Promedio Mínimo- - - - - - - 1,217.69 1,217.69 21.83 - 166.28 -

16.32 14.26 16.26 17.62 20.02 18.57 20.72 99.66 99.66 33.81 20.72 24.73 4.56 14.41 13.88 14.38 17.80 20.29 18.73 20.95 34.02 15.10 34.02 20.95 11.64 -

- 1.43 - 1.84 2.09 1.93 2.16 1,218.33 1,218.33 22.56 2.16 166.89 - 28.06 24.18 27.95 29.72 33.94 31.26 34.98 56.78 25.23 56.78 34.98 21.03 4.21

B6 B7 B8 B9 B10 B11 B12 Mínimo- - - - - - - - - - - 201.72 50.43 201.72 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -


- - - - - - - 5,060.18 5,060.18 4,711.23 - 1,130.55 - - - - 201.72 50.43 201.72 - 2,958.77 2,958.77 793.37 201.72 438.76 - - - - - - - - 1,348.70 727.83 1,348.70 - 124.98 - - - - - - - - 5,060.18 5,060.18 4,711.23 - 992.03 - - - - - - - - 1,750.78 946.39 1,750.78 256.16 304.24 -

MFIPlan: Incrementar la compensación serie3ra Nueva Línea AC 220 kV + CompensaciónMFI/$Compensación Serie Adicional con Control TCSCNuevas Líneas AC 500 KV + Phase ShifterMínimo



Costo Marginal (Sur) [USD/MWh]Plan: Incrementar la compensación serie

B6 B7 B8 B9 B10 B11 B12 Mínimo(54.74) 0.09 (55.81) 0.34 (5.05) 2.84 0.49 (55.81)

(547.47) (122.11) (536.38) 4.46 (5.11) 10.03 (0.19) (547.47) (91.51) (9.03) (91.67) 0.23 (2.23) 1.50 0.60 (91.67) (51.83) 0.39 (53.11) 0.34 (5.05) 2.84 0.49 (53.11)

(603.73) (136.06) (591.25) 0.12 0.92 0.01 0.73 (603.73) (603.73) (136.06) (591.25) 0.12 (5.11) 0.01 (0.19)


548.99 136.15 535.44 0.22 0.06 2.83 0.68 5,411.70 5,411.70 1,145.99 548.99 1,097.68 - 56.26 13.95 54.87 4.34 - 10.02 - 1,816.24 1,816.24 112.27 56.26 272.57 -

512.22 127.03 499.58 0.12 2.88 1.49 0.79 1,120.48 1,120.48 490.82 512.22 218.63 - 551.90 136.45 538.14 0.22 0.06 2.83 0.68 5,411.70 5,411.70 1,145.99 551.90 890.31 -

- - - - 6.02 - 0.92 1,466.32 1,466.32 663.80 14.48 270.36 -

B6 B7 B8 B9 B10 B11 B12 Mínimo(2.52) (0.01) (1.63) (0.27) 0.10 (0.20) 0.16 (2.52)

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(14.53) 16.65 0.57 27.54 26.71 27.75 26.87 (14.53) 10.41 13.48 11.81 17.71 20.33 18.62 20.99 6.36 (0.92) 1.39 (0.07) 1.57 2.19 1.73 2.32 (0.92) (0.25) 18.22 10.11 29.82 33.90 31.25 34.89 (0.25)

(14.53) (0.01) (1.63) (0.27) 0.10 (0.20) 0.16 Solo A Solo M Solo B

Máximo Máximo Máximo Máximo Promedio Mínimo12.02 - - - - - - 1,236.88 1,236.88 25.01 12.02 172.14 -

- 16.66 2.20 27.81 26.62 27.95 26.71 802.49 802.49 46.20 27.95 145.92 - 24.94 13.49 13.45 17.98 20.23 18.82 20.84 32.79 16.11 32.79 24.94 12.65 - 13.62 1.40 1.56 1.84 2.09 1.93 2.16 1,237.52 1,237.52 25.74 13.62 171.37 - 14.29 18.23 11.74 30.09 33.80 31.45 34.74 55.18 24.98 55.18 34.74 20.74 4.23

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Pág. 135

ANEXO N – Análisis Adicional - Aumento del Límite por Estabilidad de Tensión

Pág. 1

INTRODUCION En algunos de loa casos presentados en este informe los límites de transferencia están

dictados por límites de estabilidad de tensión. Dado que estos límites en principio podrían ser

superados mediante el uso de capacitores en el extremo receptor, decidimos evaluar el

impacto de ignorar estos límites y considerar únicamente los límites térmicos y de estabilidad.

Efecto de Ignorar los Limites por Estabilidad de Tensión en Incremento del pago de la demanda y el costo total de la opción

La figura de abajo muestra los resultados para el incremento del pago de la demanda y el

costo total de la opción para el caso en el cual se consideran los límites de tensión. En dicha

figura se muestran los resultados correspondientes a cada opción y futuro de generación en

grupos de a cuatro (2 hidrologías y dos costos de combustibles) Figura 1

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da [U

SD/M

Wh]


Pág. 2

A continuación mostramos los cambios en la ubicación de las diferentes opciones cuando se

ignoran los límites correspondientes a estabilidad de tensión (ver figura 2.) Como puede ser

observado únicamente los casos correspondientes a la tercera línea a 220 kV y la línea a 500

kV se desplazan en el diagrama fundamentalmente por reducirse los pagos de la demanda en

el futuro Norte Solo y Alta Demanda.

Figura 2

0

10

20

30

40

50

60

70

80

90

100

110

120

- 2 4 6 8 10 12 14 16 18 20 22 24 26


VPN

del

Pag

o de

la D

eman

da [U

SD/M

Wh]


0

10

20

30

40

50

60

70

80

90

100

110

120

- 2 4 6 8 10 12 14 16 18 20 22 24 26


VPN

del

Pag

o de

la D

eman

da [U

SD/M

Wh]


El cambio en los resultados de la línea a 500 kV no es suficiente para cambiar su posición relativa con respecto de la línea HVDC en el futuro Norte Solo, por lo que aún ignorando el efecto de lis límites por estabilidad de tensión HVDC sigue siendo la opción preferida. Con respecto de la tercera línea a 220 kV los resultados pasan a ser muy similares a los de la línea HVDC para el futuro Norte Solo y Alta Demanda, sin embargo considerando que estas dos opciones tienen costos similares, aún sin incluir los costos de la compensación adicional, podemos considerar que al incluir dichos costos HVDC continuará siendo la opción preferida bajo este escenario.

Pág. 3

Efectos en el Arrepentimiento

El arrepentimiento de las opciones con altos límites de transmisión (HVDV, 3ª línea a 220 kV y

línea a 500 kV.) esta dado por el no utilizar su capacidad durante los casos de baja demanda o

por la instalación de generación en el sur. En este caso el ignorar los límites impuestos por la

estabilidad de voltaje es nulo, ya que todo lo que se estaría haciendo es incrementar una

capacidad de transmisión que no está siendo utilizada.

Conclusión

Con base en los resultados anteriores se concluye que el ignorar los límites impuestos por

estabilidad de tensión en el análisis no altera el orden de preferencia de las opciones.

Pág. 136

ANEXO O – Resultados para los Criterios HDN y MFI

ANEXO O – Resultados para los Criterios HDN y MFI Este anexo muestra los resultados obtenidos durante este Estudio por el Grupo Consultor

para los criterios definidos en el estudio anterior (desarrollado por Siemens PTI-Quantum).

Esos criterios son las Horas de Despacho No Económico (HDN), medidas en horas/MMUS$

y los MWh de Flujo Interrumpido (MFI), medido en kWh/US$.

La tabla O-1 muestra los resultados para todos los planes, mientras que la Figura O-1

muestra el panorama de aquellos escenarios para facilitar la visualización de cuales cumplen

con los criterios (HDN de al menos 100 horas/MMUS$, y MFI de al menos 15 kWh/US$).

De la figura O-1 puede verse que sólo algunos escenarios del Plan 1 (Compensación Serie

únicamente) cumplen con ambos criterios. Es por esta razón que se recomienda

implementar este Plan de manera inmediata. Con relación a los planes que no cumplen con

ninguno de los criterios (como es el caso del Plan 3 – Nueva Línea HVDC + Compensación

Serie) debe destacarse que sus evidentes beneficios han sido expuestos durante el cuerpo

de este documento, y los mismos incluyen otras consideraciones adicionales a los criterios

HDN y MFI, como lo son la reducción en el costo del despacho, del pago de la demanda,

costos de operación y mantenimiento, de energía no servida y de pérdidas.

Tabla O-1. Valores Obtenidos para HDN (horas/MMUS$) y MFI (en kWh/US$)

A1A2

A3A4

A5A6

A7A8

A9A1

0A1

1A1

2M1

M2M3

B1B2

B3B4

B5B6

B7B8

B9B1

0B1

1B1

2

Dema

nda

Alta

Alta

Alta

Alta

Alta

Alta

Alta

Alta

Alta

Alta

Alta

Alta

Media

Media

Media

Baja

Baja

Baja

Baja

Baja

Baja

Baja

Baja

Baja

Baja

Baja

Baja

Ofer

taNo

rte S

oloNo

rte S

oloNo

rte S

oloNo

rte S

oloHi

dro S

ur E

steHi

dro S

ur Es

teHi

dro S

ur E

steHi

dro S

ur E

steTe

rmico

Term

icoTe

rmico

Term

icoNo

rte S

oloHi

dro S

ur E

steTe

rmico

Norte

Solo

Norte

Solo

Norte

Solo

Norte

Solo

Hidr

o Sur

Este

Hidr

o Sur

Este

Hidr

o Sur

Este

Hidr

o Sur

Este

Term

icoTe

rmico

Term

icoTe

rmico

Comp

esac

ion S

erie

HDN

Hora

s/MM$

0.00

28.54

0.00

27.14

270.0

749

7.72

240.9

446

3.90

0.00

154.9

210

9.09

94.32

272.3

10.0

028

5.00

434.5

451

5.53

388.0

854

2.38

0.00

0.00

0.00

0.00

67.34

16.84

67.34

0.00

MFI k

Wh/$

109.4

310

0.75

109.3

910

0.90

39.51

49.53

40.45

51.89

56.59

49.85

59.87

49.28

67.07

-0.17

28.80

1.65

2.61

1.35

2.69

6.19

23.29

7.32

23.08

0.24

-0.23

0.42

0.03

3a Li

nea

HDN

Hora

s/MM$

10.92

13.22

10.92

12.88

11.36

17.42

10.04

16.79

1.11

9.46

7.00

5.94

28.56

0.00

17.45

13.10

15.54

11.70

16.35

0.00

0.00

0.00

0.00

0.98

0.25

0.98

0.00

MFI k

Wh/$

7.84

7.24

7.83

7.24

1.19

1.49

1.22

1.57

3.43

3.03

3.62

2.99

3.17

0.00

1.65

0.05

0.08

0.04

0.08

0.01

0.03

0.01

0.03

0.00

0.00

0.01

0.00

Nuev

a Line

a DC

mas c

ompe

nsac

ion

HDN

Hora

s/MM$

26.01

24.84

26.01

24.84

11.27

17.23

9.96

16.61

0.00

5.62

3.96

3.42

32.34

0.00

10.33

12.96

15.37

11.57

16.17

0.00

0.00

0.00

0.00

2.01

0.50

2.01

0.00

MFI k

Wh/$

9.94

9.19

9.94

9.19

1.18

1.48

1.20

1.55

2.05

1.81

2.17

1.79

3.20

-0.01

1.04

0.05

0.08

0.04

0.08

0.17

0.64

0.20

0.64

0.01

-0.01

0.01

0.00

Comp

ensa

ción S

erie

Adici

onal

con C

ontro

l TCS

C

HDN

Hora

s/MM$

0.00

6.60

0.00

6.27

62.41

115.0

155

.6810

7.20

4.25

70.13

50.68

43.39

62.92

0.00

129.2

110

0.41

119.1

389

.6812

5.33

0.00

0.00

0.00

0.00

15.56

3.89

15.56

0.00

MFI k

Wh/$

25.29

23.28

25.28

23.32

9.13

11.45

9.35

11.99

25.53

22.50

26.96

22.23

15.50

-0.04

12.63

0.38

0.60

0.31

0.62

1.43

5.42

1.69

5.37

0.06

-0.05

0.10

0.01

Nuev

as Lí

neas

AC

500 K

V + P

hase

Shif

ter

HDN

Hora

s/MM$

13.45

12.58

13.45

12.58

6.88

10.44

6.08

10.07

0.00

2.72

1.92

1.66

19.60

0.00

5.01

7.21

8.56

6.44

9.00

0.00

0.00

0.00

0.00

0.52

0.13

0.52

0.00

MFI k

Wh/$

5.85

5.37

5.85

5.36

0.72

0.90

0.73

0.94

0.99

0.88

1.05

0.87

1.94

-0.01

0.51

0.03

0.04

0.02

0.04

0.01

0.03

0.01

0.03

0.00

0.00

0.00

0.00

Figura O-1. HDN vs MFI

0

10

20

30

40

50

60

70

80

90

100

110

120

0 100 200 300 400 500 600

HDN h/MM$

MFI

kW

h/$.

-- Plan 1Plan 2Plan 3Plan 4Plan 5

Pág. 137

ANEXO P – Límites Térmicos Transitorios Mantaro-Socabaya

INTRODUCCIÓN En el anexo F se demostró que después de la entrada en servicio del enlace Cotaruse- Machupicchu el enlace Mantaro - Socabaya estará limitado por consideraciones térmicas ya que los límites de estabilidad angular y tensión son superiores al límite térmico (505 MVA) En consecuencia es conveniente investigar la posibilidad de cargar transitoriamente estas líneas y de dar tiempo después de la primera contingencia (N-1) de hacer un redespacho. Utilizando este límite térmico transitorio sería posible operar mas cercano al límite por estabilidad en condiciones N-0.

1-Limites de Operación de un conductor de aluminio. Un conductor de aluminio puede operar por una hora sin afectar sus características mecánicas como se muestra en la figura de abajo.

Dado lo anterior es práctica común el tomar este límite para operación en emergencia (N-1.) Esto da tiempo para un redespacho.:

2 - Limites Transitorios de la Línea Mantaro - Socabaya . Utilizamos un método de tanteo para determinar los límites de emergencia de un conductor Starling para diferentes temperaturas ambientales las cuales se muestran a continuación:

Temperatura Ambiente

Capacidad Normal (70 C)Amp.

Capacidad Emergencia

(100 C)Amp.

40 C 539.7 85030 C 663.4 92020 C 765.7 99010 C 854.5 1060


Capacidad Normal (70 C)MVA


(100 C)MVA

40 C 411 64830 C 505 70120 C 584 75410 C 651 808

Conductor Starling

220 kV 2x Starling

A manera de referencia la figura a continuación muestra los resultados de temperatura del conductor con 920 amperes y 30 °C de temperatura ambiental.

Transient Thermal Rating (AmbTemp 30C) At t=0+ conductor current steps from 663.4 A to 920 A

0

20

40

60

80

100

120

0 20 40 60 80 100 120Time (minutes)

Con

d Te

mp

( deg

rees

Con base en estos límites calculamos los límites transitorios de las líneas Mantaro – Socabaya:


Capacidad Normal (70 C)Amp.


(100 C)Amp.

40 C 539.7 85030 C 663.4 92020 C 765.7 99010 C 854.5 1060


Capacidad Normal (70 C)MVA


(100 C)MVA

40 C 411 64830 C 505 70120 C 584 75410 C 651 808

Conductor Starling

220 kV 2x Starling

Como se observa sería factible operar la línea Mantaro – Socabaya .con un flujo de 700 MVA por corto plazo sin dañar el conductor (30° C). Sin embargo, es necesario verificar las distancias a tierra bajo esta condición en los diferentes vanos de la línea.

Documents

ANEXO J – Simulaciones Dinámicas...CONTINGENCY: DOUBLE_FAULT_2010_CV TUE, AUG 21 2007 15:27 TIME (SECONDS) SIEMENS POWER TECHNOLOGIES INTERNATIONAL R 0.0 2.0000 4.0000 6.0000 8.0000