A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Optimization of residential battery energy storage system scheduling for cost and emissions reductions
Highlights Battery energy storage systems used to minimize energy costs increase emissions. Multi-objective models considering both emissions and cost reduce BESS impacts. Regions in the Central USA see the most benefits from multi-objective models.
Abstract The introduction of dynamic electricity pricing in residential markets has created the possibility for residential electricity consumers to reduce their electric bills using battery energy storage systems (BESSs) for load shifting and/or peak load reduction. While there are numerous system designs and model formulations for minimizing electric bills under dynamic prices the use of these systems has the potential to cause an increase in emissions from electricity generation. 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. In this work a multi-objective optimization model is designed to balance the competing goals of minimizing electricity costs for the home owner as well as minimizing carbon dioxide (CO2) emissions from the operation of a BESS under dynamic prices. A total of 22 different regions in the US are analyzed. Optimizing only for energy cost resulted in an annual increase of CO2 emissions in all but two regions ranging from 70 to 2200 kg per household. The multi-objective model when using a social cost of carbon of 42 $/ton can be used to economically reduce these additional emissions in most regions by anywhere from 49 to 1450 kg of CO2 per year.
Optimization of residential battery energy storage system scheduling for cost and emissions reductions
Highlights Battery energy storage systems used to minimize energy costs increase emissions. Multi-objective models considering both emissions and cost reduce BESS impacts. Regions in the Central USA see the most benefits from multi-objective models.
Abstract The introduction of dynamic electricity pricing in residential markets has created the possibility for residential electricity consumers to reduce their electric bills using battery energy storage systems (BESSs) for load shifting and/or peak load reduction. While there are numerous system designs and model formulations for minimizing electric bills under dynamic prices the use of these systems has the potential to cause an increase in emissions from electricity generation. 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. In this work a multi-objective optimization model is designed to balance the competing goals of minimizing electricity costs for the home owner as well as minimizing carbon dioxide (CO2) emissions from the operation of a BESS under dynamic prices. A total of 22 different regions in the US are analyzed. Optimizing only for energy cost resulted in an annual increase of CO2 emissions in all but two regions ranging from 70 to 2200 kg per household. The multi-objective model when using a social cost of carbon of 42 $/ton can be used to economically reduce these additional emissions in most regions by anywhere from 49 to 1450 kg of CO2 per year.
Optimization of residential battery energy storage system scheduling for cost and emissions reductions
Olivieri, Zachary T. (author) / McConky, Katie (author)
Energy and Buildings ; 210
2020-01-14
Article (Journal)
Electronic Resource
English
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