1,721,016 research outputs found
A stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control
Get PDFWe present a numerical method for finite-horizon stochastic optimal control models. We derive a stochastic minimum principle (SMP) and then develop a numerical method based on the direct solution of the SMP. The method combines Monte Carlo pathwise simulation and non-parametric interpolation methods. We present results from a standard linear quadratic control model, and a realistic case study that captures the stochastic dynamics of intermittent power generation in the context of optimal economic dispatch models.National Science Foundation (U.S.) (Grant 1128147)United States. Dept. of Energy. Office of Science (Biological and Environmental Research Program Grant DE-SC0005171)United States. Dept. of Energy. Office of Science (Biological and Environmental Research Program Grant DE-SC0003906
A stochastic multiscale model for electricity generation capacity expansion
No full textLong-term planning for electric power systems, or capacity expansion, has traditionally been modeled using simplified models or heuristics to approximate the short-term dynamics. However, current trends such as increasing penetration of intermittent renewable generation and increased demand response requires a coupling of both the long and short term dynamics. We present an efficient method for coupling multiple temporal scales using the framework of singular perturbation theory for the control of Markov processes in continuous time. We show that the uncertainties that exist in many energy planning problems, in particular load demand uncertainty and uncertainties in generation availability, can be captured with a multiscale model. We then use a dimensionality reduction technique, which is valid if the scale separation present in the model is large enough, to derive a computationally tractable model. We show that both wind data and electricity demand data do exhibit sufficient scale separation. A numerical example using real data and a finite difference approximation of the Hamilton–Jacobi–Bellman equation is used to illustrate the proposed method. We compare the results of our approximate model with those of the exact model. We also show that the proposed approximation outperforms a commonly used heuristic used in capacity expansion models
Modeling adaptation as a flow and stock decision with mitigation
Get PDFAn effective policy response to climate change will include, among other things, investments in lowering greenhouse gas emissions (mitigation), as well as short-term temporary (flow) and long-lived capital-intensive (stock) adaptation to climate change. A critical near-term question is how investments in reducing climate damages should be allocated across these elements of a climate policy portfolio, especially in the face of uncertainty in both future climate damages and also the effectiveness of yet-untested adaptation efforts. We build on recent efforts in DICE-based integrated assessment modeling approaches that include two types of adaptation—short-lived flow spending and long-lived depreciable adaptation stock investments—along with mitigation, and we identify and explore the uncertainties that impact the relative proportions of policies within a response portfolio. We demonstrate that the relative ratio of flow adaptation, stock adaptation, and mitigation depend critically on interactions among: 1) the relative effectiveness in the baseline of stock versus flow adaptation, 2) the degree of substitutability between stock and flow adaptation types, and 3) whether there exist physical limits on the amount of damages that can be reduced by flow-type adaptation investments. The results indicate where more empirical research on adaptation could focus to best inform near-term policy decisions, and provide a first step towards considering near-term policies that are flexible in the face of uncertainty
Second-best instruments for near-term climate policy: Intensity targets vs. the safety valve
Get PDFCurrent proposals for greenhouse gas emissions regulations in the United States mainly take the form of emissions caps with tradable permits. Since Weitzman's (1974) [3] study of prices vs. quantities, economic theory predicts that a price instrument is superior under uncertainty in the case of stock pollutants. Given the general belief in the political infeasibility of a carbon tax in the US, there has been recent interest in two other policy instrument designs: hybrid policies and intensity targets. We extend the Weitzman model to derive an analytical expression for the expected net benefits of a hybrid instrument under uncertainty. We compare this expression to one developed by Newell and Pizer (2006) [6] for an intensity target, and show the theoretical minimum correlation between GDP and emissions required for an intensity target to be preferred over a hybrid. In general, we show that unrealistically high correlations are required for the intensity target to be preferred to a hybrid, making a hybrid a more practical instrument in practice. We test the predictions by performing Monte Carlo simulation on a computable general equilibrium model of the US economy. The results are similar, and we show with the numerical model that when marginal abatement costs are non-linear, an even higher correlation is required for an intensity target to be preferred over a safety valve.Doris Duke Charitable Foundatio
Technology variation vs. R&D uncertainty: What matters most for energy patent success?
No full textR&D is an uncertain activity with highly skewed outcomes. Nonetheless, most recent empirical studies and modeling estimates of the potential of technological change focus on the average returns to research and development (R&D) for a composite technology and contain little or no information about the distribution of returns to R&D – which could be important for capturing the range of costs associated with climate change mitigation policies – by individual technologies. Through an empirical study of patent citation data, this paper adds to the literature on the outcomes of energy R&D by focusing on the behavior of the most successful innovations for six energy technologies, allowing us to determine whether uncertainty or differences in technologies matter most for success. We highlight two key results. First, we compare the results from an aggregate analysis of six energy technologies to technology-by-technology results. Our results show that existing work that assumes diminishing returns but assumes one generic technology is too simplistic and misses important differences between more successful and less successful technologies. Second, we use quantile regression techniques to learn more about patents that have a high positive error term in our regressions – that is, patents that receive many more citations than predicted based on observable characteristics. We find that differences across technologies, rather than differences across quantiles within technologies, are more important. The value of successful technologies persists longer than those of less successful technologies, providing evidence that success is the culmination of several advances building upon one another, rather than resulting from one single breakthrough. Diminishing returns to research activities appear most problematic during rapid increases of research investment, such as experienced by solar energy in the 1970s.National Science Foundation (U.S.) (Grant 0825915
Impact of unit commitment constraints on generation expansion planning with renewables
No full textGrowing use of renewables pushes thermal generators against operating constraints - e.g. ramping, minimum output, and operating reserves - that are traditionally ignored in expansion planning models. We show how including such unit-commitment-derived details can significantly change energy production and optimal capacity mix. We introduce a method for efficiently combining unit commitment and generation expansion planning into a single mixed-integer optimization model. Our formulation groups generators into categories allowing integer commitment states from zero to the installed capacity. This formulation scales well, runs much faster (e.g. 5000×) than individual plant binary decisions, and makes the combined model computationally tractable for large systems (hundreds of generators) at hourly time resolutions (8760 hours) using modern solvers on a personal computer. We show that ignoring these constraints during planning can result in a sub-optimal capacity mix with significantly higher operating costs (17%) and carbon emissions (39%) and/or the inability to meet emissions targets
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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