Cost-effectiveness of electricity energy efficiency programs

We analyze the cost-effectiveness of electric utility ratepayer-funded programs to promote demand-side management (DSM) and energy efficiency (EE) investments. We specify a model that relates electricity demand to previous EE DSM spending, energy prices, income, weather, and other demand factors. In contrast to previous studies, we allow EE DSM spending to have a potential longterm demand effect and explicitly address possible endogeneity in spending. We find that current period EE DSM expenditures reduce electricity demand and that this effect persists for a number of years. Our findings suggest that ratepayer funded DSM expenditures between 1992 and 2006 produced a central estimate of 0.9 percent savings in electricity consumption over that time period and a 1.8 percent savings over all years. These energy savings came at an expected average cost to utilities of roughly 5 cents per kWh saved when future savings are discounted at a 5 percent rate. Copyright © 2012 by the IAEE. All rights reserved.

A COMPARISON OF MARKET CLEARING TOOLS IN ELECTRICITY SYSTEMS

To maintain a strict balance between demand and supply in the US power systems, the Independent System Operators (ISOs) schedule power plants and determine electricity prices using a market clearing model. This model determines for each time period and power plant, the times of startup, shutdown, the amount of power production, and the provisioning of spinning and non-spinning power generation reserves, etc. Such a deterministic optimization model takes as input the characteristics of all the generating units such as their power generation installed capacity, ramp rates, minimum up and down time requirements, and marginal costs for production, as well as the forecast of intermittent energy such as wind and solar, along with the minimum reserve requirement of the whole system. This reserve requirement is determined based on the likelihood of outages on the supply side and on the levels of error forecasts in demand and intermittent generation. With increased installed capacity of intermittent renewable energy, determining the appropriate level of reserve requirements has become harder. Stochastic market clearing models have been proposed as an alternative to deterministic market clearing models. Rather than using a fixed reserve targets as an input, stochastic market clearing models take different scenarios of wind power into consideration and determine reserves schedule as output. Using a scaled version of the power generation system of PJM, a regional transmission organization (RTO) that coordinates the movement of wholesale electricity in all or parts of 13 states and the District of Columbia, and wind scenarios generated from BPA (Bonneville Power Administration) data, this paper explores a comparison of the performance between a stochastic and deterministic model in market clearing. The two models are compared in their ability to contribute to the affordability, reliability and sustainability of the electricity system, measured in terms of total operational costs, load shedding and air emissions. The process of building the models and running for tests indicate that a fair comparison is difficult to obtain due to the multi-dimensional performance metrics considered here, and the difficulty in setting up the parameters of the models in a way that does not advantage or disadvantage one modeling framework. Along these lines, this study explores the effect that model assumptions such as reserve requirements, value of lost load (VOLL) and wind spillage costs have on the comparison of the performance of stochastic vs deterministic market clearing models.

Cost of wind energy: comparing distant wind resources to local resources in the midwestern United States.

The best wind sites in the United States are often located far from electricity demand centers and lack transmission access. Local sites that have lower quality wind resources but do not require as much power transmission capacity are an alternative to distant wind resources. In this paper, we explore the trade-offs between developing new wind generation at local sites and installing wind farms at remote sites. We first examine the general relationship between the high capital costs required for local wind development and the relatively lower capital costs required to install a wind farm capable of generating the same electrical output at a remote site,with the results representing the maximum amount an investor should be willing to pay for transmission access. We suggest that this analysis can be used as a first step in comparing potential wind resources to meet a state renewable portfolio standard (RPS). To illustrate, we compare the cost of local wind (∼50 km from the load) to the cost of distant wind requiring new transmission (∼550-750 km from the load) to meet the Illinois RPS. We find that local, lower capacity factor wind sites are the lowest cost option for meeting the Illinois RPS if new long distance transmission is required to access distant, higher capacity factor wind resources. If higher capacity wind sites can be connected to the existing grid at minimal cost, in many cases they will have lower costs.

Leveraging nanoscale plasmonic modes to achieve reproducible enhancement of light.

The strongly enhanced and localized optical fields that occur within the gaps between metallic nanostructures can be leveraged for a wide range of functionality in nanophotonic and optical metamaterial applications. Here, we introduce a means of precise control over these nanoscale gaps through the application of a molecular spacer layer that is self-assembled onto a gold film, upon which gold nanoparticles (NPs) are deposited electrostatically. Simulations using a three-dimensional finite element model and measurements from single NPs confirm that the gaps formed by this process, between the NP and the gold film, are highly reproducible transducers of surface-enhanced resonant Raman scattering. With a spacer layer of roughly 1.6 nm, all NPs exhibit a strong Raman signal that decays rapidly as the spacer layer is increased.

Membrane binding of plasmid DNA and endocytic pathways are involved in electrotransfection of mammalian cells.

Electric field mediated gene delivery or electrotransfection is a widely used method in various studies ranging from basic cell biology research to clinical gene therapy. Yet, mechanisms of electrotransfection are still controversial. To this end, we investigated the dependence of electrotransfection efficiency (eTE) on binding of plasmid DNA (pDNA) to plasma membrane and how treatment of cells with three endocytic inhibitors (chlorpromazine, genistein, dynasore) or silencing of dynamin expression with specific, small interfering RNA (siRNA) would affect the eTE. Our data demonstrated that the presence of divalent cations (Ca(2+) and Mg(2+)) in electrotransfection buffer enhanced pDNA adsorption to cell membrane and consequently, this enhanced adsorption led to an increase in eTE, up to a certain threshold concentration for each cation. Trypsin treatment of cells at 10 min post electrotransfection stripped off membrane-bound pDNA and resulted in a significant reduction in eTE, indicating that the time period for complete cellular uptake of pDNA (between 10 and 40 min) far exceeded the lifetime of electric field-induced transient pores (∼10 msec) in the cell membrane. Furthermore, treatment of cells with the siRNA and all three pharmacological inhibitors yielded substantial and statistically significant reductions in the eTE. These findings suggest that electrotransfection depends on two mechanisms: (i) binding of pDNA to cell membrane and (ii) endocytosis of membrane-bound pDNA.

Environmental and technology policies for climate mitigation

We assess different policies for reducing carbon dioxide emissions and promoting innovation and diffusion of renewable energy. We evaluate the relative performance of policies according to incentives provided for emissions reduction, efficiency, and other outcomes. We also assess how the nature of technological progress through learning and research and development (R&D), and the degree of knowledge spillovers, affects the desirability of different policies. Due to knowledge spillovers, optimal policy involves a portfolio of different instruments targeted at emissions, learning, and R&D. Although the relative cost of individual policies in achieving reductions depends on parameter values and the emissions target, in a numerical application to the U.S. electricity sector, the ranking is roughly as follows: (1) emissions price, (2) emissions performance standard, (3) fossil power tax, (4) renewables share requirement, (5) renewables subsidy, and (6) R&D subsidy. Nonetheless, an optimal portfolio of policies achieves emissions reductions at a significantly lower cost than any single policy. © 2007 Elsevier Inc. All rights reserved.

Implications of shale gas development for climate change.

Advances in technologies for extracting oil and gas from shale formations have dramatically increased U.S. production of natural gas. As production expands domestically and abroad, natural gas prices will be lower than without shale gas. Lower prices have two main effects: increasing overall energy consumption, and encouraging substitution away from sources such as coal, nuclear, renewables, and electricity. We examine the evidence and analyze modeling projections to understand how these two dynamics affect greenhouse gas emissions. Most evidence indicates that natural gas as a substitute for coal in electricity production, gasoline in transport, and electricity in buildings decreases greenhouse gases, although as an electricity substitute this depends on the electricity mix displaced. Modeling suggests that absent substantial policy changes, increased natural gas production slightly increases overall energy use, more substantially encourages fuel-switching, and that the combined effect slightly alters economy wide GHG emissions; whether the net effect is a slight decrease or increase depends on modeling assumptions including upstream methane emissions. Our main conclusions are that natural gas can help reduce GHG emissions, but in the absence of targeted climate policy measures, it will not substantially change the course of global GHG concentrations. Abundant natural gas can, however, help reduce the costs of achieving GHG reduction goals.

Modeling economy-wide vs sectoral climate policies using combined aggregate-sectoral models

Economic analyses of climate change policies frequently focus on reductions of energy-related carbon dioxide emissions via market-based, economy-wide policies. The current course of environment and energy policy debate in the United States, however, suggests an alternative outcome: sector-based and/or inefficiently designed policies. This paper uses a collection of specialized, sector-based models in conjunction with a computable general equilibrium model of the economy to examine and compare these policies at an aggregate level. We examine the relative cost of different policies designed to achieve the same quantity of emission reductions. We find that excluding a limited number of sectors from an economy-wide policy does not significantly raise costs. Focusing policy solely on the electricity and transportation sectors doubles costs, however, and using non-market policies can raise cost by a factor of ten. These results are driven in part by, and are sensitive to, our modeling of pre-existing tax distortions. Copyright © 2006 by the IAEE. All rights reserved.

Electrostatic Energetics of Bacillus subtilis Ribonuclease P Protein Determined by Nuclear Magnetic Resonance-Based Histidine pKa Measurements.

The pKa values of ionizable groups in proteins report the free energy of site-specific proton binding and provide a direct means of studying pH-dependent stability. We measured histidine pKa values (H3, H22, and H105) in the unfolded (U), intermediate (I), and sulfate-bound folded (F) states of RNase P protein, using an efficient and accurate nuclear magnetic resonance-monitored titration approach that utilizes internal reference compounds and a parametric fitting method. The three histidines in the sulfate-bound folded protein have pKa values depressed by 0.21 ± 0.01, 0.49 ± 0.01, and 1.00 ± 0.01 units, respectively, relative to that of the model compound N-acetyl-l-histidine methylamide. In the unliganded and unfolded protein, the pKa values are depressed relative to that of the model compound by 0.73 ± 0.02, 0.45 ± 0.02, and 0.68 ± 0.02 units, respectively. Above pH 5.5, H22 displays a separate resonance, which we have assigned to I, whose apparent pKa value is depressed by 1.03 ± 0.25 units, which is ∼0.5 units more than in either U or F. The depressed pKa values we observe are consistent with repulsive interactions between protonated histidine side chains and the net positive charge of the protein. However, the pKa differences between F and U are small for all three histidines, and they have little ionic strength dependence in F. Taken together, these observations suggest that unfavorable electrostatics alone do not account for the fact that RNase P protein is intrinsically unfolded in the absence of ligand. Multiple factors encoded in the P protein sequence account for its IUP property, which may play an important role in its function.