Internal Cost Analysis

Large, interesting problems are generally constrained by limits on resources, emissions, or other factors, and financial resources are usually among the most important. The LEA multi-criteria approach always includes several forms of financial costs in its set of economic indicators. Internal costs that are born by customers are of first interest, but these are also added to external costs born by society (non-customers) to obtain total costs. The LEA multi-criteria approach focuses mostly upon tradeoff analysis and multi-criteria decision analysis, but the total cost ranking of technologies and scenarios is used as a valuable comparison to MCDA rankings using stakeholder inputs.

Internal costs are those born by the consumer, but for broad policy issues the question can arise of whether the customer is the producer or generator, the end-consumer or society as a whole (i.e., “your price is my cost”). The TA group approach is to focus on the net present value (NPV) production cost, averaged over the unit of the service produced (kWh electricity, or vehicle-km or tonne-km of transport). This technology-based focus does not normally include producer overheads (like the grid) or profits unless needed. It also normally ignores cross-subsidies or penalties, as these are essentially social transfer payments from taxpayers to customers (or vice versa). This focus on average costs is also useful for comparing overall system scenario that may have different rates of demand growth. Other economic indicators are also tracked that may be of more interest to specific stakeholders, including total capital costs (investment risk), fuel costs as a fraction of overall cost (price shock risk), and more subjective measures of the risk due to fuel supply interruption.

The basic economic methodology is to use levelized or life-cycle cost analysis, based on the time value of costs and credits over the whole life of a technology or a system analysis period. This is illustrated in Figure 1 below.

The figure shows fixed costs (capital costs, fixed O&M, and end-of-life costs and credits) that do not depend upon how a technology is operated, as well as variable costs or credits (fuel costs, variable O&M, heat sales, etc.) that do depend on how many kWh or km per year are expected. All costs and credits are brought to their equivalent value at t = 0, amortized over the operating life, and divided by the annual production. The figure does not show the interest rate, but this has an important influence based on capital costs and construction period. Long operating life and high interest rates also reduce the effect of any end-of-life costs or credits.

For assessing a single technology the equivalent hours per year of full operation (or capacity factor) are also very important. The level of operation and whether it is constant over plant or vehicle life can play an important role in the choice between capital or fuel intense technologies. For system level studies, operating patterns depend upon technology interactions, based on variable cost, maintenance requirements, and outage rates.

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Figure 1: Levelized cost methodology. Source: Schenler (2016), 5th Internationaler Geothermie Kongress, St. Gallen.

Estimation of the relevant present and forecast future data is based on a broad acquaintance with the relevant technologies, project-specific analysis, contributions (and confirmation) from project partners and stakeholders, and acquired expert judgment. This includes estimating the economies of scale for technologies where the size may vary, as well as the learning curve that is expected due to experience and improvement based on cumulative technology production (e.g. the strong drop in solar panel costs).

For good comparison between technologies, as well as good modeling of system interactions, it is also necessary to have consistency of cross-cutting assumptions such as fuel costs and interest rates. Particularly when working with partners or stakeholders, there is a tendency for data contributors to be more optimistic regarding their own technologies, and a balanced mix of realism and optimism across all technologies is the goal. When the cost results will contribute to a multi-stakeholder debate, then stakeholder acceptance of input data is important to acceptance of analytic results.

The interaction between technology cost results and technological system interactions or operation have already been mentioned above. The difference between average and marginal costs is quite important, because system expansion planning is based on expected average costs (i.e. expected operation), and this drives capital investment (generally constrained). But system operation is based on the variable cost, and the entrance or subsidy of new technologies can disrupt expected future operation. For example, subsidy of renewable energy technologies with very low marginal costs has led to significant systemic problems impacting the Swiss and European power markets. For the transportation sector, shifts in modal choice or driving patterns can lead to similar misinvestment in the vehicle stock and infrastructure.

For such reasons, TA cost analysis always includes sensitivity analysis of the impacts and uncertainty of economic factors contributing to cost results. Figure 2 below shows an example of sensitivity to the various factors contributing to the average generation cost of a Generation III European Power Reactor (EPR). The base values for each factor are shown in the figure’s legend, and these are varied one at a time along the x-axis to produce the so-called spider graph, where the steepness or flatness of each line indicates a factor’s sensitivity or insensitivity. As this figure shows, capital costs, interest rate and operation (load factor) have the largest impacts.

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Figure 2: Sensitivity of average cost to component factors for a Generation III EPR. Source: Hirschberg, S. et al (2012). “Review of current and future nuclear technologies.” PSI report prepared for Swiss Federal Office of Energy

This figure shows the sensitivity of cost to cost components and plant operation (including lifetime), but not how the cost depends upon the actual technology design choices. To achieve this, it is desirable to construct a model that links the physical and economic choices and assumptions. One example of this has been done in a recent project analysis of geothermal generation potential in Switzerland. In addition, this model was further extended by the TA group team to include a life cycle analysis model, as shown in Figure 3 below.

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Figure 3: Coupled Model for Cost and LCA Results. Source: Updated from Hirschberg S., Wiemer S., and Burgherr P., eds. (2015)..

The physical model is based on the circulation loop flow of geothermal fluid down the injection well, through the fractured heat reservoir and back up the production well to the surface generation plant. The plant design choices and (uncertain) geological conditions that depend on site choice affect the plant’s thermal efficiency, gross generation, pumping power, and net energy production. Component costs are scaled with size by a drilling cost model and economies of scale, and LCA inventories are also automatically scaled. This means physical plant choices can be adjusted to automatically produce consistent results for average cost, and environmental burdens like steel consumed and CO2 produced.

Selected Results

Whether technology or system assessment, basically all TA projects include internal cost assessment. Several representative projects have therefore been chosen here to reflect the range and scale of applications, and particular elements of the internal cost methodology described above.

New Energy Externalities Developments for Sustainability (NEEDS)
This was a large-scale, European-funded assessment of the sustainability characteristics of a wide range of electric generation technologies, with 66 different participating institutions. PSI played an integrating role in collecting and harmonizing results from teams analyzing individual technologies, ensuring consistent assumptions, and also combining internal and external costs to produce total costs. Costs and a wide range of other sustainability criteria were also combined in a MCDA analysis that drew its criteria weights from an online PSI survey process, and technology ranks based on these weights were produced using a new MCDA algorithms co-developed by PSI and the International Institute for Applied Systems Analysis. Figure 4 below shows the average MCDA ranks based on survey input weights, as well the total cost components for the full range of technologies considered.

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Figure 4: Total costs and MCDA ranks for NEEDS technologies. Source: Schenler W., Hirschberg W., Burgherr P. Makowski M., Granat J. (2009)

TA-SWISS Geothermal Project
As described above, PSI developed and applied a linked physical-economic-LCA model for geothermal power as part of a multi-stakeholder project performed for Technology Assessment Switzerland, resulting in the book “Energy from the Earth: Geothermal as a Resource for the Future” that covered Swiss resources, technology, Swiss, economics, environment, risk, and public opinion and the Swiss legal framework for future development. Figure 5 below shows cost components for current plant scenarios with 2 and 3 wells (doublets and triplets), as well as results for the previous TA-SWISS project. This figure shows a heat credit that can be considered an optimistic upper bound (7 Rp/kWh of heat is a typical value for the full retail price of district heating), while the total cost including the heat credit is shown by the black diamonds.

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Figure 5: Range of geothermal generation costs for plants with 2 and 3 wells, with and without heat credit. Source: Hirschberg S., Wiemer S., and Burgherr P., eds. (2015).

Technology-centered Electric Mobility Assessment (THELMA)
This project for CCEM and Swiss Electric Research investigated the impacts of electric mobility into the Swiss passenger vehicle fleet. Together with ETH, PSI TA developed drivetrain models for energy demand, based on the combination of vehicle class, drivetrain, fuel, and driving cycles. These vehicles were used to compose a “virtual fleet” of models that were used for fleet scenario analysis. Electric vehicles penetrating the fleet were assigned to specific drivers (“agents”), based on fleet driving behavior modeling performed by other ETH partners. Electric sector impacts and generation mix were based on electric sector modeling also performed by ETH partners, and PSI performed the overall coordination and fleet scenario integration. Figure 6 below shows fleet greenhouse gas emissions components for various fleet scenarios composed of different drivetrain penetration mixes, and different electricity and hydrogen supplies.

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Figure 6: Greenhouse gas emissions for the 2050 Swiss car fleet with component contributions for different fleet technology penetration scenarios. Source: THELMA project report.

Selected Publications