Article

Energy sector portfolio analysis with uncertainty

Authors:
  • Lumina Decision Systems, Inc
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Abstract

Governments are dealing with the challenge of how to efficiently invest in research and development portfolios related to energy technologies. Research and development investment decisions in the energy space are especially difficult due to numerous risks and uncertainties, and due to the complexity of energy’s interactions with the broad economy. Historically, much of the U.S. Department of Energy’s in-depth research and development analyses focused on assessing the impact of a research and development activity in isolation from other available opportunities and did not substantially consider risk and uncertainty. Endeavoring to combine integrated energy-economy modeling with uncertainty analysis and technology-specific research and development activities, the U.S. Department of Energy commissioned the development of the Stochastic Energy Deployment System to support and improve public energy research and development decision-making. The Stochastic Energy Deployment System draws from expert-elicited probability distributions for research and development-driven improvements in technology cost and performance, and it uses Monte Carlo simulations to evaluate the likelihood of outcomes within a system dynamics energy-economy model. The framework estimates the uncertain benefits and costs of various research and development portfolios and provides insight into the probability of meeting national technology goals, while accounting for interactions with the larger economy and for interactions among research and development investments spanning many energy sectors.

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This paper considers the effect of several key parameters of low carbon energy technologies on the cost of abatement. A methodology for determining the minimum level of performance required for a parameter to have a statistically significant impact on CO2 abatement cost is developed and used to evaluate the impact of eight key parameters of low carbon energy supply technologies on the cost of CO2 abatement. The capital cost of nuclear technology is found to have the greatest impact of the parameters studied. The cost of biomass and CCS technologies also have impacts, while their efficiencies have little, if any. Sensitivity analysis of the results with respect to population, GDP, and CO2 emission constraint show that the minimum performance level and impact of nuclear technologies is consistent across the socioeconomic scenarios studied, while the other technology parameters show different performance under higher population, lower GDP scenarios. Solar technology was found to have a small impact, and then only at very low costs. These results indicate that the cost of nuclear is the single most important driver of abatement cost, and that trading efficiency for cost may make biomass and CCS technologies more competitive.
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In the present paper we use the output of multiple expert elicitation surveys on the future cost of key low-carbon technologies and use it as input of three Integrated Assessment models, GCAM, MARKAL_US and WITCH. By means of a large set of simulations we aim to assess the implications of these subjective distributions of technological costs over key model outputs. We are able to detect what sources of technology uncertainty are more influential, how this differs across models, and whether and how results are affected by the time horizon, the metric considered or the stringency of the climate policy. In unconstrained emission scenarios, within the range of future technology performances considered in the present analysis, the cost of nuclear energy is shown to dominate all others in affecting future emissions. Climate-constrained scenarios, stress the relevance, in addition to that of nuclear energy, of biofuels, as they represent the main source of decarbonization of the transportation sector and bioenergy, since the latter can be coupled with Carbon Capture and Storage (CCS) to produce negative emissions.
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The Balanced Scorecard was developed to measure both current operating performance and the drivers of future performance. Many managers believe they are using a Balanced Scorecard when they supplement traditional financial measures with generic, non-financial measures about customers, processes, and employees. But the best Balanced Scorecards are more than ad hoc collections of financial and non-financial measures. The objectives and measures on a Balanced Scorecard should be derived from the business unit's strategy. A scorecard should contain outcome measures and the performance drivers of those outcomes, linked together in causeand-effect relationships.
Conference Paper
When managing technology portfolios like the Small Business Innovative Research (SBIR) Program, NASA makes a considerable effort to gauge the risk and cost as guidance factors for balancing a program. Metrics like the Technology Readiness Level (TRL) are used to determine the maturity of a given technology and thereby provide an assessment of the required steps and funding needed to infuse the technology. Analysis of funded SBIR projects indicates that a newly developed metric described in this paper that can be a proxy for “benefit to NASA” shows strong correlation to receiving Phase II awards. We examine this correlation to determine which additional metrics might better assess the potential programmatic reward for investing in a given technology. By examining a pool of proposals with high technical merit that are initially recommended for funding, we have developed a metric known as the “Technology Impact” that seems to have a good correlation with proposals selected for award. This reward potential or impact of investing in a new technology is divided into two factors. The first is the value of the missions or programs impacted. For NASA the value of all funds associated with the creation of or the contribution to the program is the “market”. Depending on the specific technology the size of the market could be a component, an instrument, a service or even an entire flight mission. The second factor is the leverage a new technology will have on the value of the impacted missions. As TRL acts as a proxy for risk, we propose an analogous proxy for reward called the Technology Leverage Factor (TLF) as a measure of the potential leverage a technology can have for creating the market. TLF is a measure that relates to the market contribution of the new technology. This can range from a 1% contribution for a component, a 10% contribution for an instrument, to a 100% contribution for a mission enabling technology. We use t- e market size estimate and TLF to demonstrate how these can be used to create risk reward metrics to be used in conjunction with TRL for technology portfolio management.
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This paper addresses the capital budgeting problem under uncertainty. In particular, we propose a multistage stochastic programming model aimed at selecting and managing a project portfolio. The dynamic uncertain evolution of each project value is modelled by a scenario tree over the planning horizon. The model allows the decision maker to revise decisions by decommitting from a given project if it shows a negative performance. Risk is explicitly assessed by defining a mean-risk objective function, where the conditional value at risk is used. A customized branch-and-bound method is also introduced for solving the proposed model. Extensive computational experiments have been carried out to validate the model effectiveness, also in comparison with other possible benchmark policies. The numerical results collected by solving randomly generated instances with the proposed branch-and-bound approach seems to be encouraging.
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In a relatively short period of time Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. DEA has been successfully applied to a host of different types of entities engaged in a wide variety of activities in many contexts woridwide. This chapter discusses the fundamental DEA models and some of their extensions.
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Since there is a mismatch between long-term basic research and short-term financial markets, the dependency of biotechnology (biotech) start-ups on strategic partnerships with pharmaceutical companies is expanding more than on venture capital. The research objectives are to examine the difference between license-fee elements, try to determine the innovation valuation in strategic partnerships, and minimise the risk in partnership formation for both sides. A key concept is that a biotech start-up is defined as a portfolio of real options based on an entrepreneur's ideas about investment opportunity. Methodologies used are compound options and stochastic optimisation for innovative but risky projects. In conclusion, this paper tries to address a wider scope with a deeper theoretical grounding, by using the real options perspective for the valuation of innovation partnership, through a biotech license-fee case study for simulation. Furthermore, the real option valuation can be expected to improve open innovation from matchmaking to innovation partnership.
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The boxplot plot has been around for over 40 years. This paper summarises the improvements, exten-sions and variations since Tukey first introduced his "schematic plot" in 1970. We focus particularly on richer displays of density and extensions to 2d.
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Many low carbon energy technologies are non-dispatchable, which imposes additional costs over and above the cost of the base technology when these technologies are connected to the grid. This paper examines the impact of assumptions about these grid integration costs on the optimal Research and Development (R&D) portfolio for minimizing the cost of climate change. This paper’s goal is not an in-depth analysis of the drivers of grid integration costs, but rather to place bounds on the size of the problem, and to determine under what circumstances integration costs are relevant to policy design. This research finds that in the absence of a budget constraint the optimal R&D portfolio is affected by assumptions about grid integration costs, but given a budget constraint, assumptions about grid integration costs have little impact on the composition of the optimal R&D portfolio. This finding implies that the importance of getting grid integration costs right depends on the specific question that is being asked – how to allocate a given budget, or what the size of the budget should be.
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Operations management methods have been applied profitably to a wide range of technology portfolio management problems, but have been slow to be adopted by governments and policy makers. We develop a framework that allows us to apply such techniques to a large and important public policy problem: energy technology R&D portfolio management under climate change. We apply a multi-model approach, implementing probabilistic data derived from expert elicitations into a novel stochastic programming version of a dynamic integrated assessment model. We note that while the unifying framework we present can be applied to a range of models and data sets, the specific results depend on the data and assumptions used and therefore may not be generalizable. Nevertheless, the results are suggestive, and we find that the optimal technology portfolio for the set of projects considered is fairly robust to different specifications of climate uncertainty, to different policy environments, and to assumptions about the opportunity cost of investing. We also conclude that policy makers would do better to over-invest in R&D rather than under-invest. Finally, we show that R&D can play different roles in different types of policy environments, sometimes leading primarily to cost reduction, other times leading to better environmental outcomes.
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Expert elicitations are critical tools for characterizing technological uncertainty, since historical data on technical progress may not provide a sufficient basis for forecasting future advances. The objectives of this paper are to describe the protocol and results for an expert elicitation on the future performance of gas-turbine-based technologies in the electric power sector and to discuss how these insights relate to the current elicitation literature in energy modeling. Elicitation results suggest that prospective efficiency gains are likely to be slower than historical trends; however, the assessed values are still appreciably higher than the efficiencies used in many energy models. The results also indicate that conducting face-to-face elicitations may be important for minimizing overconfidence and for critically examining reported values, especially when assessing non-central probabilities in the tails of a distribution.