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The empirical evidence from a growing body of academic literature clearly suggests that oil price increases and volatility dampen macroeconomic growth by raising inflation and unemployment and by depressing the value of financial and other assets. Surprisingly, this issue seems to have received little attention from energy policy makers. In percentage terms, the oil–GDP effect is relatively small, producing losses in the order of 0.5% of GDP for a 10% oil price increase. In absolute terms however, even a 10% oil price rise—oil has risen at least 50% in the last year alone—produces GDP losses that, could they have been averted, would significantly offset the cost of increased RE deployment. This paper draws on the empirical oil–GDP literature, which we summarize, to show that (i) by displacing gas and oil, renewable energy investments can help nations avoid costly macroeconomic losses produced by the oil–GDP effect and, (ii) that these avoided losses represent a significant external macroeconomic benefit of such investments. We show that a 10% increase in RE share avoids GDP losses in the range of $29–$53 billion in the US and the EU ($49–$90 billion for OECD). These avoided losses offset one-fifth of the RE investment needs projected by the EREC and half the OECD investment projected by a G-8 Task Force. For the US, the figures further suggest that each additional kW of renewables, on average, avoids $250–$450 in GDP losses, a figure that varies across technologies as a function of annual capacity factors. We approximate that the offset is worth $200/kW for wind and solar and $800/kW for geothermal and biomass. While we focus only on renewables, the GDP offset will apply in some measure to other non-fossil technologies including energy efficiency, DSM and nuclear. The societal valuation of non-fossil alternatives must reflect these avoided GDP losses, whose benefit is not fully captured by private investors. This said, we fully recognize that wealth created in this manner does not directly form a pool of public funds that is easily earmarked for renewables support. Finally, the oil–GDP relationship has important implications for correctly estimating direct electricity generating cost for conventional and renewable alternatives and for developing more useful energy security and diversity concepts. We also address these issues.

Renewable generating technologies offer an effective means for climate change mitigation. Policy makers, however, are wary because of the widespread perception that these technologies cost more than conventional alternatives so that increasing their deployment will raise overall electricity generating costs. Energy planning represents an investment-decision problem. Investors commonly evaluate such problems using portfolio theory to manage risk and maximize portfolio performance under a variety of unpredictable economic outcomes. Energy planners need to similarly abandon their reliance on traditional, “least-cost” stand-alone kWh generating cost measures and instead evaluate conventional and renewable energy sources on the basis of their portfolio cost – their cost contribution relative to their risk contribution to a mix of generating assets. Energy security generally focuses on the threat of abrupt supply disruptions. This paper suggests a more profound aspect: mitigating fossil price volatility. An extensive body of research indicates that fossil volatility significantly disrupts the economies of consuming nations, potentially exacting hundreds of billions of dollars from the US and EU economies alone. Energy security is reduced when countries hold inefficient portfolios that are needlessly exposed to fossil price risks. This paper describes essential portfolio-theory ideas and uses three case studies to illustrate how electricity-generating mixes can benefit from additional shares of wind, geothermal and other renewables. Compared to existing, fossil-dominated mixes, efficient portfolios reduce generating cost while including greater renewables shares in the mix thereby enhancing energy security. Though counter-intuitive, the idea that adding more costly renewables can actually reduce portfolio-generating cost is consistent with basic finance theory. An important implication is that in dynamic and uncertain environments, the relative value of generating technologies must be determined not by evaluating alternative resources, but by evaluating alternative resource portfolios.

In Europe and the US, national energy planning agencies value resource alternatives using outmoded techniques, conceived around the time of the Model-T Ford. These models, long since discarded in manufacturing and other industries, bias in favor of riskier fossil alternatives while understating the true value of photovoltaics (PV) and similar low-risk, passive, capital-intensive technologies. PV and similar renewables offer a unique cost-risk menu along with other valuable attributes that traditional valuation models, conceived long before such attributes became technologically feasible, cannot “see” because they are steeped in the vocabulary and measurement concepts of a different technological era. Properly understood and exploited, the attributes of PV could undoubtedly form the basis for reengineering the electricity production and delivery process to deliver cost reductions in ways that can yet not be imagined. Lenders and investors likewise do not yet fully understand the unique financial properties of PV as differentiated from traditional resource alternatives. Policy makers have a responsibility to broaden the analytic horizons to include new valuation models and concepts that more properly reflect the unique attributes of PV.

The challenge of public policy in the transition is to bring the benefits of competition to consumers as soon as possible, while allowing shareholders to recover those costs that were truly incurred as a direct consequence of prior regulatory policies. Securitization, a part of the transition package, does less than its advocates say, but more than nothing.

For most of this century the electric utility industry has operated as a regulated natural monopoly which produced a commodity product in an environment of low technological progress and sold it to a homogeneous captive market. Given these conditions, the primary capital budgeting issues involved the timing of capacity additions and the mix of base load and peaking capacity. This relatively simple and stable situation, however, has been eroded by market, regulatory and technological changes. As a consequence, old embedded capital budgeting approaches are hindering the pursuit of efficiency with respect to the production and distribution of electric energy. Our purpose in this paper is to assess traditional utility capital budgeting procedures and to explore possibilities for improvement. We begin with a review of traditional capital budgeting decision processes. Next, we overview the changes that have emerged over the last two decades and explore capital budgeting processes more appropriate to this ‘early competitive environment’. Finally, we project the changes a bit further and explore the capital budgeting techniques that may be useful in an ‘advanced competitive environment’.

Making incorrect decisions in this period of restructuring transition could leave utilities with more inefficient plant or costly purchased-power contracts, either of which will increase stranded investment problems. Proper market-based valuation is not just a monopoly-based IRP issue: It affects decision making in a competitive environment even more profoundly.

DSM and energy-efficiency options are generally evaluated using engineering oriented discounted cash flow procedures which value all cost and benefit streams using a single arbitrary discount rate — usually the firm's weighted average cost of capital (WACC). The WACC based approach ignores financial risk and hence leads to unreliable results. In a recent paper in this journal Hassett and Metcalf depart from this tradition; they reflect financial risk by simulating the total variability of returns from energy saving investments. Their findings indicate that DSM projects are potentially risky, suggesting that the net benefits must be discounted at significantly higher rates, thus lowering their present values. This paper presents a market based evaluation of a particular DSM option — residential fuel switching — using the capital asset pricing model (CAPM). Under the CAPM approach, a risk adjusted, market based discount rate is estimated for each cost stream — fuel, maintenance and avoided outlays for electricity. The market based results, which reflect the systematic risk of each cost stream, suggest that fuel switching is probably not cost-effective because it is considerably more risky than indicated by a WACC based analysis. This supports Hassett and Metcalf's conclusion that consumers who reject DSM projects are not necessarily acting irrationally.

Integrated resource planning, as currently practiced, has a fatal flaw: the revenue-requirements method of evaluating new capacity options improperly ignores risk features. When risk is properly accounted for, the cost of supposed ‘high-cost’ resources—photovoltaics, for example—drops dramatically while the cost of supposed ‘low-cost’ resources like natural gas-fired generation goes up.

Utility planning models evaluate alternative generating options using the revenue requirements method—an engineering-oriented, discounted cash-flow (DCF) methodology that has been widely used for over three decades. Discounted cash-flow techniques were conceived in the context of active expense-intensive technologies, such as conventional, fuel-intensive power generation. Photovoltaic (PV) technology, by contrast, is passive and capital intensive—attributes that are similar to those of other new process technolgies, such as computer-integrated manufacturing. Discounted cash-flow techniques have a dismal record for correctly valuing new technologies with these attributes, in part because their benefits cannot be easily measured using tradiational accounting concepts. This paper examines how these issues affect cost measurement in both conventional and PV-based electricity, and presents kWh-cost estimates for three technologies (coal, gas and PV) using risk-adjusted approaches, which suggest that PV costs are generally equivalent to the gas/combined cycle and about twice the cost of base-load coal (environmental externalities are ignored). Finally, the paper evaluates independent power purchases for a typical US utility and finds that in such a setting the cost of PV-based power is comparable to the firm's published avoided costs.

Depreciation allowances directly affect the economic returns of regulated firms yet are frequently based on inappropriate accounting concepts of value and obsolescence. Regulated rates are correctly set only under economic (Hotelling) depreciation which turns out to be an accelerated recovery schedule for assets with high rates of technological progress. Nonetheless, regulators widely use straight-line or even back-loaded schedules for fear that accelerated recovery will raise rates paid by consumers. Using a multiasset model it is shown that, contrary to widely held beliefs, accelerated depreciation leads to lower regulated rates for mature firms in the steady state.

This paper examines the widely-held notion that the regulatory approach to setting earnings equalizes the accounting and internal rates of return, and that this condition holds for any depreciation profile. The evidence suggests that the practical usefulness of this proposition for policy-making may be quite limited. This paper also examines the underlying premise—that, because of its relative constancy over time, the regulated rate of return must equal the internal rate of return—and finds that contrary to earlier suggestions, this result does not always hold in the multi-asset case.

Publisher Summary This chapter introduces the intuition and theory underpinning the application of Mean-Variance Portfolio theory to the power sector. Mean-Variance Portfolio theory can usefully complement traditional valuation approaches to inform a country or utility on the optimal generation portfolio to minimize the impact of some critical risks in liberalized power markets. Electric utilities operating in liberalized markets are faced with a wide range of risks and uncertainties when evaluating different generation technologies. While many of the risks facing power producers in liberalized electricity markets existed in the fully regulated and vertically integrated industry, investors can no longer pass these costs on to consumers or taxpayers automatically. The traditional “levelized cost” valuation approach was well adapted to assess power investments prior to liberalization. The levelized cost methodology remains widely used in the liberalized industry, both by energy planners and by electric companies. Most importantly, the levelized cost methodology values investment projects on a stand-alone basis. The various generation technologies have different risk–return profiles, and there are potentially great advantages in operating a diversified portfolio of plants for a utility. Because it does not take into account the complementarity in the risk–return profiles of different plants that a utility operates, the levelized cost methodology cannot inform a utility or country on the optimal technological choice for an additional power plant, given the current portfolio that a utility or country operates. The expected risk of an electricity portfolio is a weighted average of the risks of the individual technology costs, tempered by their correlations or covariances.

Publisher Summary Energy diversity and security are evaluated using Stirling's Multi-criteria Diversity Analysis (MDA) as well as more classical Markowitz Mean Variance Portfolio (MVP) theory. Each of these approaches is capable of producing an Efficient Frontier (EF) that shows optimal generating mixes—those that maximize performance (i.e. minimize cost) while minimizing risk or uncertainty (i.e. maximizing diversity). MDA covers the full spectrum of “incertitude,” reaching into areas where little is known about the range of possible outcomes, let alone their probabilities. However, MDA does not exploit statistical information that is available in certain parts of the risk spectrum where historic means, variances, and co-variances of outcomes are known as well as are used to make inferences about the future. MVP operates precisely in this space, although, like other capital market models, its prescriptive value rests on the idea that the past is the best guide to the future and that. As such MVP can be blind to unforeseen events that create future structural change.

Energy planning represents an investment-decision problem. Investors commonly evaluate such problems using portfolio theory to manage risk and maximize portfolio performance under a variety of unpredictable economic outcomes. Energy planners need to similarly abandon their reliance on traditional, ''least-cost'' stand-alone technology cost estimates and instead evaluate conventional and renewable energy sources on the basis of their portfolio cost--their cost contribution relative to their risk contribution to a mix of generating assets. This report describes essential portfolio-theory ideas and discusses their application in the Western US region. The memo illustrates how electricity-generating mixes can benefit from additional shares of geothermal and other renewables. Compared to fossil-dominated mixes, efficient portfolios reduce generating cost while including greater renewables shares in the mix. This enhances energy security. Though counter-intuitive, the idea that adding more costly geothermal can actually reduce portfolio-generating cost is consistent with basic finance theory. An important implication is that in dynamic and uncertain environments, the relative value of generating technologies must be determined not by evaluating alternative resources, but by evaluating alternative resource portfolios. The optimal results for the Western US Region indicate that compared to the EIA target mixes, there exist generating mixes with larger geothermal shares at equal-or-lower expectedmore » cost and risk.« less

The empirical evidence from a growing body of academic literature clearly suggests that oil price increases and volatility dampen macroeconomic growth by raising inflation and unemployment and by depressing the value of financial and other assets. Surprisingly, this issue seems to have received little attention from energy policy makers. In percentage terms, the Oil-GDP effect is relatively small, producing losses in the order of 0.5% of GDP for a 10% oil price increase. In absolute terms however, even a 10% oil price rise. and oil has risen at least 50% in the last year alone. produces GDP losses that, could they have been averted, would significantly offset the cost of increased RE deployment. While we focus on renewables, the GDP offset applies equally to energy efficiency, DSM and nuclear and other non-fossil technologies. This paper draws on the empirical Oil-GDP literature, which we summarize, to show that by displacing gas and oil, renewable energy investments can help nations avoid costly macroeconomic losses produced by the Oil-GDP effect. We show that a 10% increase in RE share avoids GDP losses in the range of $29.$53 billion in the US and the EU ($49.$90 billion for OECD). These avoided losses offset one-fifth of the RE investment needs projected by the EREC and half the OECD investment projected by a G-8 Task Force. For the US, the figures further suggest that each additional kW of renewables, on average, avoids $250.$450 in GDP losses, a figure that varies across technologies as a function of annual capacity factors. We approximate that the offset is worth $200/kW for wind and solar and $800/kW for geothermal and biomass (and probably nuclear). The societal valuation of non-fossil alternatives needs to reflect the avoided GDP losses, whose benefit is not fully captured by private investors. This said, we fully recognize that wealth created in this manner does not directly form a pool of public funds that is easily earmarked for renewables support. Finally, the Oil-GDP relationship has important implications for correctly estimating direct electricity generating cost for conventional and renewable alternatives and for developing more useful energy security and diversity concepts. We also address these issues.

This paper highlights the potential benefits derived from deploying new methodologies in technology assessment and risk management that drive strategic technological and infrastructural investments by power utilities in response to internal and external contingencies they are confronted with in a rapidly deregulating, increasingly competitive environment. The role of technological learning in helping to continually re-shape decision making criteria and worldviews is illustrated and lends into formulating some ideas about how to integrate strategic (qualitative) with financial (quantitative) criteria in valuating architectural innovations such as the virtual utility concept. Moreover, the paper explores the nature of the different kinds of technological innovation, architectural, radical, generational, and incremental, and then illustrates-using examples from diverse technology-driven industries, such as the information technology, pharmaceuticals, and industrial chemicals industries-how capital budgeting, because of its narrow accounting orientation often fails to capture and qualify strategic benefits associated with emerging technologies, thus impeding the diffusion and adoption of technological innovations.

Traditionally, the electric utility industry has been structured as a vertically integrated regulated monopoly. This began to change in the 1980s with the privatization of the industry in the United Kingdom. One important aim of the U.K. privatization was to introduce competition into an industry that previously had been a publicly owned monopoly. A similar process of attempting to open up the industry to competition was set in motion in the United States with the Energy Policy Act of 1992. However, the process is far from complete and agreement as to the structure of the industry currently does not exist. This is nowhere more apparent than in the problem of the organization and ownership of the transmission function, which plays a critical role in enabling competition to take place. Thus, this paper is concerned with governance of transmission networks.

This paper examines the problems associated with the application of traditional capital budgeting practices to the valuation of radically new processes and technologies in an environment of rapid technological change. It focuses on a restructured utility industry, and the possible emergence of the Virtual Utility.

The Context: What Is the Virtual Utility? The current transformation of the US electric utility industry is not dissimilar to the significant changes undergone by US manufacturers over the last two decades. During this period the industry has changed radically by abandoning previous mass-production protocols and adopting flexible, computer-integrated or “just in time” manufacturing.1 A considerable body of literature has investigated this transformation. The virtual utility (VU) is a flexible collaboration of independent, market-driven entities that provide efficient energy service demanded by consumers without necessarily owning the corresponding assets. The VU becomes a metaphor for lean, flexible electricity production/delivery and flexible, customer-oriented energy service provision. The VU construct provides a context for examining the issues surrounding the current transformation of the industry in part by adapting and using important concepts developed in the “new manufacturing.”

The U.S. Coast Guard has been given responsibility for protection of the marine environment within navigable waters. These responsibilities have resulted in the need for an environment management information system. The work reported in this paper is one aspect of such a system, assessing the value of regression analysis in allocating resources to the operations of field personnel in the Coast Guard.

This note identifies instances where nonlinearities may occur in the application of goal programming. An illustration is presented and implications discussed.

Utility company managers (and most business people) are often surprised to learn that accounting income and rate-of-return measures have little economic significance and do not offer much insight into the profitability of a venture or firm. Why the serious shortcomings of the accounting earnings measure are not more widely recognized is quite puzzling in view of the fact that the academic literature has been dealing with this problem for at least 25 years.1 So significant are the errors associated with the accounting rate of return (ARR), according to some researchers, that it “provide[s] almost no information about the [true] economic rates of return” of American corporations (Fisher and McGowan 1983, 82). Nonetheless, the ARR continues to be widely followed, and, in the regulatory context, forms the basis for ratemaking, a role for which it was certainly never intended and for which it is poorly suited (Awerbuch 1986, 20–21).