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Sigrin, Benjamin O

Publications and source records attributed to Sigrin, Benjamin O.

Quantifying the Relationship Between Higher Photovoltaic Module Efficiency and the Adoption of Distributed Solar

The purpose of this study is understanding the relationship between module efficiency, system costs, and the economic and adoption potential of distributed rooftop solar. We use the dGen model to project the economic potential and adoption of high-efficiency photovoltaic (PV) modules across a range of efficiencies and cost scenarios in the continental United States. Our results reveal that high-efficiency modules can modestly increase the projected adoption of distributed solar, but system capital costs may play a larger role in changing the amount of projected adoption.

14 SOLAR ENERGY↗

Analysis of the Value Proposition of High-Efficiency, Multijunction Solar Modules for Residential Rooftop Installations

Utilizing detailed bottom-up models, we analyze the potential markets for high-efficiency photovoltaic modules in the United States. We first estimate the fraction of area-constrained residential rooftops that could benefit from increasing the availability of high-efficiency systems. We then model the impact of efficiency on installed system costs in these area-constrained scenarios. Finally, we explore the value proposition of high-efficiency, fixed-tilt micro-concentrator photovoltaic modules in a series of case studies of residential customers, modeling the levelized cost of energy, payback period, and annual bill savings. We find that efficiency can provide significant savings in installed system cost and improve value to the solar system owner. The improvement in value depends on location and the size of the rooftop, as well as the ability of a given technology to collect diffuse light and achieve low module costs.

14 SOLAR ENERGY↗

Quantifying the Relationship Between Higher Photovoltaic Module Efficiency and the Adoption of Distributed Solar: Preprint

The purpose of this study is understanding the relationship between module efficiency, system costs, and the economic and adoption potential of distributed rooftop solar. We use the dGen model to project the economic potential and adoption of high-efficiency photovoltaic (PV) modules across a range of efficiencies and cost scenarios in the continental United States. Our results reveal that high-efficiency modules can modestly increase the projected adoption of distributed solar, but system capital costs may play a larger role in changing the amount of projected adoption.

14 SOLAR ENERGY↗

California Time-of-Use (TOU) Transition: Effects on Distributed Wind and Solar Economic Potential

Time-of-use (TOU) retail energy rates price electricity differently by the time of day, thereby communicating to consumers the costs of supplying electricity throughout the day. This study evaluates the impact of Decision D.15.07-001, which mandates a transition to default TOU rates in California, on the economic viability of behind-the-meter distributed wind and solar systems. We assess viability through 'economic potential,' a metric we define as the amount of generation capacity that exceeds a specified rate of return (5.4%) and therefore would be economic to construct; we do not attempt to project deployment or adoption. In addition, we identify cost improvements needed for a robust distributed wind market and the specific counties and sectors with substantial economic potential. Over the scenarios considered, model results indicate realistic pathways to reaching a robust, 1-GW distributed wind market in California though solar economic potential was found to be substantially larger than that of wind. Distributed wind's capacity factor does not appear to be strongly correlated to the periods of peak electricity prices as set by utility's TOU periods. Because of this disassociation and nuances in the billing of tiered electricity, the transition to TOU is projected to moderately decrease distributed wind economic potential and increase that of distributed solar. However, a limitation of this analysis is that it does not factor in the change in rate values as California works toward its goal of 100% renewables. As variable renewable energy penetration increases to meet this goal, our results suggest that wind generation will become increasingly correlated to periods of peak electricity prices. Increased variable renewable penetration, especially of solar, is likely to shift periods of higher electricity demand to later in the evening, and any periodic adjustments in TOU periods to reflect these new periods of demand will positively affect the value of wind generation more than the value of solar generation. High distributed wind capital costs are a significant barrier to large scale adoption; we explore the sensitivity of capital cost reductions on distributed wind economic potential. We find that distributed wind economic potential increases substantially in 2020 for capital costs below 3 dollars/W - particularly for residential- and commercial-scale turbines; capital costs must fall below 2 dollars/W in 2030 to support a significant increase in economic potential, in large part due to the expected continued cost reductions in solar. We identified an upper limit to the state's behind-the-meter distributed wind economic potential at approximately 5 GW. Overall, California exhibits over 1 GW of economic potential for distributed wind by 2030, even with the moderate decrease in potential under TOU. This potential is represented mostly by Southern California, which sees the most notable increase in economic viability as capital expenditure prices decline.

17 WIND ENERGY↗

An Overview of Distributed Energy Resource (DER) Interconnection: Current Practices and Emerging Solutions

This guidebook provides an overview of key issues for utilities and utility regulators to consider as deployments of distributed energy resources (DER) increase, including what has been tried in the field and emerging good practices. Topics covered include: Interconnection Applications and Application Processing; Interconnection Standards; Advanced Inverters; Impact Screening and Evaluation; Estimating Future DER deployments; Distribution System Upgrades and Planning in a DER context; Cybersecurity; Storage and Solar plus Storage Interconnection; and Cost Allocation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Quantifying Resolution Implications for Agent-based Distributed Energy Resource Customer Adoption Models

In this report we explore tradeoffs of top-down and bottom-up methods in their precision and computational burden using NREL's dGen model, an agent-based model of residential and non-residential distributed PV adoption. In particular, we assess the role of agent resolution in instantiating statistically-representative populations in the model--and the resulting variance of model projections at the state, sector and county levels. Running the model for a single state with fixed macroeconomic assumptions, we vary both the number of agents instantiated and the number of unique simulations. Notably, in each run we introduce sources of uncertainty in cumulative DPV adoption results during agent instantiation as we stochastically sample agent attributes from localized building characteristic and solar resource distributions. We find three key trends which guide what can be expected of results. First, precision of the model projections, as measured by the variance of the unique simulations increased as a function of the number of agents sampled, though with diminishing returns to scale. The result is expected given the Law of Large Numbers. Second, the mean or expected value of projections decreased as the number of agents instantiated increased. Empirically, aggregate DPV capacities projected by dGen are 7% lower on average at a 10-agent resolution compared to 1-agent resolutions, and up to 10% lower in the non-residential sector. We attribute this outcome to a system sizing scheme that biases lower cumulative installed capacity as agent resolution increases when roof size and load are not positively correlated. Finally, the variance of model projections differed by sector, where variance was substantially larger in the commercial sector, largely due to differences in parameterization by sector. We explain the larger variance in the non-residential sector as an outcome of the greater variance in building uses and load shapes as compared to the residential sector, and we find that a resolution on the order of 100-agents per county would be necessary to bring non-residential variance in line with residential variance at a 2-agent resolution. We propose that further research explore increasing precision and agent resolution in the non-residential sector. Moreover, given that days of intensive computing may be required to resolve a suitable estimate even at the extent of a single state within acceptable levels of precision, to promote computational efficiency we also suggest more clearly quantifying sources of variability at the county level such that resolution can by dynamically increased where county level results are least precise.

14 SOLAR ENERGY↗

Assessment of the Economic Potential of Distributed Wind in Colorado, Minnesota, and New York

Stakeholders in the small and distributed wind space require access to better tools and data for more informed decisions on high-impact topics, including project planning, policymaking, and funding allocation. A major challenge in obtaining improved information is in the identification of favorable sites - namely, the intersection of sufficient wind resource with economic parameters such as retail rates, incentives, and other policies. This presentation made at the AWEA WINDPOWER Conference and Exhibition in Chicago in 2018 explores the researchers' objective: To understand the spatial variance of key distributed wind parameters and identify where they intersect to form pockets of favorable areas in Colorado, Minnesota, and New York.

17 WIND ENERGY↗