Planning for a Distributed Disruption: Innovative Practices for Incorporating Distributed Solar into Utility Planning

Mill, Andrew; Barbose, Galen; Seel, Joachim; Dong, Changgui; Mai, Trieu; Sigrin, Ben; Zuboy, Jarrett

doi:10.2172/1332539

Cited by 14 publications

(24 citation statements)

References 27 publications

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“…Similar results have been found even when treated as spill-overs between regions, that is, when neighboring regions do influence each other throughout the adoption process [13,14,15]. As Mills et al [16] correctly pointed out, researchers and policymakers need to improve their understanding of non-monetary adoption factors in order to better incorporate solar systems in to utility planning, thus focusing on potential policy shortfalls in supporting the adoption of PV for late-comers.…”

Section: Introduction and Objectivessupporting

confidence: 56%

“…Finally, our methodologies provide a better understanding of profile adopters and adoption patterns within areas at the bottom of their adoption curve [16]. These results are important for making it easier for utilities and policymakers to address potential needs within the power grid system, especially as community solar is bound to expand further, and social interactions increase in importance [34] for successfully transitioning towards a low-carbon electricity generation.…”

Section: Conclusion and Future Research: Defining The Right Policiesmentioning

confidence: 95%

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Peer effects in the adoption of solar energy technologies in the United States: An urban case study

Graziano

Fiaschetti

Atkinson-Palombo

2019

Energy Research & Social Science

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Building upon recent literature, we combine a novel spatiotemporal variable with spatial methods to investigate and quantify the influence of the built environment and jurisdictional boundaries on spatial peer-effects (SPEs) in inner-city areas. We focus on the Hartford Capital region, using detailed data at block-group and PV system levels for the years 2005-2013. This region is part of a state, Connecticut, actively engaged in supporting PV system at residential level. Adoption of PV systems varies substantially, and state policies are mediated by town-level regulations. We initially employ typology analysis to investigate the heterogeneity of the block groups with higher adoption rates. We then use panel FE and spatial estimations to determine the existence of spill-overs of SPEs beyond town boundaries. Our estimations suggest that new PV systems have a more limited spatiotemporal influence in inner-cities. We identify spatial spill-overs from neighboring block groups even between towns, suggesting that SPEs transcend municipal barriers. We do not find significant results for built-environment, although we identify several data limitations. Our results suggest that centralized, non-voluntary support policies may have larger effects if implemented beyond town-level, and that SPEs change their determination power depending on the underlying built environment. Economic Analysis.

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Section: Introduction and Objectivessupporting

confidence: 56%

Section: Conclusion and Future Research: Defining The Right Policiesmentioning

confidence: 95%

Peer effects in the adoption of solar energy technologies in the United States: An urban case study

Graziano

Fiaschetti

Atkinson-Palombo

2019

Energy Research & Social Science

View full text Add to dashboard Cite

show abstract

“…The dGen model mitigates such variability in the sampling of weight, roof area suitable to DPV deployment, and annual load by an agent-mutation mechanism that scales these attributes in aggregate across all agents per county and sector to known totals. 4 Though the scaling ensures central tendencies are reflected, it removes heterogeneity in these attributes at the county level. Alternatively, at sufficiently high agent resolutions, the distribution in roof area suitable to DPV deployment and annual load across all agents in a county will more closely resemble the true population distribution of each sector and county.…”

Section: Figure 1 Instantiation Workflow For Key Stochastically Sampmentioning

confidence: 99%

“…These methods differ widely in the algorithms and input data used, though they can generally be classified as either top-down or bottom-up. Top-down models use central tendencies to project aggregate deployment, notable examples include time series models [4,5] and econometric models [6][7][8][9][10]. Bass Models are perhaps the most widely used adoption models [5 -11] .…”

Section: Introductionmentioning

confidence: 99%

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

Kwasnik

Sigrin

Bielen

2019

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Distributed photovoltaics (DPV) are a growing source of electricity generation in the United States, and with adoption driven by customer behavior and localized economics, projecting the deployment of this technology is a challenging analytical problem. Moreover, understanding the sources of uncertainty in customer adoption models and how they can be reduced is important to a range of stakeholders that use their outputs, including grid planners, regulators, and industry. Most prior studies have used top-down methods, such as the use of population central tendencies to project aggregate adoption. In contrast, a growing field of work seeks to use bottom-up methods (i.e., individual-level decision-making).We explore trade-offs of top-down and bottom-up methods in their precision and computational burden using the National Renewable Energy Laboratory's (NREL's) Distributed Generation Market Demand (dGen) model, an agent-based model of residential and nonresidential 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. At low sampling rates, the model resembles a top-down model, whereas as sampling rates increase dGen converges to a bottom-up structure by simulating more unique customer types. Though sampling-based models such as dGen can be operated with many agents to ensure accuracy, doing so greatly increases the computational burden of the simulation. This report lends insight into whether high-resolution results can be approximated sufficiently well using fewer computational resources.vii

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“…Various T&D planning studies are beginning to address this issue. Although they apply a range of approaches to perform such analyses, all come to the same conclusion; results are primarily driven by the detailed conditions of particular distribution feeders (Mills et al, 2016). Furthermore, it is unclear if the investment in EE by a customer on the same distribution feeder with a PV system or even by one without a PV system will have an exacerbating or mitigating effect on the need for additional T&D investment.…”

Section: Impacts On Utility Costsmentioning

confidence: 99%

Financial Impacts of a Combined Energy Efficiency and Net-Metered PV Portfolio on a Prototypical Northeast Utility

Satchwell

Cappers

Goldman

2017

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Planning for a Distributed Disruption: Innovative Practices for Incorporating Distributed Solar into Utility Planning

Cited by 14 publications

References 27 publications

Peer effects in the adoption of solar energy technologies in the United States: An urban case study

Peer effects in the adoption of solar energy technologies in the United States: An urban case study

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

Financial Impacts of a Combined Energy Efficiency and Net-Metered PV Portfolio on a Prototypical Northeast Utility

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