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Background
Scope 3 emissions in the context of LDES encompass the indirect greenhouse gas emissions generated throughout the entire lifecycle of the storage system, extending beyond the boundaries of its production and operation. These emissions arise from various sources such as raw material extraction, manufacturing of components, transportation, and disposal. Battery energy storage systems may involve the production of minerals and materials associated with energy-intensive mining processes.
Additionally, emissions can arise from the manufacturing and transportation of the components that make up the battery system. While battery energy storage offers substantial environmental benefits by enabling renewable energy integration and grid stability, understanding and mitigating its Scope 3 emissions is crucial for a comprehensive assessment of its overall environmental impact. Strategies to address these emissions might involve sourcing materials responsibly, optimizing manufacturing processes, and promoting recycling and circular economy practices.
Challenge
Accurately computing and benchmarking lifecycle analysis [LCA] for LDES technologies presents several challenges due to the complex and interconnected nature of these systems. Some of the key challenges include:
Data Availability and Quality: Gathering accurate data throughout the entire lifecycle of an energy storage system is challenging. Data on material extraction, manufacturing processes, energy inputs, transportation, and end-of-life disposal can be incomplete, inconsistent, or not readily available. This lack of data quality can lead to uncertainties in the analysis.
Technology Diversity: LDES technologies encompass a wide range of technologies, each with unique characteristics and lifecycles. Comparing technologies like across; chemical, mechanical, electro-chemical and thermal, requires accounting for their varying energy and environmental impacts, making standardized analysis difficult.
Rapid Technological Changes: The LDES sector is evolving rapidly, with new technologies and improvements continuously emerging. Traditional lifecycle analysis may struggle to keep up with the pace of innovation, and projections might become inaccurate as technologies change.
Geographical Variation: The environmental impacts of LDES can vary significantly based on the location of manufacturing, operation, and end-of-life processes. Different regions have varying energy sources, regulations, and waste management practices, leading to varying lifecycle outcomes.
Boundary Definitions: Defining the boundaries of a lifecycle analysis is challenging. Should it include only the LDES system, or also the associated grid infrastructure, power sources, and grid integration impacts? Deciding on these boundaries can greatly influence the results.
Indirect Effects: Lifecycle analyses often overlook indirect effects, such as the influence of LDES deployment on the energy mix and grid operations. These effects can be substantial and are vital for understanding the broader environmental impact of LDES.
Uncertainty and Assumptions: Many lifecycle analyses require making assumptions and estimations due to the lack of complete data or uncertainties about future developments. These assumptions can introduce biases and affect the accuracy of the analysis.
Time Horizons: The impacts of LDES technologies might vary over time due to factors like degradation, changes in energy demand, and improvements in technology. Choosing an appropriate time horizon for the analysis can be challenging.
Interactions with Other Sectors: Energy storage systems interact with other sectors, such as transportation and industry. These interactions can lead to cascading effects and can be complex to model accurately.
Policy and Regulation: Government policies, regulations, and incentives can influence the lifecycle impacts of energy storage technologies. These factors are subject to change and can have significant effects on the results of lifecycle analyses.
To address these challenges, researchers and practitioners need to work collaboratively, improve data collection methods, develop standardized methodologies, and consider broader systems approach those accounts for interdependencies between different technologies and sectors.
Potential Pathways
The primary objective of our model ReSynergyâ„¢ is to leverage the capabilities of high-performance computing and natural language processing to unlock invaluable insights pertaining to battery supply chains and recycling decisions, thereby advancing the development of a more sustainable energy storage supply chain. Specifically, the model empowers users to:
Benchmark production using recycled materials against production employing virgin materials, providing a comprehensive assessment of the benefits and trade-offs associated with battery recycling.
Estimate the cost and environmental impacts of prevailing industrial practices across the battery supply chain, identifying critical cost and environmental focal points, and evaluating the potential implications of business decisions and market dynamics; and
Benchmark novel technologies/processes against existing practices within the battery industry, examining the potential changes in cost and environmental impacts as the new technology/process scales up.
Benchmarking LCA of LDES technologies against the incumbent technologies.
Learn more about our SCM optimisation engine here