Based in Reykjavík, Iceland, Enex provides renewable energy services, specialising in the development of geothermal power plants to generate electricity and provide district heating. The company searches for opportunities to harness geothermal power, along with the design, engineering, procurement and construction of a variety of power plants including Combined Heat and Power plants (CHP), Flash Steam and Binary Cycle power plants, and Geothermal District Heating Systems.
Enex also acts as an investor and forms joint ventures with local partners to finance, develop, construct and operate renewable energy projects. Its key markets include Germany, Eastern Europe, and the US.
The company is planning to build a geothermal power plant in Europe. In preparation for this, and to maximise expected financial gains, Enex conducted a study using Palisade’s The DecisionTools Suite comprising decision and risk analysis software for Microsoft Excel. The DecisionTools software PrecisionTree®, Palisade’s decision analysis tool, and risk analysis software @RISK, its Monte Carlo simulation tool, enabled Enex to determine the optimal timescale for procuring equipment during the construction of this new geothermal power plant along with the optimal plant size.
Financial risk and uncertainty
Geothermal power plants are developed to harness the thermal energy stored within the earth’s crust. This means that the site identified to build the plant has to be suitable for its construction.
The key geological factors that impact on the success or failure of a geothermal power plant project are temperature of reservoirs, flow of liquid from wells (or enthalpy and pressure), depths to which wells need to be drilled and the chemical conditions of the brine within the wells. These define the production capacity of a geothermal power plant as well as influence the investment and operation costs of the project.
As these are natural conditions, there is a huge amount of uncertainty for geothermal power plant suppliers about whether the selected site will be suitable for such a plant.
Enex has identified a site for its new power plant based on a calculated guess from studying the historical and geological data available for the region in question. However, Enex will only be able to determine whether the site is going to be viable for a geothermal power plant when it starts drilling the wells. This means that Enex will have to incur a proportion of the overall financial investment in the project upfront, without any certainty of success.
Cost of equipment constitutes bulk of total investment
In a geothermal project, the cost of drilling the wells and the power plant (i.e. equipment) are the key factors that account for the bulk of the costs of a geothermal project. In fact, about 70 to100 components that are purchased for a power plant can account for anything between 30 to 80 per cent of the total cost of the project. This translates to approximately 1500-3500 /Kilo Watt of energy. The drilling of the wells accounts for the balance of the cost.
Further, given the engineering precision and complexity of power plant equipment, some of the critical and most expensive components such as turbines, heat exchangers and pumps have very long lead times. For instance, delivery time from the point of ordering a turbine until delivery can be over can be 75 weeks.
Timing of ordering equipment critical
To ensure that construction and commissioning of the plant will be executed smoothly, with minimum delay and maximum profitability, the timing for ordering equipment with long lead times is critical.
However, identifying the optimal time for ordering components is very difficult. For example, if Enex pre-orders equipment before the first drilling of the well, there is a possibility that the company may find out after the drilling is completed (which can last up to four months) that the well needs to be closed due to sub-optimal well conditions pertaining to temperature, flow and depth of the well. Or that the results of the drilling will not be suitable for the components that were pre-ordered.
Viktor Thorisson
Analyst, Enex
PrecisionTree used to model potential scenarios for equipment procurement
For this study, Enex made two key assumptions. Firstly, the plant would comprise two production wells. Secondly, if drilling results in sub-optimal conditions in a well twice in a row, the project would be considered a failure on account of the location not being suitable for a geothermal power plant.
The critical question that Enex needed to answer in this study was whether to procure the critical components with the longest manufacturing and shipping lead time before the first drilling, after the last drilling or between the first and last drilling.
Enex used PrecisionTree to model the different types of events that could potentially occur if the equipment was ordered before or after the drillings. For instance, if equipment is ordered before drilling the first well, whilst this will ensure that the equipment is available in time for use, Enex will be taking the risk of making a substantial investment without being certain that there would eventually be a need for it. Also, the company will need to order the components based on certain specifications that may or may not be suitable for the conditions of the wells. The possibility of a well being unsuitable and therefore being closed after initial drilling is considerable. Therefore, if the pre-ordered equipment is unsuitable, Enex will incur cancellation fees, which can be in the region of 20 per cent of the component cost. Cumulatively, across the different types of equipment ordered, this can amount to a substantial cost.
Additionally, after the last drilling, Enex will potentially have more information on the state of the well and will therefore be able to better determine the equipment specifications to optimise plant efficiency. For example, prior to drilling, Enex might order a turbine that was of a higher capacity than actually required, making it a more expensive component than necessary. Alternatively, the pre-ordered turbine may be under-specified, which could significantly impact the production levels of the power plant and therefore profitability of the project.
Used @RISK to assess probability of success and failure
These different scenarios highlighted in the PrecisionTree model were then evaluated by Enex using Monte Carlo simulation in @RISK. This showed all potential outcomes, as well as the likelihood of every single event occurring.
For example, using historical and geological data, Enex used @RISK to simulate the production index and temperature to estimate the potential distribution of the peak production capacity of the plant.
Further, @RISK enabled Enex to estimate the optimal plant size. An accurate estimate of plant size is important as it can have huge ramifications on costs and profitability of the plant, especially when pre-ordering of equipment is being considered. For instance, if the pre-ordered equipment is under-sized or less powerful than required, Enex would incur a cost based on the lost production as the components will be unable to optimise production in the plant. On the other hand, if the pre-ordered equipment is over-sized or more powerful than required, Enex would have to incur an unnecessary investment cost for the larger components as well as the potential loss of efficiency as a result of the gap between the estimated size of components during the design phase and the actual conditions being too much.
Using a combination of PrecisionTree and @RISK, Enex weighed up the advantages and disadvantages of pre-ordering and post-ordering the equipment. It has been able to confidently conclude that the optimal time for the company to order equipment for this project in question is before the first drilling of the well. Extra investment costs would need to increase by approximately 400 per cent, to warrant a change in this decision. There is an 89 per cent probability of success for a plant size of 14.5 Mega Watts.
For this plant size, by pre-ordering equipment, the plant will be able to start production sooner and therefore reduce the time between construction and when the plant begins to generate income.
Viktor Thorisson, analyst at Enex, commented, “Using Palisade’s sophisticated technology, its PrecisionTree and @RISK tools have highlighted that this rather untraditional approach of ordering the critical components before the first drilling is likely to give us a higher expected profitability. We expect drilling for this plant to start in Europe shortly.”
Additional Information
Distributions used Normal Uniform Beta General as this was best suited for geological and historical data
Sensitivity analysis This was used to analyse impact of efficiency reduction, lost production and extra investment on the NPV
Quantitative techniques used For defining the probability of success to input into the PrecisionTree, a function of production index and temperature was plotted that would give a 10 per cent Internal Rate of Return. The simulation was then performed using @RISK to assess how many iterations would result below that line, as shown in graph 1.
Graphs
For defining what temperature and what production index (PI) should be considered failure, a function was determined that would give 10% Internal Rate of Return on equity (IRR), which represents the minimum IRR for a project to be classified as successful.
When the PI approaches 0, the temperature necessary to obtain 10% IRR approaches infinity. Therefore, all values of PI below 0.2 and to the left of the plotted line are defined as failure.
Graph 1: Definition of failure as a function of temperature and PI
Graph 2: The sensitivity of the NPV for two decision paths This graph below shows that the expected extra investment cost due to pre-ordering (i.e. the mean expected extra investment cost due to under or over sizing the equipment) will need to increase by approximately 400% from the calculated base value to change the optimal decision. However, it can be seen that the slope of the line representing the expected NPV of the project, if the first well is drilled before ordering components, changes when the value of the X-axis is 200%. This figure represents a 200% increase in the extra investment cost due to pre-ordering of the components. This is due to the fact that at that point, the optimal decision in the PrecisionTree is to wait until all drilling is complete and therefore the NPV will be unaffected by the extra investment cost as a result of pre-ordering equipment.
Graph 2: The sensitivity of the NPV for two decision paths