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201469 https://hdl.handle.net/2286/R.2.N.201469 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2025 273 pages Doctoral Dissertation Academic theses en Elio, Joseph Michael-James Milcarek, Ryan Chan, Candace Kwon, Beomjin Phelan, Patrick Wang, Robert Arizona State University Partial requirement for: Ph.D., Arizona State University, 2025 Field of study: Mechanical Engineering Behind-the-meter battery energy storage systems (BESS) offer commercial and industrial facilities opportunities for cost savings and indirect CO2 emissions reduction. However, the extent to which feasibility varies across sectors has not been well established in existing literature, nor have the specific demand profile features driving economic and environmental benefits been fully explored. This dissertation addresses these gaps by analyzing real-world electricity demand data from a diverse set of commercial and industrial facilities to determine which sectors benefit most from BESS installations and which features of the facility demand profiles drive economic and environmental (CO2 savings) benefits. A multi-objective optimization model is used to determine optimal battery dispatch strategies that maximize electricity cost savings and CO2 emissions reductions. Results show that sectors such as manufacturing, and mining, quarrying, oil, and gas, consistently achieve the highest cost savings and CO2 reductions, while others, such as agricultural related and waste related, see little financial benefit under any optimization scenario. Key demand-related predictors/features of BESS feasibility include the facilities average peak demand during demand response (DR) events, energy consumption during DR events, and July energy consumption. Under multi-objective cost- and CO2-optimization, facilities with higher DR peak demands and electricity consumption during DR events, as well as in July, tend to achieve faster discounted payback periods (DPPs) but less CO2 emissions reductions. Time-of-day electricity usage patterns also play a significant role, particularly the percentage of electricity consumed between 20:00–24:00, 8:00-12:00, and the amount of time spent at 90–100% of the monthly peak demand. Facilities with lower electricity consumption in the evening/night (20:00 – 24:00) are more likely to have greater reductions in DPPs and CO2 emissions. Conversely, facilities with greater electricity consumption in the morning (8:00 – 12:00) tend to achieve higher CO2 reductions. Event-based DR programs are found to substantially reduce the DPP, making BESS investments viable for more facilities. Additionally, the use of time-dependent marginal emissions factors (MEFs) highlights the impact of grid mix variations on BESS-driven indirect CO2 emissions reductions. Engineering Energy Battery Energy Storage Commercial and Industrial Facilities Demand Response Multi-Objective Modeling Optimization A Multi-Objective Optimization of Li-ion Battery Energy Storage Dispatch in Commercial and Industrial Facilities