Author: National Research Council (U.S.). Modeling Resource Group Synthesis Panel of the Committee on Nuclear and Alternative Energy SystemsPublisher:
ISBN: 9780309027816
Category : Energy policy
Languages : en
Pages : 0
Book Description
Energy Modeling for an Uncertain Future
Author: National Research Council (U.S.). Modeling Resource Group Synthesis Panel of the Committee on Nuclear and Alternative Energy SystemsPublisher:
ISBN: 9780309027816
Category : Energy policy
Languages : en
Pages : 0
Book Description
Energy Modeling for an Uncertain Future
Author: National Research Council (U.S.). Committee on Nuclear and Alternative Energy Systems. Synthesis Panel. Modeling Resource GroupPublisher:
ISBN: 9780309027816
Category : Energy policy
Languages : en
Pages : 0
Book Description
Energy Modeling for an Uncertain Future
Author: National Research Council (U.S.). Committee on Nuclear and Alternative Energy Systems. Synthesis Panel. Modeling Resource GroupPublisher:
ISBN:
Category : Political Science
Languages : en
Pages : 258
Book Description
Energy, an Uncertain Future
Author: Herman T. FranssenPublisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages : 348
Book Description
Pp. 1.
Addressing an Uncertain Future Using Scenario Analysis
Author: Chris MarnayPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
The Office of Energy Efficiency and Renewable Energy (EERE) has had a longstanding goal of introducing uncertainty into the analysis it routinely conducts in compliance with the Government Performance and Results Act (GPRA) and for strategic management purposes. The need to introduce some treatment of uncertainty arises both because it would be good general management practice, and because intuitively many of the technologies under development by EERE have a considerable advantage in an uncertain world. For example, an expected kWh output from a wind generator in a future year, which is not exposed to volatile and unpredictable fuel prices, should be truly worth more than an equivalent kWh from an alternative fossil fuel fired technology. Indeed, analysts have attempted to measure this value by comparing the prices observed in fixed-price natural gas contracts compared to ones in which buyers are exposed to market prices (see Bolinger, Wiser, and Golove and (2004)). In addition to the routine reasons for exploring uncertainty given above, the history of energy markets appears to have exhibited infrequent, but troubling, regime shifts, i.e., historic turning points at which the center of gravity or fundamental nature of the system appears to have abruptly shifted. Figure 1 below shows an estimate of how the history of natural gas fired generating costs has evolved over the last three decades. The costs shown incorporate both the well-head gas price and an estimate of how improving generation technology has gradually tended to lower costs. The purpose of this paper is to explore scenario analysis as a method for introducing uncertainty into EERE's forecasting in a manner consistent with the preceding observation. The two questions are how could it be done, and what is its academic basis, if any. Despite the interest in uncertainty methods, applying them poses some major hurdles because of the heavy reliance of EERE on forecasting tools that are deterministic in nature, such as the Energy Information Administration's (EIA's) National Energy Modeling System (NEMS). NEMS is the source of the influential Annual Energy Outlook whose business-as-usual (BAU) case, the Reference Case, forms the baseline for most of the U.S. energy policy discussion. NEMS is an optimizing model because: 1. it iterates to an equilibrium among modules representing the supply, demand, and energy conversion subsectors; and 2. several subsectoral models are individually solved using linear programs (LP). Consequently, it is deeply rooted in the recent past and any effort to simulate the consequences of a major regime shift as depicted in Figure 1 must come by applying an exogenously specified scenario. And, more generally, simulating futures that lie outside of our recent historic experience, even if they do not include regime switches suggest some form of scenario approach. At the same time, the statistical validity of scenarios that deviate significantly outside the ranges of historic inputs should be questioned.
The National Energy Modeling System
Author: National Research CouncilPublisher: National Academies Press
ISBN: 0309046343
Category : Science
Languages : en
Pages : 165
Book Description
This book addresses the process and actions for developing enhanced capabilities to analyze energy policy issues and perform strategic planning activities at the U.S. Department of Energy (DOE) on an ongoing basis. Within the broader context of useful analytical and modeling capabilities within and outside the DOE, this volume examines the requirements that a National Energy Modeling System (NEMS) should fulfill, presents an overall architecture for a NEMS, identifies data needs, and outlines priority actions for timely implementation of the system.
Energy in America's Future
Author: Sam H. SchurrPublisher: Routledge
ISBN: 1135985812
Category : Business & Economics
Languages : en
Pages : 574
Book Description
Results of a comprehensive two-year study analyzing the facts and policy alternatives. Originally published in 1979.
Energy Systems Optimization Considering the Uncertainty of Future Developments
Author: Wolf Gereon WedelPublisher: BoD – Books on Demand
ISBN: 3759718280
Category : Technology & Engineering
Languages : en
Pages : 282
Book Description
In light of anthropogenic climate change and the importance of energy to ensure high living standards, energy system optimization is used to explore different energy system layouts. A recent focus has been on determining cost-effective ways to mitigate greenhouse gas emissions. This work investigates how future uncertainties regarding technology costs influence optimization results. This is achieved through energy system optimization aimed at reducing system cost using stochastic optimization with probability distributions to capture expected future costs and uncertainties. Theoretical considerations and a minimal example energy system show that Jensen's inequality leads to an overestimation of necessary system costs when scenario optimization considers only the expected technology cost means. Stochastic optimization is applied to a model of the German energy system, including the electricity, heating, and transport sectors. Results from stochastic optimization are compared to scenario results based on mean cost distributions. The use of a factor effect-based meta-model and fewer optimizations in stochastic analysis are investigated to reduce computational effort. The results confirm the overestimation of necessary costs by scenario optimization, showing a 3.5% overestimation with an 80% emission reduction target and 0.4% for a completely renewable system. Stochastic optimization also provides the interquartile range to characterize uncertainty, with a 13.2 Euro MWh-1 interquartile range (27.3% of the mean) for a completely renewable system. Using 30 to 60 optimizations in the stochastic case yields results similar to 500 optimizations, the benchmark. The proposed meta-models offer limited advantages except for predicting extreme results, which are not evident with fewer optimizations. In some cases, especially for non-renewable systems, the expected values from stochastic optimization differ significantly from scenario optimization results. For instance, at a 20% emission limit of 1990 levels, scenario optimization yields 18% of the CO2 emissions compared to the mean of stochastic optimization. Similar differences are seen in other parameters, though most are well-represented by scenario results. Clustering helps manage the diverse results from stochastic optimization by identifying underlying system layouts. Stochastic optimization with probability distributions is robust, with small changes to distributions having minimal impact on outcomes.
The Role of Energy Models
Author: Tara Kathleen RighettiPublisher:
ISBN:
Category :
Languages : en
Pages : 22
Book Description
When designing environmental protection and energy regulation policies, legislators and regulators rely upon the results of computer models that purport to forecast future conditions such as energy supply, demand, available technologies and market characteristics. In a perfect world, these energy models would prove to be reliable and would, in turn, yield projections that would enable legislators and regulators to confidently enact regulations that advance societal energy and environmental goals. Unfortunately, it is impossible to predict or forecast with confidence all the variables that influence regulation and the effects of any regulatory choice. In this Article, we suggest that principles of dynamic law can be used as guidance to design policy that is coherent with the highly uncertain context in which it operates. We explore the idea that the uncertainty surrounding the outcomes of a regulation can be taken into account and made part of the regulatory design. In so doing, we suggest that regulations can tackle uncertainty using the same methods by which the energy modeling community attempts to understand and bound uncertainty. The diverse set of projected regulatory effects produced by different models under different assumptions reveals risks and opportunities: The risk of ineffective regulation and unintended consequences; and the opportunity of making “dynamic regulations” that change with the pace of new information.
Validation and Assessment of Energy Models
Author: Saul I. GassPublisher:
ISBN:
Category : Energy policy
Languages : en
Pages : 268
Book Description