Philip Dickemann

2026

With the rapid deployment of energy storage in corporate grids, accurate tracking of emissions and costs becomes crucial. This thesis extends the ECCA (Energy Cost and Carbon Accounting) model by incorporating real-world, technical constraints of electrical energy storage systems (EES), such as capacity limits, power thresholds, and efficiency losses. Simulations across various countries demonstrate that a cost-optimized operational strategy enables significant financial savings, exceeding 40\% in volatile markets like New South Wales, while simultaneously achieving moderate, synergistic Scope 2 emission reductions. Under a purely carbon-optimized operation, CO$_2$ savings scale up to 9.59\%, while still delivering economic co-benefits.
Regarding carbon and cost accounting, the integration of EES severely exacerbates inaccuracies in temporally aggregated data models. Dynamic charging cycles are completely obscured at coarser time steps (daily to annual) which leads to systematic overestimations of costs and emissions. In extreme cases the overestimation exceeds 50\%. This makes hourly resolved data indispensable for valid management.