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Lower electricity prices and greenhouse gas emissions due to rooftop solar: empirical results for Massachusetts

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  • Kaufmann, Robert K.
  • Vaid, Devina

Abstract

Monthly and hourly correlations among photovoltaic (PV) capacity utilization, electricity prices, electricity consumption, and the thermal efficiency of power plants in Massachusetts reduce electricity prices and carbon emissions beyond average calculations. PV utilization rates are highest when the thermal efficiencies of natural gas fired power plants are lowest, which reduces emissions of CO2 and CH4 by 0.3% relative to the annual average emission rate. There is a positive correlation between PV utilization rates and electricity prices, which raises the implied price of PV electricity by up to 10% relative to the annual average price, such that the average MWh reduces electricity prices by $0.26–$1.86 per MWh. These price reductions save Massachusetts rate-payers $184 million between 2010 and 2012. The current and net present values of these savings are greater than the cost of solar renewable energy credits which is the policy instrument that is used to accelerate the installation of PV capacity. Together, these results suggest that rooftop PV is an economically viable source of power in Massachusetts even though it has not reached socket parity.

Suggested Citation

  • Kaufmann, Robert K. & Vaid, Devina, 2016. "Lower electricity prices and greenhouse gas emissions due to rooftop solar: empirical results for Massachusetts," Energy Policy, Elsevier, vol. 93(C), pages 345-352.
  • Handle: RePEc:eee:enepol:v:93:y:2016:i:c:p:345-352
    DOI: 10.1016/j.enpol.2016.03.006
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    Citations

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    Cited by:

    1. Bell, William Paul & Wild, Phillip & Foster, John & Hewson, Michael, 2017. "Revitalising the wind power induced merit order effect to reduce wholesale and retail electricity prices in Australia," Energy Economics, Elsevier, vol. 67(C), pages 224-241.
    2. Chesser, Michael & Hanly, Jim & Cassells, Damien & Apergis, Nicholas, 2018. "The positive feedback cycle in the electricity market: Residential solar PV adoption, electricity demand and prices," Energy Policy, Elsevier, vol. 122(C), pages 36-44.
    3. Mosquera-López, Stephanía & Nursimulu, Anjali, 2019. "Drivers of electricity price dynamics: Comparative analysis of spot and futures markets," Energy Policy, Elsevier, vol. 126(C), pages 76-87.
    4. Mosquera-López, Stephanía & Uribe, Jorge M. & Manotas-Duque, Diego Fernando, 2017. "Nonlinear empirical pricing in electricity markets using fundamental weather factors," Energy, Elsevier, vol. 139(C), pages 594-605.
    5. Véliz, Karina D. & Kaufmann, Robert K. & Cleveland, Cutler J. & Stoner, Anne M.K., 2017. "The effect of climate change on electricity expenditures in Massachusetts," Energy Policy, Elsevier, vol. 106(C), pages 1-11.
    6. Ma, Jia-Jun & Du, Gang & Xie, Bai-Chen, 2019. "CO2 emission changes of China's power generation system: Input-output subsystem analysis," Energy Policy, Elsevier, vol. 124(C), pages 1-12.
    7. Yasir Alsaedi & Gurudeo Anand Tularam & Victor Wong, 2020. "Impact of Solar and Wind Prices on the Integrated Global Electricity Spot and Options Markets: A Time Series Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 337-353.
    8. Haitao Xiang & Ying Kong & Wai Kin Victor Chan & Sum Wai Chiang, 2019. "Impact of Price–Quantity Uncertainties and Risk Aversion on Energy Retailer’s Pricing and Hedging Behaviors," Energies, MDPI, Open Access Journal, vol. 12(17), pages 1-20, August.

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