Optimization of Residential Battery Energy Storage System Scheduling for Cost and Emissions Reductions

The introduction of dynamic electricity pricing structures such as Time of Use (TOU) rates and Day Ahead Pricing (DAP) in residential markets has created the possibility for customers to reduce their electric bills by using energy storage systems for load shifting and/or peak load shaving. While there are numerous system designs and model formulations for minimizing electric bills under these rate structures the use of these systems has the potential to cause an increase in emissions from the electricity system. The Increase in emissions is linked to the difference in fuel mix of marginal generators throughout the day as well as inefficiencies associated with energy storage systems. In this work a multi-objective optimization model is designed to optimize reduction in cost of electricity as well as reduction in carbon dioxide (CO2) emissions from the electricity used by residential customers operating a battery energy storage system under dynamic pricing structures. A total of 22 different regions in the US are analyzed. Excluding emissions from the model resulted in an annual increase of CO2 emissions in all but one region ranging from 60-2000kg per household. The multi-objective model could be used to economically reduce these additional emissions in most regions by anywhere from 5 – 1300kg of CO2 per year depending on the region. When using the multi-objective model several regions had a net decrease in CO2 emissions compared to not using a battery system but most had a net increase