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| 1 |
ID:
088279
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| Publication |
2009.
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| Summary/Abstract |
The US transportation sector is a major contributor to global greenhouse gas (GHG) emissions. As such, policymakers and stakeholder groups have proposed a number of policy instruments aimed at reducing these emissions. In order to fully evaluate the effectiveness of these policies, policymakers must consider both the direct responses associated with policy actions, and the indirect responses that occur through complex relationships within socioeconomic systems. In cases where multiple policy instruments are employed, these indirect effects create policy interactions that are either complementary or competing; policymakers need to understand these interactions in order to leverage policy synergies and manage policy conflicts. Analysis of these indirect effects is particularly difficult in the transportation sector, where system boundaries are uncertain and feedback among systems components can be complicated. This paper begins to address this problem by applying systems dynamics tools (in particular causal loop diagrams) to help identify and understand the role of feedback effects on transportation-related GHG reduction policies. Policymakers can use this framework to qualitatively explore the impacts of various policy instruments, as well as identify important relationships that can be later included in quantitative modeling approaches.
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| 2 |
ID:
109661
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| Publication |
2011.
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| Summary/Abstract |
With increased focus on renewable energy in our modern era, it is increasingly important to understand the impact of policies on the performance and reliability of regional energy systems. This research develops a model to understand how geographic dispersion of PV installations impacts the reliability of electricity generated from the total PV network, measured by the variance of the distribution of generated electricity. Using NREL data, beta probability distributions of sunlight (kWh/m2/day) in various regions of Virginia are estimated using a fitting method that minimizes the Kolmogorov-Smirnov test statistic. A Monte Carlo simulation model is developed to measure PV electricity generation from multiple centralized and dispersed configurations over 100,000 days of probabilistic sunlight.
There is a calculable tradeoff between average generation and generation variability, and increased geographic dispersion of PV installations can decrease this variability. Controlling variable generation through policies that promote efficient PV siting can help provide reliable power, minimizing the need for load-balancing peaking power infrastructure and costly electricity purchases from the grid. Using a tradeoff framework of generation and costs, this paper shows that geographically dispersed generation can mitigate the risk of unreliable solar generation that can significantly impact the end-user costs and make PV infrastructure unattractive.
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