Options for real options Dealing with uncertainty in investment decisions for electricity networks

Transcription

1 Options for real options Dealing with uncertainty in investment decisions for electricity networks Ype C. Wijnia Risk Manager of Asset Management Essent Netwerk Den Bosch, The Netherlands Abstract - Part of the liberalization effort in the Netherlands was to regulate the income of the network companies. Although the reduction in income was not as dramatic as in the United Kingdom, it still forced the network companies to review their expenditures. Complicating the challenge was the silent requirement that the cost reduction should not result in more risk. This required a change in the valuation methods. So far these valuation methods have changed from purely technical criteria into a more risk based, net present value alike approach. The real option theory is not commonly used in the decisions. This seems a bit odd, as major uncertainties influence the valuation process. In this paper a few options in which the real option theory might be valuable are presented. It is an invitation to the real option specialists to show the network companies how real options could be of value. Keywords: Electricity Grid, Investment, Decision- Making, Real Options, Uncertainty. 1 Introduction According to the European regulations the electricity markets have to be liberalized. In this system competition is supposed to drive down costs and improve service levels. This competition presumably will not work for the networks, because the huge investments needed to build a competing network can never be recovered by gains in operating efficiency. Networks are `natural monopolies`. Because real competition is not feasible, regulators simulate competition by posing income cuts on the network companies. These cuts can be substantial. In the United Kingdom the average income cut for the network companies in the year april/march 1 was 1,%. The total average income cut over the period was more than 5%[1].The level of the income cut is determined by the efficiency improvement of the best player. In this situation it pays to be the most efficient network operator. Reacting to this incentive the network companies started to rationalize their expenditure patterns Paulien M. Herder Faculty of Technology, Policy and Management Delft University of Technology Delft, The Netherlands P.M.Herder@TBM.TUDelft.nl for the network. This is called Asset management by most companies. For asset management different definitions exist, although the differences in work practices are almost negligible. All Asset managers try to postpone investment, reduce operating cost and improve the performance of the network. To realize those seemingly conflicting targets at the same time two approaches can be followed regarding the investment opportunities 1 : 1. Optimize the individual opportunity. In this approach investment proposals are scanned for unnecessary or contra-productive activities. For example, Preventative Maintenance is based on the bathtub curve, according to which components have a high failure rate right after their start up (infant mortality) or after a certain period (wear out). However, while 7% of components suffer from infant mortality, only 11% shows wear out problems[]. In % of the cases maintenance would do more harm than good by introducing a new infant mortality phase.. Optimize the portfolio of investment opportunities. In this approach the effort is not spent on improving the individual proposals, but on searching for better opportunities in the network. For example, if the regulator sets a target for the Customer Average Interruption Duration Index (CAIDI) [3], this approach would identify all opportunities for improvement and start with the ones with the highest yield (CAIDI improvement per Euro spent). Of these alternatives the first is relatively straightforward, as it does not require a new approach to valuing the investment opportunities. The second one does. It requires a framework with which one can value the different aspects involved in infrastructure decision making, and there are many. All asset managers have somehow 1 In the context of this paper maintenance is also an investment opportunity.

2 introduced a multi criteria analysis in their decision making. This MCA can be a cost benefit approach, a costrisk approach, or any other variety of MCA available. Sometimes the MCA is performed at the individual investment, sometimes it looks at the portfolio, a few look even at the dependencies within the total expenditure portfolio 3. Although these new valuation frameworks have brought tremendous efforts [], most decisions are still plagued by large uncertainties in the prognoses. In financial asset management options are used to reduce the risk of uncertainties, and real options can be used for real asset management. However, so far real options are not commonly used in valuing the investment opportunities in electricity networks. In this paper we will explore the possibilities for the use of real options in valuing investments opportunities. To do so we will first describe the system in which the decisions about the infrastructure are made. Secondly, we will introduce a common decision problem for network managers. For this case we will use a number of decision criteria (both technical and economic), all resulting in a different outcome for investment timing. We then identify the uncertainties that drive these differences. A few of these uncertainties have been resolved over the years. Others have not. Finally, we will look at some opportunities for the real option theory to value solutions to overcome those remaining uncertainties. System description In this paper we look at decision making about investment opportunities in electricity networks. In this section we will describe the system in which the decisions are made and specify what we mean with an investment opportunity..1 Investment decisions Investment decisions in electricity infrastructures are quite different from financial investment decisions. Financial investments are supposed to deliver a series of future cash flows that, adjusted for the opportunity cost of capital, has a higher value to the investor than the initial payment[5]. An investment for which this is the case is said to have a positive Net Present Value (NPV). The decision rule is very simple: only make investments that have a positive NPV. Investment decisions in electricity networks cannot be based on such a simple rule. There are 3 major causes for this deviation: 1. It is very difficult to allocate cash inflows to specific network elements. Most elements can be removed individually without effects on the cash PRIMO, a portfolio optimizing tool developed by TNO telecom and Essent Netwerk. 3 For this a tool like dependency can be used ( inflow, but removing all elements will stop the cash inflow. Most investments in the network do not directly induce new income, but only create the opportunity to connect new customers.. Investments that do induce new income are new connections. However, to protect the customer against monopolistic behavior the network companies have the obligation to connect anyone requesting it to the network against predetermined tariffs, even if the new connection has a negative NPV. 3. Investments often deliver benefits on other values than the financial one, for example on safety, sustainability or reliability. For those values the agreed upon monetary equivalent is only an order of magnitude. For example, a fatality is mostly valued between 1 and 1 million euros. This is not nearly accurate enough to be used in Net Present Value calculations. Because of these differences with normal investment decisions we will call decision making in electricity networks risk management. This risk management consists of two control loops, as shown in the picture below. Decisions Institutional Environment of the system: Government and Public Regulations Control system: Decision makers, Engineers and Operators Risk Management (valuing opportunities) Network Risk Governance ( valuing risks) Network performance Network company performance Figure 1: The risk management model The first control loop is risk governance in which is decided about the monetary equivalent of the non financial values. Decided is put between quotation marks, as it often is no explicit valuation, but an implicit valuation that can be derived from the decision to implement certain regulations. If the costs and the benefits of those measures are known, the monetary equivalent can be calculated. Risk Governance involves multiple actors. The second control loop is risk management. In this loop the costs, the projected (non financial) benefits and the monetary value of those benefits are calculated and tested for a positive NPV. For this calculation the non financials have to be discounted at the same rate as the financials[].. Investment opportunities The problem space in the risk management cycle is very large. Although we only look at one case study, we do not

3 want to tempt the reader into thinking that decision making in infrastructures is easy. To get a feeling for the immensity of the problem space the table below shows some dimensions of the problem space with the upper and lower limit on the dimensions[7]. Table 1 Dimensions and upper and lower limit of the problem space village will not be interrupted if one of the cables fails. The power consumption in the village is growing. A question every asset managers asks is when the cable section should be upgraded with a third cable. The diagram below shows the basic outline of the problem. 11kV Dimension Lower limit (example) Consequenc Voltage sag e of failure Failure 1 ms (voltage duration sag) Probability <1- (Nuclear (1/yr) meltdown) Mitigation 1 (switchgear Size maintenance) Complexity Single criterion threshold (load level exceeded) Scope Technical (device settings) Time horizon Actors Risk Perception Tomorrow (new customer application) Single actor single objective Perception in line with objective risk analysis Upper limit (example) European blackout Weeks (France 1999) >1 (medium voltage interruptions) >1 G (new high capacity power plant) Multi criteria weighted sum (maintenance concept) Ethical (Can we continue operating overhead lines if this might increase the probability of leukemia) 5 years (network design for distributed generation) Multi actor multi objective Perception deviating from objective risk analysis Uncertainty Almost certain Uncertainty about consequences and likelihood Ambiguity Shared objectives Conflicting objectives High voltage substation 1 kv 3 Al, 1 3 Al, 1,5 Medium voltage substation1 kv Figure : The capacity decision problem In this diagram we see a redundant high voltage substation, to which the medium voltage substation is connected with two cables which have a nominal rating of 1 5. The total load connected to the substation is 1 and grows with 3% a year. The diagram below shows the development of power consumption in the last years and the prognosis of future demand Load development Figuur 3: Load development,5,5,5 Projected peak Historic peak In this table we can see 11 dimensions with upper and lower limits that can differ more than a factor 1 million. This makes it very difficult for the decision maker to keep the portfolio of activities consistent, or if that is not possible, at least coherent. 3 Case introduction The case we will look at is a fairly common capacity problem. A village of 5 customers is connected to the high voltage grid with two medium voltage cables. These two cables are designed to be redundant, meaning that the A single cable has a failure rate of about hours every 1 years. The likelihood of the second cable failing in this window is again hours per 1 years, resulting in a total likelihood of about once every 1 years with an average duration of about hours (half the window). As both cables can be the first to fail the combined frequency is doubled. This number does not account for common cause failures, in which a single event takes out both cables. 5 The real power capacity (in MW) is about 9% of the rating. The actual growth rate of electricity consumption is estimated between 1% and 3% annually, depending mostly on economic development.

4 Technical valuation.1 The classic approach: nominal rating The most safe decision approach is to upgrade the circuit with a third cable once the nominal rating of a single cable is reached. This means regardless of the circumstances, the remaining cable will not be overloaded in case the other cable fails. In the example above this would mean to upgrade the circuit in the year 5. This safe investment strategy therefore is an expensive one, as investments are made relatively early.. The enhanced approach: dynamic rating The failure mechanism behind overload is temperature related degradation of the insulation material. This means the overloaded cable will not fail immediately after an overload occurs, but only if the temperature has reached a critical level. As the cable and the surrounding ground need time to heat up, an overload could be accepted for a limited time. The diagram below shows the maximum allowed load levels (MALL) for some periods [],[9] second 3 Maximum allowed load levels 1 hour hour Time hour continuous MALL Figure : Maximum allowed load levels (MALL) of the electricity cable used in the example. All failures in the cables will be repaired within hours. In the figure above we can see that the cable can bear a load of 1 for hours. Investment timing Investment moment Investment moment according to according to hour continuous rating MALL Prognosis Historic Figure 5: The delay of investment by changing the criterion This means the circuit does not need upgrading before the load level has reached 1. This will only happen in 1. We see in figure 5 the replacement of the allowed nominal current by the allowed dynamic current delays the investment by years. Only in case the cable will not be repaired within hours there will be a slight increase in the risk. In the diagram below the shift from continuous rating to the hour MALL is visualized. 5 Economic valuation In the previous two decision approaches the decision criterion was technical. Could the customers be supplied if a failure occurred in one cable? In the decision no attention was paid to the value of the new cable. To value an investment in reliability a figure is needed for the value of reliability. As no real market exists for reliability this is a very difficult task. A figure commonly used for valuation of reliability is the economic damage caused by an interruption. However, this is a highly volatile number. A report by KEMA [1] showed values ranging between,5 EUR/kW and EUR/kW. For the quality regulation in the Netherlands the figure to be used is,5 EUR per customer minute lost (CML). With an average of kw per customer and an average interruption duration of two hours this equals 15 EUR/kW, well within the specified range. In this paper we will use the figure expressed in EUR/CML, as the interruption duration in the case deviates a factor from the average interruption duration in the Netherlands. To use this economic value in the decision we need a model to calculate the expected unreliability. In the base case we will assume that the cable will fail if its load exceeds the previously specified value of 1. The value of an interruption will be 5 customers times minutes times,5 EUR/CML equals Euro. Such an interruption is expected once every 5 years 7. The expected annual value of an interruption is then 19 euros. The economic value of the cable would be the net present value of this figure over 3 years (the life expectancy of the cable), resulting in a value of.111. euro. As the costs of a third cable are about 1.. euro 9, the benefits clearly exceed the costs. If in case of a failure only the part of the load that exceeds the maximum load level is interrupted, the economic damage could be limited very much. This is called load shedding. In the figure below the cost of load shedding are compared with the annuity of the investment (3 year,,5% real interest) in a new cable. For the 7 The failure rate of a single cable is 1 per 1 years. As there are cables, the failure rate of the circuit is per 1 years or 1 per 5 years. Based on a real interest rate of,5%. 9 Cables cost about 1 euro per kilometre

5 maximum allowed load level the safe value of 1 is used. We can see that there is no cost of load shedding until 1, the year in which the load exceeds1. Load shedding is the low cost alternative until the year 5. This means the use of a very simple risk calculation method will postpone the investment in a new cable until the year 5, a delay of years compared with the classical approach. load level is only 5,5. The peak load occurs only in blocks of hours, so with the current load pattern the limitation to the hour MALL is very risk averse. Even the use of the hour MALL combined with the maximum occurring load level would be risk averse. 1 Load pattern 1 Minimum Euro/year x Optimal investment timing Figure : Optimal timing of investment Valuing load shedding Cost of load shedding Annual cost cable Economic optimum The previous section showed a possible delay of investment in new capacity of about years. This would solve the expected problem of the income cuts for the network managers. They even would not have to think about investing in new capacity for at least 15 years. However, this only holds if load shedding is valued equally as the unintended power interruptions. There is evidence this is not the case. After the energy crisis in California the market was restructured to prevent any further rolling blackouts. The costs involved in this restructuring was a thousand times more than the expected economic damage resulting from those rolling blackouts[11]. This would result in a value of 5 euro/cml. If this figure is used for calculating the optimal investment moment it will not result in an extra delay compared with the dynamic rating example. In the following sections we will not consider involuntary load shedding any more. Instead, we will look further into the uncertainties of the risk model. 7 Uncertainties In the previous examples we did assume certainty in modelling the decision. Unfortunately we are not at all certain about the behaviour of cables and loads in reality. The first major deviation is the load pattern. The model assumed a continuous load level. In reality the load shows a pattern. In the diagram below the load pattern of the described case study is shown. The maximum load of 1 is only reached one hour a year. The average Hour in week,5 percentile 1, percentile Average 3,3 percentile 97,5 percentile Maximum Figuur 7: Actual Load pattern for a village recorded at Essent Netwerk. The diagram shows the distribution per hour of the week. For example, the maximum load of 1 occurred at hour 1 of the week, which is between 19. and. of a Saturday. However, for 3 (or 3%) of the 5 weeks the load in this hour 1 did not exceed 9. The second deviation is the timing of the incident. In the model we assumed that the interruptions were evenly distributed over the years. This is in accordance with cost benefit analyses[1] and net present value calculations[5]. However, in reality either a large interruption occurs, or no interruption at all. There is a reasonable likelihood that the circuit will not experience any failure in its lifetime. For a circuit with a failure rate of 5% per year the probability of no interruption at all in 3 years is more than %. It is clear that in an perfectly working circuit the redundant cable has no value. The third deviation is the actual economic damage caused by the interruption. As we have seen the damage can vary by a factor 1, depending on the type of customer connected. Not all areas have the same distribution of customer types. Optimizing the reliability on the average value would lead to a too low quality in areas with a high concentration of vulnerable customers, and vice versa. Besides, as customers move, the distribution is not constant over time. Therefore the true economic damage caused by failures is uncertain both in location and in time. The last major deviation is that the Maximum Allowed Load Levels are safe figures, at which a cable will not fail for certain. The model assumed that cables would fail for certain if the load exceeded the figure. This is most likely not the case. Unfortunately, the relation between load level and failure rate is not known very well. In some cases the load can be up to % of the MALL without causing any damage, in other cases the cables

6 might fail below the continuous level, if hot spots 1 are present in the circuit. Uncertainties resolved In this section we describe the methods that are currently in use to resolve two of the uncertainties..1 Load pattern A way to overcome the uncertainty in the load pattern is to replace the peak load characterization with a thermal equivalent load. This is the continuous load which dissipates the same amount of heat in the cable as the load pattern. As heat dissipation is proportional to the squared current, the thermal equivalent is the Root Mean Square (RMS) value of the pattern. In the example, the max line has a RMS value of,9. This is shown in the figure below. 1 1 Load pattern Hour in week Minimum,5 percentile 1, percentile Average 3,3 percentile 97,5 percentile Maximum RMS maximum Figure : the RMS value of the maximum If this RMS value is used in combination with the hour MALL, it is highly unlikely that in case of a cable failure the other cable will fail because of an overload. The diagram below shows the optimal investment timing Investment timing Investment moment according to hour MALL and RMS value Projected RMS Historic RMS Figure 9: Investment timing according to RMS value The use of the RMS value delays the investment until the year. This is again a technical judgment, in which no 1 A hot spot is a section of the cable in which the heat dissipating capacity is limited. This might be caused by the proximity of other cables, tree roots, air ducts or soil with a high heat resistance. attention is paid to the costs of the investment, or the expected costs of an interruption.. Incident timing In our base case model we assumed that every year a small interruption would occur, whereas in reality it would be a large interruption or no interruption at all. For a single circuit with a failure rate of 5% per year the probability of a worthless redundancy for the next 3 years is more than %. Fortunately, if one looks at multiple circuits at the same time, the behaviour of this set is represented correctly by the sum of the average interruption sizes of the individual circuit. As networks are a large set of circuits (the three large network companies in the Netherlands each have.5 million customers, corresponding to 5 circuits as described) this uncertainty automatically resolves itself if looked at from the portfolio perspective. 9 Options for real options Of the major uncertainties are more or less resolved currently, which was described in the previous section. However, major uncertainties remain. The first one is the true economic damage caused by an interruption; the other is the true capacity of the cable. Uncertainties lend themselves for real options. In case of the electricity network these would be waiting and learning before investing and making the capacity more flexible. In this section we will describe a few examples of those real options. Currently these investment alternatives are ignored by decision makers because of the problems in assigning the proper value. 9.1 True economic damage An approach to get a better estimate of the true economic damage in case of an interruption is to characterize the customers in the area for their vulnerability. However, as the valuation of the involuntary load shedding might differ by a factor 1 from the true economic damage, it seems reasonable to question this approach for its validity. Another approach is the use of load management contracts. A load management contract is an agreement between the network company and a customer, in which the customer commits itself to reduce power consumption when requested for by the network company. The network company pays a fee for this right. However, negotiating such a load management contract is quite a challenge, as it is vulnerable to all sorts of strategic behaviour. For example, a customer would try to get a fee as high as possible, regardless of the economic damage caused by the interruption. The price of the contract therefore would not be a measure of the true economic damage, but it would be a true measure of the value of the interruption. The limitations in this game are this expected damage as the lower limit and the worth of the investment postponement

7 for the network company. To be able to play this game correctly the network company needs to value the investment postponement, for which the real option theory seems the appropriate instrumentation. However, it is not clear yet to those network companies how the theory should be applied. 9. True cable capacity The true cable capacity is mostly determined by hotspots. At the moment the technology is available to measure the temperature of the cable real-time. This would allow the operator to monitor the cable for overload situations, and thus preventing a failure caused by such an overload. The costs of such a built-in thermometer are relatively modest. If such a technology was used in combination with the current design practices, it would become valuable in to 3 years. However, its worth is yet unknown. 9.3 Circuit upgrading options A final opportunity for the real option theory is to value a cable which can be upgraded relatively cheaply. For example, using a kv cable instead of a 1 kv cable allows for doubling the capacity if needed. It would require two 1/ kv transformers at both ends of the cable, but for long stretches of cable this would cost less than a new cable. The question is if the value of the option to expand relatively cheaply exceeds the extra cost of the cable. 1 Conclusions Network managers try to postpone investment in new capacity because of the income cuts posed upon them by the regulator. If the investment strategy for new capacity was based on maximum allowed load levels for continuous loads and the occurred peak load there are lots of opportunities to delay investment. This can be reached without any economic valuation, just by using common sense to review the investments. However, not all uncertainties can be resolved by using common sense. In case of load management contracts, cable monitoring equipment and circuit upgrading options the real option theory seems to be the appropriate tool to value the investment opportunities. If the real option theory could be used to value these uncertainties properly, it would be a great support for the decision makers of the future. Acknowledgement The research of the author, described in this paper, is sponsored by the Next Generation Infrastructures research programme ( References [1] Electricity Association, Electricity industry review,. [] ALM-3-79-C, "Reliability Centered Maintenance." [3] T. Short, "Reliability indices," presented at T&D World Expo, Indianapolis,. [] Y. C. Wijnia, "The Challenges of a risk based asset management organization," Strategic asset management, pp. 3-37,. [5] R. A. Brealey and S. C. Myers, Principles of corporate Finance. New York: McGraw-Hill,. [] "Spec Ranking regulatory investments in public health," in Treasury and General Government Appropriation Act, vol. Public law 1-55, 1. [7] Y. C. Wijnia and P. M. Herder, "Modeling Interdependencies in electricity infrastructure risk," presented at 1st Annual CZAEE International Conference Critical Infrastructure in the energy sector: Vulnerabilities and protection, Prague,. [] IEC 7, "Electric cables- calculation of the current rating," in Part 1-3: Current rating equations(1% load factor) and calculation of losses- current sharing between parallel single-core cables and calculation of circulating current losses,. [9] IEC 53, "Calculation of the cyclic and emergency current rating of cables," in Part 1: Cyclic rating factors for cables up to and including 1/3 (3) kv, 195. [1] KEMA, "Wensstromen: Gewenste kwaliteit- De waardering van de kwaliteit van levering van elektrische energie door aangeslotenen," KEMA, Arnhem PREGO 1 33-TDC 3-3B,. [11] M. de Bruijne, "Reliability against the odds: California's electricity crises," presented at 1st Annual CZAEE International Conference Critical Infrastructure in the energy sector: Vulnerabilities and protection, Prague,. [1] R. Sugden and A. Williams, The principles of practical Cost Benefit Analysis. Oxford: Oxford University Press, 197.