Maryam Arbabzadeh
Center for Sustainable Systems, University of Michigan
Maryam Arbabzadeh is a Doctoral Student at the School of Natural Resources & Environment at University of Michigan. Her research informs the development of rigorous sustainability assessment approaches for energy storage systems in grid applications to inform technology design, sustainable policy implications while considering environmental and economic impacts.
Grid-scale energy storage may serve as a solution to the integration challenges of high penetrations of renewable energy, reduce air pollution from the grid, and therefore yield better environmental outcomes. However, understanding the total impact of using energy storage for grid applications are challenging because each application yields to different environmental responses to the complex grid system. Therefore, comprehensive sustainability assessments are necessary to yield the best environmental outcomes for grid-scale energy storage systems. To achieve this, first we have developed fundamental principles for green energy storage [1], guiding the design and deployment of these technologies to enhance sustainability performance of the power grid. These principles address key issues such as material sustainability, round-trip efficiency, service life, and sustainability performance of grid generations assets. Next, we coupled the principles with tools that we have developed for environmental assessment of energy storage systems. These tools are model equations and sustainability assessment algorithm that address environmental and economic impacts of utilizing energy storage for a specific grid application, and inform decision-making, as well as technology selection.
In this algorithm, the potential energy storage systems are screened to satisfy performance requirements and sustainability criteria in meeting a specific application. This process takes into account the service that the energy storage would provide (e.g., capacity for reserves, bulk energy time-shifting), and the energy storage parameters that influence outcomes, which include size, round-trip efficiency, degradation, service life, and production burden of energy storage technology. An energy system analysis is run for each alternative scenario, providing the necessary data for life cycle assessment and cost analysis to evaluate environmental and economic sustainability performance. In the following step, optimization modeling for decision-making is operated to find the cost-effective technology to satisfy an emission target. This framework systematically assesses the key tradeoffs that emerge when considering energy storage options, guiding the design of new technologies, and the modification and improvement of existing ones. This algorithm can be applied to different grid applications to provide recommendation for technology selection for the specific application.
As a case study, California ISO (CAISO) generation mix is implemented in this algorithm to analyze the role of cost-effective energy storage in time-shifting the peak load of CAISO with the goal of avoiding the emissions and capital investments associated with the peak generations, as well as increasing the alignment of renewable energy resources (specifically solar energy) with the load shape.
References
[1] M. Arbabzadeh, J. X. Johnson, G. A. Keoleian, P. G. Rasmussen, and L. T. Thompson, “Twelve principles for green energy storage in grid applications,” Environmental Science & Technology, vol. 50, pp. 1046-1055, DOI 10.1021/acs.est.5b03867, 2015.
• Life cycle sustainability assessment , • Sustainable energy systems
