Financial Analysis of Grid-tied Rooftop Solar Photovoltaic System employing Net-Metering

Kalke, Durvesh; Kokkonda, Karun; Kulkarni, P. S.

doi:10.1109/icsedps.2018.8536037

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…Swift (12) [15]. Recent studies in India have found PV to be financially viable for residential systems [16] and rural areas [17] while a study in Spain found utilizing grid electricity and natural gas for heating to be more economical than PV coupled with solar thermal and a micro-CHP system [18]. A more recent study for the United States by Lee, Hong, Koo and Kim (19) found that, depending on state and incentive, the payback period for select states in the United States ranges from as high as 25 years (Nevada and Wisconsin) to as few as 5 years (Hawaii).…”

Section: Literature Reviewmentioning

confidence: 99%

Developing Cost Functions for Estimating Solar Photovoltaic System Installed and Life Cycle Costs Using Historical Quote Data

Webb¹,

Kneifel²,

O'Fallon³

2020

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Residential solar photovoltaic (PV) system installations have become more prevalent as the installed cost has decreased over the last 10 years while system performance has improved. As these installations have increased, so too has interest in determining their economic value to a homeowner. PV installation cost estimates have typically assumed the entire cost as marginal (average cost per watt) using reported data aggregated to a state or country. This study implements a cost function that includes a fixed cost and marginal cost element to account for differences in cost structures while controlling for panel quality and specific location. The analysis uses county level installed quote data applied to estimate cost functions and apply these functions to life cycle cost analyses of the Washington DC-Maryland-Virginia (DMV) metropolitan area while incorporating state and county level differences in pricing and incentives. The estimated cost function is found to provide an installed cost estimate that is statistically different than using the traditional total average cost per watt approach for both standard and premium systems up to 9 kW and 11 kWh, respectively. The analysis finds no statistical difference in the installed cost function across counties, but clear differences in the life-cycle cost-effectiveness to a homeowner due to state policies and retail electricity prices. Absent financing, only PV systems in DC are life-cycle cost effective compared to retail electricity due to DC's strong solar renewable energy credit (SREC) market. PV can be cost effective in Maryland if financing and incentives are applied while no combination of financing or incentives makes PV cost effective in Virginia due to its lower relative electricity prices. Sensitivity analysis finds that the homeowner's assumed discount rate and the upcoming phase out of the federal investment tax credit (ITC) has a significant impact on residential PV economics.

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Section: Literature Reviewmentioning

confidence: 99%

Developing Cost Functions for Estimating Solar Photovoltaic System Installed and Life Cycle Costs Using Historical Quote Data

Webb¹,

Kneifel²,

O'Fallon³

2020

View full text Add to dashboard Cite

show abstract

“…A recent Canadian study examining urban deployment of rooftop solar PV found 96 % of identified suitable rooftops would be profitable using NPV [14]. Recent studies in India have found solar PV to be financially viable for residential systems [15] and rural areas [16] while a study in Spain found utilizing grid electricity and natural gas for heating to be more economical than solar PV coupled with solar thermal and a micro-CHP system [17]. A more recent study for the United States by Lee, Hong, Koo and Kim (18) found that 18 states realized a payback period to at least break even while the other 32 states not being able to reach a breakeven point.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing Cost Functions for Estimating Solar Photovoltaic System Installed Using Historical Data and OLS Regression

Webb¹,

Kneifel²

2020

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Solar photovoltaics (PV) continues to increase in market share. Policy decisions and the nature of solar markets continue to shift; however, it is likely that the price of solar will continue to decrease in the near term. Given the increasing market and more competition in installations, it is beneficial to have a greater understanding in the driving factors in solar PV pricing, as well as models to help perspective buyers and sellers to obtain estimates for the cost of installations. Currently, most estimates rely on a marginal cost that is equivalent to the total cost divided by the system size. This study uses data from EnergySage and the National Renewable Energy Laboratory's Tracking the Sun data set for California, specifically Fresno, San Francisco, Los Angeles, San Diego, and San Jose, to accomplish three goals: to determine if there are significant predictors for solar PV pricing outside of the current method of relying on system size only, to determine what model would make sense for predictive purpose in preparation for the development of a tool to predict the total life cycle cost of solar PV, and to determine if smaller geographical resolutions are warranted when looking at price by location. This paper finds that there are several more significant predictors of Solar PV pricing by including more PV system specifications, such as panel efficiency, inverter type, and system quality. Results also indicate that the installer of the PV system may proxy for the specification variables when it is included in the model. While the installer-based models show significant difference from many of the other models, including the specification-based models, they fail to increase the predictive capability for the EnergySage data, however, show promise for better predictions using the Tracking the Sun data. This difference is driven by the EnergySage data being far more dependent on system size to the point that it can serve reliably as a quote predictor on its own. By breaking the data down to models by city and city-installer groups regional differences can be clearly seem, indicating a more refined geographic approach is necessary for PV price estimation.

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A Robust IMFOGI Control for Power Quality Enhancement of a Dual Stage SPV-BES-BDC-SyRG DG Set Based Standalone Microgrid

Chakraborty

Modi

Singh

2022

2022 IEEE Energy Conversion Congress and Exposition (ECCE)

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Financial Analysis of Grid-tied Rooftop Solar Photovoltaic System employing Net-Metering

Cited by 8 publications

References 12 publications

Developing Cost Functions for Estimating Solar Photovoltaic System Installed and Life Cycle Costs Using Historical Quote Data

Developing Cost Functions for Estimating Solar Photovoltaic System Installed and Life Cycle Costs Using Historical Quote Data

Developing Cost Functions for Estimating Solar Photovoltaic System Installed Using Historical Data and OLS Regression

A Robust IMFOGI Control for Power Quality Enhancement of a Dual Stage SPV-BES-BDC-SyRG DG Set Based Standalone Microgrid

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