This paper highlights the importance of precise assessments of greenhouse gas (GHG) emissions associated with power generation for effective policy making in environmental sustainability. The current assessment approaches based on historical data or estimated generation using energy models may not accurately reflect the reality of future power systems due to the impact of spatial-temporal and techno-economic characteristics of generation mix and load demands. To address this, the paper presents a comprehensive methodology for accurately quantifying the geographical and temporal variations in GHG emissions associated with generating units’ operation, startup, and shutdown at an hourly resolution. The methodology is based on a detailed electricity model that considers various sources of generation, techno-economic, and spatial-temporal characteristics of system components. The study demonstrates the effectiveness of the methodology in quantifying GHG emissions in the IEEE RTS-GLMC system, with a focus on CO2, N2O, and CH4. The analysis reveals significant variations in GHG emissions among different generation buses and hours of the year, attributed to the high proportion of renewable energy in the generation mix. The paper emphasizes the inadequacy of examining marginal environmental impacts based on GHG emission intensity alone and suggests a more thorough analysis based on total GHG emissions generation. Finally, the paper emphasizes the crucial role of time-varying and marginal assessment techniques in identifying effective strategies for reducing GHG emissions in the electricity sector, including optimizing the operation and capacity of generation units, energy storage systems, and electric vehicles, including their locations.

The design and operation of cost-effective power systems and high penetration of renewable energy and energy storage require a modeling approach that simultaneously reflects the system's reality. Although power systems with high penetration of renewable energy and energy storage have been analyzed in the past, more systematic analysis are in need to determine the impact of generation units' techno-economic parameters on the model's accuracy. This study investigated the effects of the techno-economic and spatial-temporal parameters, which will be referred to as combined techno-economic parameters, via seven scenarios. The combined techno-economic effects were demonstrated on optimal generation mix, renewable curtailments, variability status of storage units, and energy cost. The omission of combined techno-economic parameters results in the renewable generation to be more visible and with less curtailment while significantly decreasing the share of energy storage and decreasing the energy cost by 1.53 USD/MWh. As a result, if power systems assessment dismissed the evolving techno-economic and spatial–temporal parameters, the precision of cost–benefit analysis can be compromised, thus affecting the decision-making in terms of directing projects, regulations, and policies within the power industry.