11.1 Case Study 1: Liquid Natural Gas in Victoria State, Australia
11.2 Setup of the EOA Approach for the LNG Case Study
11.2.1 Design Alternatives
-
Flexible strategy—timing: this strategy deploys capacity at the central site according to how demand does or does not grow over time (Fig. 11.1b).
-
Flexible strategy—timing and location: this strategy allows for gradual deployment of capacity both over time AND geographically at the distribution sites (Fig. 11.1c).
11.2.2 Parameter Values
-
Economies of scale drive down the unit cost of capacity for larger plants, according to the standard formula: Capital cost (Capex) of plant = K (Capacity)α (K being a constant and α being the relevant economies of scale factor. α can range between 0 and 1. When α = 1, Capex increases linearly with Capacity, indicating there are no economies of scale. Smaller values of α indicate increasing economies of scale. In practice, usual economies of scale exist up to α = 0.85, and α ~0.6 is the maximum observed.)
-
The Capex of a LNG plant with a capacity of 25 tons/day is $25 million.
-
The annual operating costs for a LNG plant (Opex) are 5% of its Capex.
-
A management-imposed discount rate of 10%, given the risks and the opportunity cost of capital.
-
A management-defined project lifetime of 20 years; corporate tax rate of 15%; and depreciation as straight-line over 10 years with zero salvage value.
11.2.3 Characterization of Sources of Uncertainty
11.3 Results from Applying the EOA Approach to the LNG Case Study
11.3.1 Fixed Design
-
There is a design “sweet spot” for the optimal, most profitable, plant size (the stars on the curves) for any level of economies of scale: build too small, and the design loses out on potential profit from higher demands; build too large, and there is overcapacity and attendant lower values.
-
The greater the economies of scale (smaller α), the larger the optimal fixed design should be. This is because the economies of scale lower the average unit cost of capacity, and thus favor larger designs.
11.3.2 Performance of Fixed Design Under Uncertainty
Economies of scale factor, α | Most profitable capacity (tons/day) | Present value (PV) of plant at most profitable capacity ($ millions) | ||
---|---|---|---|---|
Deterministic analysis (ignoring uncertainty) | Stochastic analysis (recognizing uncertainty) | Deterministic net PV (NPV) (ignoring uncertainty) | Expected net PV (ENPV) (recognizing uncertainty) | |
1.0 | 50 | 25 | 1.75 | 0.87 |
0.95 | 100 | 75 | 21.51 | 14.27 |
0.90 | 175 | 125 | 51.75 | 37.18 |
0.85 | 200 | 175 | 84.56 | 61.18 |
11.3.3 Flexible Strategies
11.3.4 Flexible Strategy—Timing (But No Learning)
-
IF “the difference between the observed demand and current capacity is higher than 90% of the capacity of the module in the previous period,”
-
THEN “expand current capacity by adding a module,”
-
ELSE “do nothing”.
-
Our base case optimal fixed design (specifically with a medium α = 0.95), and
-
A flexible strategy that enables capacity expansion at the main production site when actual demand justifies this investment.
-
Can lead to large losses (NPV could be as low as−$25M), because the larger plant leads to large losses if sufficient demand does not materialize; and
-
Cannot gain more than an NPV = $21M, since it cannot serve demands exceeding its fixed capacity.
-
Its ENPV = $20.69M, nearly 44% better than the fixed design ($20.69M vs. $14.27M)!
-
Its overall performance in this case stochastically dominates that of the fixed design (that is, its target curve is absolutely to the right of, and thus better than that of the fixed design).
-
It reduces exposure to downside risks: the decision to start small puts less investment at risk and lowers maximum losses if demand is low. In this particular example, the flexible design strategy reduces maximum NPV loss from about−$25M to less than−$5M.
-
Conversely, it provides the ability to take advantage of upside opportunities: it enables the easy addition of capacity when demand soars, and increases the maximum NPV gain from about $21M to nearly $38M.
11.3.5 Flexible Strategy—Timing and Location (But No Learning)
Criterion | ENPV value ($ millions) | Improvement (%) | |||
---|---|---|---|---|---|
Optimum fixed design | Flexible timing | Flexible time + place | Flexible timing | Flexible time + place | |
Expected NPV | 14.27 | 20.69 |
23.29
| 45 |
63
|
Value at Risk, 10% | 1.82 |
5.40
| 3.74 |
197
| 105 |
Value at Gain, 90% | 20.46 | 34.54 |
45.78
| 69 |
124
|
11.3.6 Flexible Strategy—Learning
11.3.7 Learning Combined with Economies of Scale
-
As expected, lower economies of scale and greater learning rates increase the value of flexibility.
-
The value of flexibility in this case ranges up to $60 million. Flexibility thus offers significant potential, which demands exploration.
-
In this case, the flexible strategies are valuable for all but the most extreme cases (that is, cases where the economies of scale are particularly high and there is no learning). For even modest learning rates and economies of scale, the flexible modular design is valuable overall.
-
One may thus conclude that, in this case, the modular flexible strategies fare well over a wide range of variations in these parameters.
11.3.8 Multi-criteria Comparison of Strategies
Criterion | Fixed design | Timing strategy | Time + place strategy | Value of flexibility | Best strategy |
---|---|---|---|---|---|
Expected NPV | 14.27 | 36.93 |
43.17
| 28.80 | Time + place |
Value at Risk, 10% | 1.82 | 10.82 |
11.06
| 9.24 | Time + place |
Value at Gain, 90% | 20.46 | 63.17 |
80.09
| 59.63 | Time + place |
Standard Deviation |
8.78
| 18.91 | 25.31 | NA | Fixed |
Initial Capex | 60.44 |
27.50
|
27.50
| 32.94 | Either |
-
Measures of the dispersion of the results: the 10% Value at Risk, the 90% Value at Gain, and the Standard Deviation;
-
The initial Capital Expenditure (Capex) of projects, to which many investors pay great attention when there is substantial risk.
-
As often happens, different projects appear best according to different criteria.
-
The fixed design has the lowest standard deviation, and thus might be labeled most “stable.” This could be considered a good thing, but here this merely indicates that the fixed design performs uniformly poorly, as it cannot take advantage of upside opportunities.
-
Overall, the flexible strategies appear to provide the most balanced overall solutions.
11.3.9 Guidance from Applying EOA to This Case
-
Reject the fixed design.
-
Its average performance is superior only in the extreme cases, in which there are the most favorable economies of scale and no learning (Fig. 11.7).
-
Choose a flexible strategy using small modules. These dominate in terms of:
-
Start the project with a small module, leaving open the question of whether to later adopt a strategy that accepts the possibility of locational flexibility in addition to timing flexibility.
11.4 Case Study 2: Water Management Infrastructure in the Netherlands: IJmuiden Pumping Station
-
Flood defense, serving as a barrier between the North Sea and inland areas, reducing the risk of flooding from high water on the North Sea.
-
Regulation of inland water levels, discharging inland precipitation runoff to the North Sea.
-
Water quality management, separating the saline water of the North Sea from the fresher water in the canal.
-
Ecological management, facilitating the passage of fish.
11.5 Setup of the EOA Approach for the IJmuiden Pumping Station
11.5.1 Characterization of Sources of Uncertainty
Station function | Source of uncertainty | Mechanisms by which uncertainty can have an impact |
---|---|---|
Flood defense | Sea level rise | Affects the adequacy of the installed flood defense height |
Inland water level regulation | Sea level rise | Decreases the time that water discharges under gravity from the canal to the North Sea. Also increases the hydraulic head between the surface of the canal and the sea, thus reducing the pumps’ discharge ability when pumping is required |
Precipitation intensity increase | Affects the volume of water entering the canal at a given time. Given limited storage in the canal, increased inflows may require expansion of existing discharge capacity |
-
Level 2 uncertainties that we can model probabilistically with some confidence. The analysis models these using stochastic variables.
-
Level 3 and Level 4 uncertainties2 that we handle using a range of scenarios.
Uncertain variable | Scenarios for 2100 relative to 2015 | |||
---|---|---|---|---|
Mean sea level | Low: +35 cm | High: +85 cm | ||
Mean winter precipitation | Low: +4.5% | High: +12% | Medium: +11% | Extreme: +30% |
-
Uncertainty in the water heights associated with a particular flood return periods,
-
Natural variability in precipitation, and
-
Uncertainty in the precipitation-canal inflow relationship.
11.5.2 Design Alternatives
-
Fixed design, consistent with the traditional predict-then-act approach to water resource planning. The structure provides at least the minimum level of service through to the end of its design life, with a safety margin added to buffer against any uncertainties that may not be captured in the analysis. It embodies the traditional engineering mindset, emphasizing over-dimensioning and taking advantage of any economies of scale (Table 11.6, Column a).Table 11.6Design alternatives considered in the IJmuiden case study
-
Reactive Adaptive design, which acknowledges that a fixed structure may represent an over-investment and hence emphasizes designing for the best-available current information and making changes as needed as the future unfolds (Table 11.6, Column b). Designers size reactive adaptive designs for the short-term, but make no explicit preparations to facilitate possible future adaptations.
-
Proactive Flexible design, which goes a step further than the reactive adaptive design in that it prepares for the future by choosing to include options within the initial structure (Table 11.6, Column c). Designers size flexible designs for the short term, but proactively incorporate options that enable easy adaptation in the future.
-
A height able to withstand the best-estimate flood heights over a 25-year planning horizon. This relatively short planning horizon acknowledges the need to revisit this decision in the coming decades, but does not make any explicit preparations for possible later expansion. If a height expansion becomes necessary in the future, it will come at a considerable cost, because resizing of the structure’s foundation will be necessary.
-
A pumping capacity able to discharge the best-estimate canal inflow volumes over a 25-year planning horizon. If the addition of further pumping capacity becomes necessary over the structure’s lifetime, a new “mini” pumping station will need to be installed adjacent to the current structure.
-
The option to expand the flood defense function includes a larger-than-currently necessary foundation for the structure; this facilitates future height additions as needed.
-
The option to expand the function to regulate the level of inland water includes additional pump bays in the concrete frame; these enable easy installation of additional pumps if/when necessary. Steel gates seal off these additional bays until the time managers install additional pumps.
11.5.3 Details of the Analysis
-
Physical performance module links changes in future operating conditions (such as higher sea level) to performance indicators of interest (such as water levels associated with specified return periods at a certain location). It generates many simulations of future environmental conditions, consistent with the different sea level rise and precipitation change scenarios (Table 11.5). This module both indicates under what future conditions the current physical system becomes inadequate, and captures how different possible courses of action affect the future performance of the system. There is a different module for each of the two functions the case examined.
-
Economic evaluation module uses the simulations of the physical system as input to compare different courses of action based on whichever performance indicator(s) the analyst considers most suitable. This case study analyzed lifecycle costs of the different structural designs (Table 11.6). It compared the alternatives based on total cost of ownership, including possible later expansion costs in addition to initial capital costs. All analyses applied a discount rate of 5.5%, consistent with a 2.5% risk-free rate and a 3% risk premium, which the Rijkswaterstaat uses for capital investment projects. However, the case study also generated results for a range of other discount rates for the purpose of sensitivity analysis. The lifecycle cost analysis used Monte Carlo simulation to evaluate 1000 different versions of the future for each of the different scenarios.
11.6 Results from Applying the EOA Approach to the IJmuiden Pumping Station
11.6.1 Inland Water Level Regulation Function
-
Fixed, which establishes now the maximum pumping capacity that might eventually be needed;
-
Reactive Adaptive, which builds what is needed now, and will upgrade the installed pumping capacity as dictated by emerging future conditions; and
-
Proactive Flexible, which creates a pumping station with the maximum number of pump bays that might be needed, but defers purchasing and installing the pumps until actually necessitated by external developments, and thus saves on immediate costs.
-
Incremental Reactive Adaptive and Proactive Flexible designs outperform the Fixed design;
-
The Proactive Flexible design dominates the Reactive Adaptive design in riskier futures, and when lower discount rates are applied, while the Reactive Adaptive design performs better in less risky futures;
-
So the preferred choice between Reactive Adaptive and Proactive Flexible designs depends on the decisionmakers’ belief about the future and their willingness to bear high-cost worst-case outcomes.
11.6.2 Flood Defense Function
-
Fixed, which establishes now the maximum flood defense height that might eventually be needed;
-
Reactive Adaptive, which builds what is needed now, and will upgrade the flood defense height as dictated by emerging future conditions; and
-
Proactive Flexible, which creates the foundation on which to build the maximum flood defense height that might be needed, but defers raising the height until actually necessitated by external developments, and thus saves on immediate costs.
-
The benefits of the Reactive Adaptive design do not justify its risks;
-
There is little to choose from between Fixed and Proactive Flexible designs;
-
So the preferred policy might be to adopt the Fixed design, and be done with it.
11.6.3 Guidance from Applying EOA to This Case
-
For those design elements contributing to the pumping station’s ability to regulate inland water levels:
-
Reject the Fixed design, which sees all the pumping capacity that might eventually be needed installed at the outset.
-
Choose one of the two incremental strategies, either the Reactive Adaptive design or the Proactive Flexible design.
-
-
The Reactive Adaptive design is the preferred design for decisionmakers more willing to accept higher long-term costs in exchange for short-term savings by building a smaller structure.
-
The Proactive Flexible design is the preferred design for decisionmakers who anticipate and want to be prepared for large degrees of environmental change in the future.
-
For those design elements contributing to the pumping station’s ability to withstand future floods on the North Sea:
-
Reject the Reactive Adaptive design because the short-term cost savings from choosing a smaller structure do not outweigh the future risks.
-
The Fixed and Proactive Flexible designs demonstrate comparable lifetime economic performance. Thus, all else being equal, the preferred policy may be to simply adopt the traditional Fixed design.
-
11.7 Conclusions and Reflections for Practice and Theory
-
EOA does not prescribe a single plan. The focus is on how to start on a path that leads to a line of possible desirable developments while avoiding threatening downside risks. EOA does this in the same way a chess master does not commit to the details of a strategy from the start, but begins with a single move, such as Pawn to King 4. Chess masters will choose the details of subsequent strategy as they learn about the intentions of their opponents, as their deep uncertainty gives way to more knowledge and understanding. Likewise, the project planners for the LNG plant can defer the details of their strategy until they learn more about the market for the product, the government intentions, energy prices, and the economies of scale and learning rates of the technology. So too, the project managers of IJmuiden can monitor changes in precipitation and sea level before taking appropriate next steps in their long-term investment plan.
-
EOA can cope with diverse and deep uncertainties: The two cases presented here applied EOA to planning problems in which there were a diversity of sources of deep uncertainty, ranging from future technological innovation, demand for LNG, sea level rise, and changes in precipitation. Taken together, these cases indicate that EOA can provide valuable insights for the decision process, when faced with immense uncertainty and in the absence of reliable probabilistic information.
-
In contrast to traditional Options Analysis, EOA produces distributions of results, which provide valuable additional insights: These two cases produced results in the form of distributions, and demonstrate how the calculation of distributions of possible outcomes provides decisionmakers with useful information concerning worst-case outcomes, unavailable from average outcomes alone. Results in this form explicitly inform decisionmakers about the tradeoffs among objectives, helping them to identify preferred strategies.
-
EOA is a versatile and rigorous analytical method: The core EOA method can be adapted for many different types of problems and purposes. The LNG case study emphasized the role of infrastructure size, economies of scale and learning rates on the development of a sound investment plan, while the IJmuiden case highlighted how a reactive versus proactive approach to investment can result in substantially different outcomes. The basic method lends itself to diverse modifications and additions.