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WIREs Energy Environ.
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Multivariate analysis of solar city economics: impact of energy prices, policy, finance, and cost on urban photovoltaic power plant implementation

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Previous research suggests that the potential for city‐scale photovoltaic (PV) applications is substantial across the globe. Successful implementation of ‘solar city’ options will depend on the strategic application of finance mechanisms, solar energy soft cost policies, and other policy tools, as well as the grid price of electricity. Capital markets recently have embraced the roll‐out of new financial instruments, including ‘green bonds,’ which could be incorporated into solar city project design to attract large investments at a low cost. A multivariate analysis method is employed to consider solar city possibilities for six cities: Amsterdam, London, Munich, New York, Seoul, and Tokyo. A Monte Carlo simulation is conducted to capture the probabilistic nature of uncertainties in the parameters and their relative importance to the financial viability of a solar city project. The analysis finds that solar city implementation strategies can be practical under a broad range of circumstances. WIREs Energy Environ 2017, 6:e241. doi: 10.1002/wene.241 This article is categorized under: Photovoltaics > Systems and Infrastructure Solar Heating and Cooling > Economics and Policy Energy Policy and Planning > Economics and Policy
Monte Carlo assessment of project finance. *NREL's System Advisor Model (SAM) is used to calculate the benefit–cost ratios (https://sam.nrel.gov/).
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Histogram overview of model residuals for all cities combined for a 10‐year maturity. Black line is a normal distribution overlay.
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Multivariate regression results for each city for the seven variables. Notes: For each city, the ranking of the coefficients shows the influence of that variable on the benefit–cost ratio. Generation potential (kWh/m2) and the electricity retail rate are the key drivers of benefit–cost ratio outcomes in most locations. A notable exception is the City of Seoul where the electricity retail rate is relatively less important due to its low starting point.
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Results of the Monte Carlo analysis using a 90% interval around the mean for each city for a combined variability of the input data. Notes: Combined variability is achieved by varying input data by ±5% for 10,000 simulations per maturity year, ±10% for 10,000 simulations per maturity year, and ±15% variability for another 10,000 simulations per maturity year. A total of 30,000 simulations, therefore, are assessed to determine the benefit–cost ratios. The mean and 90% range correspond with the left y‐axis, and the columns correspond with the right y‐axis, depicting the percentage of simulations that are defined as viable projects (i.e., positive cumulative benefit cost ratios in all years of the project, using Eq. ). The distinctive ‘bend’ in the results is a direct effect of the assumed 10‐year lifespan of the policy benefits in each location: as soon as the policy benefits expire, the benefit‐to‐cost ratio relies solely on the retail electricity rate and electricity growth rate to determine the benefits component of the analysis. A gradual phase‐out of the policy benefits or other mitigating strategies could shorten the required financing timeframe.
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Energy Policy and Planning > Economics and Policy
Solar Heating and Cooling > Economics and Policy
Photovoltaics > Systems and Infrastructure

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