US 12,141,820 B2
Greenhouse gas emissions control based on marginal emission factor data and energy storage systems
Imran Noorani, Toronto (CA); Nathan Ota, Berkeley, CA (US); Meysam Sahafzadeh, Toronto (CA); and Mehrdad Shirinbakhsh Masouleh, Mississauga (CA)
Assigned to PEAK POWER, INC., Toronto (CA)
Filed by PEAK POWER, INC., Toronto (CA)
Filed on Feb. 9, 2023, as Appl. No. 18/166,836.
Prior Publication US 2024/0273544 A1, Aug. 15, 2024
Int. Cl. G06Q 50/06 (2024.01); G06Q 10/0637 (2023.01); G06Q 30/018 (2023.01)
CPC G06Q 30/018 (2013.01) [G06Q 10/06375 (2013.01); G06Q 50/06 (2013.01)] 20 Claims
OG exemplary drawing
 
10. A system to determine an amount of greenhouse gas associated with an asset, the system comprising:
one or more processors coupled with memory to:
identify a first data set comprising a plurality of values corresponding to marginal emission factor (MEF) of electricity provided by a provider of electricity over a time period, the plurality of values including a first MEF value associated with a first timestamp and corresponding to electricity provided to the provider from a first electricity source and a second MEF value associated with a second timestamp and corresponding to electricity provided to the provider from a second electricity source;
identify a second data set comprising a plurality of measurements obtained by one or more sensors and corresponding to power of a battery of an asset coupled with the provider of electricity over the time period, the second data set including a first measurement associated with a third timestamp corresponding to a first portion of the time period in which the battery is charged and a second measurement associated with a fourth timestamp corresponding to a second portion of the time period in which the battery is discharged;
determine a plurality of MEF values corresponding to use of the battery by the asset over the time period based on the first MEF value, the second MEF value, the first measurement and the second measurement;
generate, via a neural network trained with historical MEF data, a value corresponding to an amount of carbon associated with the asset using the battery over the time period and based on the plurality of MEF values;
generate, based on the value generated via the neural network, a setting to start charging the battery over a first portion of a subsequent time period during which MEF values are decreased below a threshold and start discharging the battery over a second portion of the subsequent time period during which MEF values are increased above the threshold; and
apply the setting to the battery to reduce the amount of carbon associated with the asset over the subsequent time period relative to the time period.