Options for real options Dealing with uncertainty in ... - Semantic Scholar

5 downloads 167 Views 230KB Size Report
In this system competition is supposed to drive down costs and improve service ... financial asset management options are used to reduce the ... system: Government and. Public ... 2015. 2020. 2025. 2030. Year. MV. A. Projected peak. Historic peak ..... health," in Treasury and General Government. Appropriation Act, vol.
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

Paulien M. Herder Faculty of Technology, Policy and Management Delft University of Technology Delft, The Netherlands

[email protected]

[email protected]

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, Making, Real Options, Uncertainty.

1

Decision-

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 april2000/march 2001 was 21,8%. The total average income cut over the period 1995-2001 was more than 50%[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

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 opportunities1: 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 72% of components suffer from infant mortality, only 11% shows wear out problems[2]. In 68% of the cases maintenance would do more harm than good by introducing a new infant mortality phase. 2. 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.

Authorized licensed use limited to: Technische Universiteit Delft. Downloaded on June 5, 2009 at 08:38 from IEEE Xplore. Restrictions apply.

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 portfolio2, a few look even at the dependencies within the total expenditure portfolio3. Although these new valuation frameworks have brought tremendous efforts [4], 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.

2

PRIMO, a portfolio optimizing tool developed by TNO telecom and Essent Netwerk. 3 For this a tool like “dependency” can be used (www.dependecy.com)

Network company performance

Risk Governance ( valuing risks)

Regulations

Control system: Decision makers, Engineers and Operators Network performance

Decisions Risk Management (valuing opportunities)

Network

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 2

Institutional Environment of the system: Government and Public

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. 2.1

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. 2. 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 10 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.

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[6]. 2.2

Investment opportunities

The problem space in the risk management cycle is very large. Although we only look at one case study, we do not

Authorized licensed use limited to: Technische Universiteit Delft. Downloaded on June 5, 2009 at 08:38 from IEEE Xplore. Restrictions apply.

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].

village will not be interrupted if one of the cables fails4. 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.

Table 1 Dimensions and upper and lower limit of the problem space

Risk Perception

Uncertainty Almost certain Ambiguity

Shared objectives

Medium voltage substation10 kV

630 Al, 10 MVA 10 kV

Weeks (France 1999)

Figure 2: 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 10 MVA5. The total load connected to the substation is 10 MVA and grows with 3% a year6. The diagram below shows the development of power consumption in the last years and the prognosis of future demand. Load development 20 18 16 14 12

MVA

>10000 (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) 25 years (network design for distributed generation) Multi actor multi objective Perception deviating from objective risk analysis

Case introduction

The case we will look at is a fairly common capacity problem. A village of 4500 customers is connected to the high voltage grid with two medium voltage cables. These two cables are designed to be redundant, meaning that the

Projected peak Historic peak

10 8 6 4 2 0 1995

2000

2005

2010

2015

2020

2025

2030

Year

Uncertainty about consequences and likelihood Conflicting objectives

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

630 Al, 10 MVA

2,5 MVA

Actors

Tomorrow (new customer application) Single actor single objective Perception in line with objective risk analysis

High voltage substation

2,5 MVA

Time horizon

Upper limit (example) European blackout

2,5 MVA

Lower limit (example) Consequenc Voltage sag e of failure Failure 10 ms (voltage duration sag) Probability